Study Guide

Objective

After completing the course the student can:
Implement a desktop or a web application for an assigned task.
Program efficiently in a team using professional tools.
Reuse code, utilize libraries, and/or application platforms and application frameworks.
Write code from UML diagrams or implement user stories.
Understand some common design patterns.

Content

An assignment for a desktop or a web application.
Elaborating on programming language and features suitable for the application.
Learning and utilising one or more design patterns.
Using professional coding and version control tools.
Re-using code and utilising libraries, frameworks and/or platforms.
Developing a desktop or a web application as a team work.

Materials

External course video material is used in the course. This causes some costs for the student.

Other material on the Internet.

Teaching methods

- watching the course video and reading other material
- participating in the lectures
- programming together with instructor
- programming alone
- using emulator and other tools; configuring
- participating in the teamwork

Exam schedules

No exam.

Completion alternatives

The student can complete the course by demonstrating his knowledge and skills of the subjects of the course, for example with the work samples they have made. However, this must be agreed with the instructor during the first 4 weeks of the course.

The student can include a corresponding course taken elsewhere at some educational institution that is acceptable by our educational institution. This happens via AHOT process. Also this matter should be initiated immediately at the beginning of the course.

Student workload

40 h videos, reading
26 h contact lessons
4 h Presenting the teamwork and following the presentations of other groups onsite
39 h teamwork
26 h personal exercises

Content scheduling

Programming mobile Apps with React Native

The contents:

What is React Native?
The basics of React Native
Debugging
Styles and layouts
Navigation
App-WIde State Management
Handling user input
Sending HTTP requests
Authentication
Publishing

Expo and other tools.

6 personal assignments.
Teamwork (not compulsory) : Implementing a native mobile app

Evaluation scale

H-5

Assessment methods and criteria

The maximum number of points available from the course is 120.

Of that maximum, 60 points comes from personal exercises, 40 points from teamwork, and 20 points from being present on the lectures.

The course evaluation scale is the following:

Min points -> Grade

0 -> 0
40 -> 1
56 -> 2
72 -> 3
88 -> 4
104 -> 5

Please note this additional condition: You must get at least 25 points from the personal exercises.

The points from being present are calculated using the following scale:

Percentage of being present on the normal lectures -> points

30% -> 5
45% ->10
60%->15
80%->20

Please also note that by being present you can earn some of the points available from the individual exercises working together with the instructor.

You must be present in demonstration. It does not accumulate your points of being present. If you are not present in the demonstrations, then there is a reduction of 50 % of the points of your returned exercises on these demos. There is also a reduction of 50 % for exercises that are returned late. No exercises are accepted after the end date of the course implementation. After the end date of the course, no substitute or supplementary assignments will be given either. The student must therefore make sure that they collect enough points from different performances during the time of the course.

Assessment criteria, fail (0)

The student has not managed to accumulate enough points to pass the course during the time of the course. Consequently, they have not been able to demonstrate the kind of competence on the basis of which an acceptable grade could be given.

Assessment criteria, satisfactory (1-2)

The student knows what the native mobile apps are and knows issues related to their development
The student knows the basics of React Native
The student can implement a simple React Native App
The student knows some of the key tools used in JavaScript programming
The student knows some main tools used in React Native development

Assessment criteria, good (3-4)

The student knows what the native mobile apps are and knows issues related to their development
The student masters the basics of React Native
The student can implement a React Native App and utilize some of the React Native libraries
The student can search information to develop their mobile application skills and to solve problems
The student can utilize some key tools used in React Native development
The student can work in a mobile app development project

Assessment criteria, excellent (5)

The student knows what the native mobile apps are and knows extensively issues related to their development
The student masters extensively the basics React Native
The student can efficiently implement a React Native App and utilize the React Native libraries
The student can implement modular React Native Apps
The student can efficiently search information to develop their mobile application skills and to solve problems
The student can utilize key tools used in React Native development
The student can show initiative and work efficiently in different roles in a mobile app development project from the requirement collection to publishing

Objective

After completing the course, the student can:
- work with advanced topics in data engineering and AI

Content

Advanced topics in Data Engineering, AI and data analytics such as
- application security
- data privacy
- legislation on data protection
- ethics of AI

Materials

Course materials are prepared by the lecturer from various sources including books, online material, etc.

Recommended books to study in this course are:
-- Practical Data Privacy: Enhancing Privacy and Security in Data 1st Edition by Katharine Jarmul
-- Fundamentals of Data Engineering: Plan and Build Robust Data Systems 1st Edition
by Joe Reis and Matt Housley

Teaching methods

Weekly contact sessions with total of 3 hours of theory and practical exercises.

Exam schedules

Exams including retake will be in Week 48 or 49 (at the same day as we have the regular lectures).

International connections

The course includes about 11 theory sessions and personal practice tasks.

This learning method combines theoretical knowledge with practical applications and real-world examples. It emphasizes understanding data engineering fundamental and privacy AI concepts, studying relevant technologies and techniques, and exploring practical implementations and use cases. Hands-on exercises, case studies, and projects will be incorporated to reinforce the learning experience

Completion alternatives

The exercises are mainly performed using Jupyter Notebook or other types of code scripts. Students will use TensorFlow and/or PyTorch. Strong python programming skills are needed to complete the exercises in part II.

Student workload

11 sessions (2.9-29.11.24 ) each 3 hours (2h lecture, 1h practice)+ Exam

Contact hours:
- Weeks 36 - 47: Theory & practice (3h/week): 11 x 3h = 33h
- Week 48: Exam: 2h
- In addition, about 5 support and inquiry hours (biweekly): 5x 1h = 5h

Total contact hours: 40 hours
Independent study and homework: about 90 h

Content scheduling

The course will be provided in two parts covering the following concepts:
Part I:
-- data security (encryption)
-- data privacy
-- data warehouses and data lakes
-- legislation on data protection (GDPR, data act)
Part II:
-- Data Regulations and Ethics in AI
-- Synthetic data generation
-- Differential privacy techniques
-- Decentralized machine learning and federated learning

Evaluation scale

H-5

Assessment methods and criteria

This course comprises 100 points including:
-- 22 points (1+1p each contact class: Lecture and Practical Session)
-- 44points for exercises
-- 34points for the exam

-Participation and exercises (50% of total to pass): Students must achieve at least 50% of the points to pass the course. Participation points can only be gained by being present in class during the Lecture and Practical sessions.

- Exam (50% of total points to pass): Students must achieve at least 50% of the points in order to pass the course.

The course is graded on a scale of 0-5.

Grading will be according to the total points collected by the student during the course as well as the exam.
1: 50% (minimum to pass the course)
2: 60-69%
3: 70-79%
4: 80-89%
5: 90-100%

Assessment criteria, fail (0)

<50% of total points or failed exam, exercise or participation points total.

Assessment criteria, satisfactory (1-2)

50-69% of the total points with passed exam, exercise and participation.

Assessment criteria, good (3-4)

70-89% of the total points with passed exam, exercise and participation.

Assessment criteria, excellent (5)

90-100% of the total points with passed exam, exercise and participation.

Objective

After completing the course a student can:
- find him/herself a work place in the field of his/her education
- complete work assignments together with workmates
- apply the knowledge and skills obtained during studies in work assignments
- describe the employer organization’s business idea, factors of profitability and elements of entrepreneurship
- evaluate the results of the work placement period.

Content

Getting familiar with the profession in the own field of technology.
The minimum total extent of practice included in the B.Eng. degree is 30 cr.

Evaluation scale

Hyväksytty/Hylätty

Qualifications

Basic Practice

Objective

After completing the course a student can:

find him/herself a work place in his/her competence area
complete work assignments connected to his/her competence area alone and as a member of a group
prearrange his/her workload and assignments
describe the employer organization’s leadership, external and internal communication and development of personnel
evaluate the results of the work placement period.

Content

Getting familiar with profession and work assignments in the student’s own competence area.

The minimum total extent of practice included in the B.Eng. degree is 30 cr.

Evaluation scale

H-5

Qualifications

Basic Practice, Field-Specific Practice

Objective

After completing the course, the student can:
- describe what kind of AI applications are available
- describe how AI based applications can be developed
- develop applications using AI

Content

Actual content is decided during the course implementation phase.
The contents vary every year.

Materials

Material available via the learning environment (ITS).

Teaching methods

The course includes about 12 theory sessions and personal practice tasks (3h),

There will be also quest lecturers (from companies or RDI people)

Exam schedules

No exam or in week 17.

International connections

This learning method combines theoretical knowledge with practical applications and real-world examples.
Weekly assignments based on the topics covered.
Around half of the exercises are done during the contact hours.
Additionally, exercises for home work.

Additionally:
- Mid-term project: Develop a simple AI application
- Final project/exam: Comprehensive AI application using multiple techniques learned in the course

Completion alternatives

None.

Student workload

Contact hours:
- Week 3: Course Introduction 2h
- Weeks 4 - 16: Theory & practice (3h/week): 12 x 3h = 36h
- Week 17: Exam/Finals 2h
Total contact hours: 40 hours

Independent study and homework: about 90 h

Total: approximately: 130 hours

Content scheduling

Weekly time schedule plan
3. Introduction to Course and AI-based applications & Examples of AI-Based Applications in various industries
4. Steps to develop AI Applications with a help of tools and frameworks
5. Data-Driven AI and techniques for data-driven AI development
6. Use of Open Data and building Decision Trees with it
7. Handling and processing tabular data and applications of tabular data
9. Generative AI and applications of generative AI (e.g., art, music, text generation)
10. Language Models (e.g., GPT, BERT) and NLP applications NLP
11. Computer Vison and it's real-world applications (e.g., facial recognition, autonomous vehicles)
12. Object Recognition and techniques & applications for object recognition
13. Synthetic Data and use cases of it.
14. Optimization in AI and applications of optimization in AI models
15. IBM Watson and practical applications with it (e.g. image recognition, NLP)
16. Building and training models using PyTorch & TensorFlow
17. Exam/Presentation of final project results
+ projects to build an AI application during the course (one alone and another in team)

Further information

ItsLearning

Evaluation scale

H-5

Assessment methods and criteria

You can achieve points from participation, exercises, participation and exam/final project:
- 20% points from participation
- 50% points from practical exercises in class room and home work
- 30% points from the final project work/exam

Objective

After completing the course the student can:
Knows the main alternatives technologies on the server-side in developing web applications.
Masters one server-side scripting language and can use some important libraries.
Understands the basics of web application architectures.
Can use a content management system or an application framework in implementing a web application.
Can use efficient tools in server-side scripting.

Content

Learning a server-side scripting language.
Introduction to web application architectures.
Integrating a database server to a web application.
Using a content management system or an application framework in implementing a web application.
Tools for server-side scripting.
Implementing a small scale web application.

Materials

* Coursebook:
Get Programming with Node.js
Jon Wexler
Manning Publications
1 edition (March 15, 2019)

* The book is 480 pages, but the reading area of the course is less than 300 pages.

* Unfortunately, the book is not available in electronic form through the library of our educational institution.

Teaching methods

- Programming By doing learning
- Interaction with teacher and classmates
- Teamwork project

International connections

The course includes approximately 12 theory sessions and practice sessions where students work with practical tasks.

Additionally, there are 5 x 1h online Q&A sessions for extra support.

Furthermore, exercises for home work that will be partly demonstrated in during contact sessions.

A teamwork project will be introduced in the second month, requiring students to apply their teamwork skills and knowledge gained from the course to implement their final project

We may also utilize a flipped-classroom model for some lectures, where students will study the theoretical part at home and engage in practical implementation and discussions during class.

Student workload

- 12 times 3h theory and practice: 12 x 3h = 36h
- 5 times 1h online Q&A sessions = 5h
- Home and independent work: approximately 70h
- Teamwork final project: approximately 24h

Total: approximately 135 hours

Content scheduling

The course includes approximately 12 supervised work and theory sessions.
Additionally, 10 personal exercises for homework that will be partly demonstrate in during contact session.
Furthermore, the course has a teamwork project that must be done in a group of 4 students.

* Exercise work is done individually outside the instructional sessions. The topic of the assignment is specified during the first month of the course.

* Planned course progress: (preplan)
Content and topic of lectures. We proceed according to the some chapters in the coursebook.
Note: students will also have some independent study or self-study tasks from the book chapters.

1: Chapters 0-2 of the Course Start Theory and Development Environment Creation Book
2: Modules and a simple web server - chapters 3-4 of the book
3. Chapters 5 and 6 of the Request Processing and Routing Book
4: Chapters 8 and 9 of the Express and MVC book
5: Outlook and Error Handling - Chapters 10 and 11
6: MongoDB Database Connection and Mongoose Schemas and Templates - Chapters 13 and 14 of the book
7: Controllers and Models - Chapter 16 of the book
8: Working with Data Models - Chapters 17 and 18 of the book
9: Working with Data Models - Chapters 19 and 21
10: Sessions and User Authentication - Book Chapters 22-24

* Two lectures will be used for demo and other as yet undefined purposes.

* Each group must set aside 15 minutes for the last week of the course to demonstrate the assignment. Individual teamwork must also be able to demonstrate on this occasion.

* The study plan may change as the course progresses.

Further information

** Prerequisites for Back- End Development Course:
1- JavaScript: Basic Syntax and Control Structures, Functions and Scope, Asynchronous Programming

2- Databases

** Recommendations (Optional but Beneficial):
1- Basic Understanding of RESTful APIs: Knowledge of HTTP methods (GET, POST, PUT, DELETE) and status codes.

2- Experience with JSON: Understanding of JSON format for data interchange.

3- Basic Knowledge of Git: Familiarity with version control using Git.

Evaluation scale

H-5

Assessment methods and criteria

- The course is graded on a scale of 0-5.
*
- In order to pass the course, the student must earn at least 50% of the points of personal assignments and 50% of the points of Teamwork project.
*
- You can get a maximum of 10 points for each practice task. You can therefore get a maximum of 100 points for all practice tasks, which affect the evaluation by 3 units.
- Participation in group work: 0.0 - 2.0 units.
*
-Students can earn one extra unit (ECTS) if they participate on at least 9 sessions (2-3h per each session) of the course.
Note: Grades will be rounded down if they include decimals less than 0.5; otherwise, they will be rounded up. (e.g., 3.4 is rounded down to 3.0, but 3.5 or higher is rounded up to 4.0)

Assessment criteria, fail (0)

The student does NOT get at least 50% of the points in project OR did not get at least 50% of the points in the course exercises.

Assessment criteria, satisfactory (1-2)

The student got 40-59% of the points for the exercises in the course AND got a grade of 1 - 3 for the project work.

Assessment criteria, good (3-4)

The student got 40-59% of the points for the exercises in the course AND got a grade of 1 - 3 for the project work.

Assessment criteria, excellent (5)

The student got at least 85% of the points for the exercises in the course AND got a grade 5 for the project work.

Objective

After completing the course the student can:
- Describe how data can be managed and processed
- Describe how data can be stored in various places and formats
- Manage and analyze data with suitable tools
- Utilize data management tools to process data
- Understand and describe how mathematics can be used for data management

Content

Introduction to data management
Data storage formats
Data storage
Introduction to data processing
Linear algebra
Data management tools

Materials

Material available via the learning environment (ITS).

Teaching methods

Weekly contact sessions when 3 hours for theory and practical exercises.

Exam schedules

Exam in Week 49.
Retake exam in January 2025.

International connections

The course includes approximately 12 theory sessions and guided exercises sessions where students work with practical tasks.
Around half of the exercises are done during the contact hours.
Additionally, exercises for home work that will be partly demonstrated in during contact sessions.

Student workload

Contact hours
- 12 times 1h theory: 12 x 1h = 12 hours (groups together)
- 12 times 2h practice: 12 x 2h = 24 hours (in own group)
- Exam: 2 hours
- 1h Q&A sessions 5-6 times = 5 hours
TOTAL: 43 hours

Home and independent work: approximately 90 hours

Total: approximately: 130 hours

Content scheduling

Weeks 36 - 48:
Introduction to data management
Introduction to Jupyter Notebook
Data storage formats
Basics of linear algebra (vectors, matrices, linear equations)
Data processing and visualization with Python
Basics of virtualization and Linux shell commands
Introduction to databases
Recap
Week 49: Exam
Week 49: Exam

Further information

Additional information is share via ITS

Evaluation scale

H-5

Assessment methods and criteria

You can achieve points from participation, exercises, participation and exam:
- 20% points from participation
- 50% points from practical exercises in class room and home work
- 30% points from the exam

Assessment:
- Participation and exercise (50% of total to pass): Students must achieve at least 50% of the points to pass the course.
- Exam (50% of total points to pass): Students must achieve at least 50% of the points in order to pass the course.

The course is graded on a scale of 0-5.
Grading will be according to the total points collected by the student during the course as well as the exam.
1: 50% (minimum to pass the course)
2: 60-70%
3: 70-80%
4: 80-90%
5: 90- 100%

Assessment criteria, fail (0)

Less than 50% points

Assessment criteria, satisfactory (1-2)

50 - 69% points

Assessment criteria, good (3-4)

70 - 89% points

Assessment criteria, excellent (5)

At least 90% points

Objective

After completing the course, the student can:
- Describe what Cloud Services are, how they affect business and which new opportunities it may enable.
- Describe use cases and benefits of cloud services,
- Describe SAAS, PAAS, IAAS.
- Develop a solution that utilizes cloud services.

Content

Introduction to cloud services
Software as a service (SAAS)
Platform as a service (PAAS)
Infrastructure as a service (IAAS)
Virtual machines and containers
Security of cloud services
Project work

Materials

Task-specific material to be announced separately in Its Learning and in AWS academy.

Teaching methods

- Weekly face-to-face meetings with lecture teaching and small group work
- Learning by doing and experimenting (exercise tasks, project work, information search)
- Small group work and peer learning
- Self-study material
- Teacher guidance and examples

Exam schedules

No exam, and retake not possible after evaluation grade is published.

International connections

FLIP classrooms and learning by doing

Completion alternatives

Not possible

Student workload

Contact hours
- Course introduction: 3 hours
- 12 times 3h theory and AWS support: 12 x 3h = 36 hours
- 16 times AWS Academy self paced sessions: 16 x 1h = 16 hours

Home work:
- Working with assignments: approximately 80 hours

Total: approximately 135 hours

Content scheduling

The course content is divided into four learning objectives(CLOs):

CLO1 Analyze classic data centers and cloud data center solutions.

Introduction to Cloud Computing
1.1 Understand the limitations of traditional computing and evolution of cloud computing
1.2 Understand the concepts of Cluster, Grid and Cloud Computing, its benefits and challenges

Cloud Computing Models and Services
1.3 Explore the standard cloud model, cloud deployment and service delivery models
1.4 Understand service abstraction

Resource Virtualization and Pooling
1.5 Implement physical computing resources virtualization
1.6 Implement machine, server level and operating system virtualization
1.7 Understand resource pooling, sharing and resource provisioning

CLO2 Design a cloud data center based on specific technical requirements.

Resource Virtualization and Pooling
2.1 Implement physical computing resources virtualization
2.2 Implement machine, server level and operating system virtualization

Scaling and Capacity Planning
2.3 Understand the foundation of cloud scaling
2.4 Explore scaling strategies and implement scalable applications
2.5 Explore approaches for capacity planning

Load Balancing
2.6 Explore the goals and categories of load balancing. Explore parameters for consideration.

File System and Storage
2.7 Understand the need for high performance processing and Big Data
2.8 Explore storage deployment models and differentiate various storage types

CLO3 Discuss the need for security, reliability and legal compliance of a cloud data center.
Database Technologies
3.1 Explore database models
3.2 Implement relational and non-relational database as a service

Cloud Computing Security
3.3 Understand the threats to cloud security
3.5 Explore and develop a cloud security model
3.6 Understand Trusted Cloud Computing

Privacy and Compliance
3.7 Explore key privacy concerns in the cloud
3.8 Differentiate security vs. privacy
3.9 Develop a privacy policy

CLO4 Design strategies for the implementation of effective cloud solutions to support business requirements.

Content Delivery Model
4.1 Understand and explore content delivery network models in the cloud

Portability and Interoperability
4.2 Explore portability and interoperability scenarios
4.3 Understand machine imaging
4.4 Differentiate virtual machine and virtual appliance

Cloud Management
4.5 Understand cloud service life cycle
4.6 Understand asset management in the cloud
4.7 Explore cloud service management
4.8 Develop disaster recovery strategies

SELF PACED / FLIP CLASSROOM
In addition to the above theoretical content the students will learn and practice the cloud concepts in AWS academy. The AWS academy online course covers the following modules.

Module 1 - Global Infrastructure
Module 2 - Structures of the Cloud
Module 3 - AWS Console
Module 4 - Virtual Servers
Module 5 - Content Delivery
Module 6 - Virtual Storage
Module 7 - Security 1
Module 8 - Security 2
Module 9 - Monitoring the Cloud
Module 10: Databases
Module 11 - Load Balancers and Caching
Module 12 - Elastic Beanstalk and Cloud Formation
Module 13 - Emerging Technologies in the Cloud
Module 14 - Billing and Support
Module 15 - Other Cloud Features
Module 16 - Optimizing the Cloud with the AWS CDK

Further information

Course material and assignments in Its Learning and AWS academy.

Evaluation scale

H-5

Assessment methods and criteria

Personal assignments: 50 points
AWS Academy Course labs: 30 points
Project: 20 points

The assignments must be returned by the deadline to get the points. The assignments returned after the deadline will give you only half of the points.
Demonstrations of exercises during the contact session is mandatory without demonstration you will lose 50% of your marks.

The grading scale (points -> grade):

50 points -> 1
60 points -> 2
70 points -> 3
80 points -> 4
90 points -> 5

Assessment criteria, fail (0)

Fail < 50 points

Assessment criteria, satisfactory (1-2)

50 points -> 1
60 points -> 2

Assessment criteria, good (3-4)

70 points -> 3
80 points -> 4

Assessment criteria, excellent (5)

90 points -> 5

Objective

After completing the course, the student can:
- Describe what Cloud Services are, how they affect business and which new opportunities it may enable.
- Describe use cases and benefits of cloud services,
- Describe SAAS, PAAS, IAAS.
- Develop a solution that utilizes cloud services.

Content

Introduction to cloud services
Software as a service (SAAS)
Platform as a service (PAAS)
Infrastructure as a service (IAAS)
Virtual machines and containers
Security of cloud services
Project work

Materials

Task-specific material to be announced separately in Its Learning and in AWS academy.

Teaching methods

- Weekly face-to-face meetings with lecture teaching and small group work
- Learning by doing and experimenting (exercise tasks, project work, information search)
- Small group work and peer learning
- Self-study material
- Teacher guidance and examples

Exam schedules

No exam, and retake not possible after evaluation grade is published.

International connections

FLIP classrooms and learning by doing

Completion alternatives

Not possible

Student workload

Contact hours
- Course introduction: 3 hours
- 12 times 2h theory: 12 x 2h = 24 hours
- 12 times 1h AWS Support: 12 x 1h = 12 hours
- 16 times AWS Academy self paced sessions: 16 x 1h = 16 hours

Home work:
- Working with assignments: approximately 80 hours

Total: approximately 135 hours

Content scheduling

The course content is divided into four learning objectives(CLOs):

CLO1 Analyze classic data centers and cloud data center solutions.

Introduction to Cloud Computing
1.1 Understand the limitations of traditional computing and evolution of cloud computing
1.2 Understand the concepts of Cluster, Grid and Cloud Computing, its benefits and challenges

Cloud Computing Models and Services
1.3 Explore the standard cloud model, cloud deployment and service delivery models
1.4 Understand service abstraction

Resource Virtualization and Pooling
1.5 Implement physical computing resources virtualization
1.6 Implement machine, server level and operating system virtualization
1.7 Understand resource pooling, sharing and resource provisioning

CLO2 Design a cloud data center based on specific technical requirements.

Resource Virtualization and Pooling
2.1 Implement physical computing resources virtualization
2.2 Implement machine, server level and operating system virtualization

Scaling and Capacity Planning
2.3 Understand the foundation of cloud scaling
2.4 Explore scaling strategies and implement scalable applications
2.5 Explore approaches for capacity planning

Load Balancing
2.6 Explore the goals and categories of load balancing. Explore parameters for consideration.

File System and Storage
2.7 Understand the need for high performance processing and Big Data
2.8 Explore storage deployment models and differentiate various storage types

CLO3 Discuss the need for security, reliability and legal compliance of a cloud data center.
Database Technologies
3.1 Explore database models
3.2 Implement relational and non-relational database as a service

Cloud Computing Security
3.3 Understand the threats to cloud security
3.5 Explore and develop a cloud security model
3.6 Understand Trusted Cloud Computing

Privacy and Compliance
3.7 Explore key privacy concerns in the cloud
3.8 Differentiate security vs. privacy
3.9 Develop a privacy policy

CLO4 Design strategies for the implementation of effective cloud solutions to support business requirements.

Content Delivery Model
4.1 Understand and explore content delivery network models in the cloud

Portability and Interoperability
4.2 Explore portability and interoperability scenarios
4.3 Understand machine imaging
4.4 Differentiate virtual machine and virtual appliance

Cloud Management
4.5 Understand cloud service life cycle
4.6 Understand asset management in the cloud
4.7 Explore cloud service management
4.8 Develop disaster recovery strategies

SELF PACED / FLIP CLASSROOM
In addition to the above theoretical content the students will learn and practice the cloud concepts in AWS academy. The AWS academy online course covers the following modules.

Introduction
Module 1 - Cloud Concepts Overview
Module 2 - Cloud Economics and Billing
Module 3 - AWS Global Infrastructure Overview
Module 4 - AWS Cloud Security
Module 5 - Networking and Content Delivery
Module 6 - Compute
Module 7 - Storage
Module 8 - Databases
Module 9 - Cloud Architecture
Module 10 - Auto Scaling and Monitoring

Further information

Course material and assignments in Its Learning and AWS academy.

Evaluation scale

H-5

Assessment methods and criteria

Personal assignments: 50 points
AWS Academy Course labs: 30 points
Project: 20 points

The assignments must be returned by the deadline to get the points. The assignments returned after the deadline will give you only half of the points.
Demonstrations of exercises during the contact session is mandatory without demonstration you will lose 50% of your marks.

The grading scale (points -> grade):

50 points -> 1
60 points -> 2
70 points -> 3
80 points -> 4
90 points -> 5

Assessment criteria, fail (0)

Fail < 50 points

Assessment criteria, satisfactory (1-2)

50 points -> 1
60 points -> 2

Assessment criteria, good (3-4)

70 points -> 3
80 points -> 4

Assessment criteria, excellent (5)

90 points -> 5

Objective

After completing the course the student can:
- explain the most common data structures
- apply the most common data structures and algorithms connected to the use of these structures
- evaluate the efficiency of algorithms.

Content

- lists, stacks, queues, trees, graphs and hash tables
- analysing and evaluating algorithms
- designing algorithms
- sorting methods
- search algorithms

Materials

Material available via the learning environment (ITS).

Teaching methods

Weekly contact 3 hours sessions for theory and practical exercises.
Additionally, if needed weekly 1h sessions for questions and support in exercises.

Exam schedules

No exam, and retake not possible after evaluation grade is published.

International connections

The course has 12 three-hour contact sessions where teacher present theory and examples and students work with practical tasks.
Additionally, students are able to receive extra guidance for exercises.

Electronic materials are used in the course. In addition, guidance is also organized online in order to reduce the carbon footprint caused by movement.

Completion alternatives

Not possible

Student workload

Contact hours
- Course introduction: 3 hours
- 13 times 2h theory: 13 x 2h = 26 hours
- 13 times 1h demo 13 x 1h = 13 hours - Group 1
- 13 times 1h demo 13 x 1h = 13 hours - - Group 2
- FLIP Classroom 10 X 1h = 10h
Home work:
- Working with assignments: approximately 80 hours


Total: approximately 130 hours

Content scheduling

Week 36: Course introduction

Session from Weeks 36 - 48
- Algorithms and algorithmic thinking
- Data structures
- Search algorithms
- Sorting algorithms

Contact hours according to lukkari.turkuamk.fi.

Further information

ITS and Teams.

Evaluation scale

H-5

Assessment methods and criteria

The course is graded on a scale of 0-5.

You can achieve 80 points from practical exercises in class room and home work exercises.
Around half of the exercises are done during the contact hours.

Demonstrations of exercises during the contact session is mandatory without demonstration you will lose 50% of your marks.


Additionally, there is a group project of 20 points, passing group project is mandatory to pass the course.

Lastly, to pass the course the student need to get at least 40 marks in the exercises and at least 10 marks in the project.

Assessment criteria, fail (0)

Less than 50% points in the exercises OR Student does not passed the group project.

Assessment criteria, satisfactory (1-2)

50 points -> 1
60 points -> 2

Assessment criteria, good (3-4)

70 points -> 3
80 points -> 4

Assessment criteria, excellent (5)

90 points -> 5

Qualifications

Introduction to Programming, or equivalent programming skills

Objective

After completing the course the student:
- Can define the main concepts related to data analytics and machine learning
- Understands the value and the drivers for data analytics and machine learning
- Can describe the processes of data analytics and machine learning
- Can use some tools for data analytics and machine learning

Content

Introduction to data analytics and machine learning
Data analytics process and methods
Machine learning process and methods
Practical work

Materials

Course book:

Aurélien Géron.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
2nd Edition.
Publisher : O'Reilly Media; 2nd edition
(October 15, 2019)

We study chapters 1, 2, 3, 4, 6, 9, and 10 of the book. They have about 300 pages, but some are skipped over.

The course book can be read in electronic form from our institution's eBook Central database.

The course also has reading material, which will be announced during the course.

Teaching methods

- Participating in lectures (theory and practice)
- Learning through hands-on programming (classwork assignments)
- Completing homework assignments
- Interacting with the teacher and classmates
- Enhancing knowledge through teamwork projects
- Following the flipped-classroom model (pre-session self-study of theoretical concepts followed by in-class practical application)

Exam schedules

No exam!

International connections

- The course includes approximately 14 theory and practice sessions, where students engage with practical tasks.
- Homework exercises will be assigned, with some parts demonstrated during contact sessions.
- A teamwork project will be introduced in the second month, requiring students to apply their teamwork skills and the knowledge gained from the course to implement their final project.
- A flipped-classroom model may be used for some lectures, where students study the theoretical content at home and focus on practical implementation and discussions during class.

Completion alternatives

The practice works and exercises are mainly performed using Python and Jupyter Notebook.

Student workload

+ Student Responsibilities:
1. Class Participation and Assignments:
- Active participation in all classes, including the completion of in-class assignments, which must be submitted during class hours.
2. Homework Assignments:
- Completing 8-10 individual homework assignments, partially demonstrated during contact sessions. The exact number of the assignments will be announced at the first lecture)
3. Final Project:
- A group project (2-3 students) to be completed over Weeks 46 & 47, culminating in a presentation in Week 48.

+ Student workload:
Contact hours (approximately):
- One introductionary session: 2h
- 13 times 3h theory and practice: 13 x 3h = 39 hours
- Final projects and presentations: 24 hours
- Home work: approximately 75 hours

Total: approximately: 140 hours

Content scheduling

+ The course includes approximately 14 guided working and theory sessions, 9 personal homework assignments, 8-9 classwork assignment and a teamwork project

+ Final project is done in groups of 2-3 people outside of guidance sessions. The group sets aside 15 minutes to present the group work during the last session.

+ Content scheduling
- Week 03: Course Introduction (2h)
- Week 04: Landscape of machine learning (3h)
- Week 05: Data exploration (3h)
- Week 06: Data preparation (3h)
- Week 07: Model training, selection, and evaluation (3h)
- Week 08: Winter break - Visualization (self-study)
- Week 09: Demonstrations of Exercises 1 – 4 (3h)
- Week 10: Classification (3h)
- Week 11: Training models (3h)
- Week 12: Decision trees (3h)
- Week 13: Unsupervised learning (3h)
- Week 14: Guidance to team work (3h)
- Week 15: Introduction to Neural networks (3h)
- Week 16: Demonstrations of Exercises 5 – 9 (3h)
- Week 17: Team work presentations (3h)

Further information

+ Qualifications:
- Python programming skills and skills in utilizing Pandas for data manipulation and Numpy for numerical operations and array handling
- Basic knowledge of probability, statistics and linear algebra

+ Communication Channel:
Itslearning and email

Evaluation scale

H-5

Assessment methods and criteria

1) The course is graded on a scale of 0-5

2) Students can achieve maximum 200 points from this course that contains:
- Participation and classwork assignments: participating on each lecture and submitting the related classwork assignment during the class hours 1+2 = 3p => 9 X 4 = 36 points.
- Homework assignments: each homework assignment has 10-15 points. There are 9 homework assignments =>minimum 90 points and maximum 135 points.
- Teamwork assignment: 29 points

Assessment criteria, fail (0)

The student did NOT get at least 50% of the points in teamwork assignment OR did not get at least 50% of the points in the homework assignments OR did not get at least 50% of the points in participation and classwork submission.

Assessment criteria, satisfactory (1-2)

The student got 50-65% of the points for the homework assignments AND got 50-65% of the points for the participation and classwork assignments submission AND got 50-65% of the points for the teamwork assignment.

Assessment criteria, good (3-4)

The student got 66-85% of the points for the homework assignments AND got 66-85% of the points for the participation and classwork assignments submission AND got 66-85% of the points for the teamwork assignment.

Assessment criteria, excellent (5)

The student got at least 86% of the points for the homework assignments AND got at least 86% of the points for the participation and classwork assignments submission AND got at least 86% of the points for the teamwork assignment.

Objective

After completing the course, the student can:
- Can define the main concepts, values and drivers for deep learning
- Can describe how machine learning and AI solutions can be developed with deep learning and neural networks
- Use tools when creating the solutions

Content

Deep Learning
Neural Networks
Natural Language Processing
Pattern Recognition
Computer Vision
Practical work

Materials

Course materials are prepared by the lecturer from various sources including books, online material, etc.

Recommended books to study in this course are:
-- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition by Aurélien Géron, OREILLY, 2021
-- Deep Learning with Python, François Chollet

Exam schedules

Exam will be in the first week of December (at the same date as we have the regular lectures).

International connections

The course includes 12 theory sessions and personal practice tasks.

The lectures cover the main theories, techniques, and algorithms in basics of deep learning, starting with fundamental concepts such as neural networks, optimization and regularization techniques, modelling, and fine tuning. Through the course, there will be more practical applications of deep learning such as CNNs, RNNs, Computer Vision, and NLP.

Completion alternatives

The exercises are mainly performed using Jupyter Notebook. Student will use TensorFlow and/or PyTorch. Strong python programming is need to complete the exercises.

Student workload

12 sessions (2.9-29.11.24 ) each 3 hours (1h lecture, 2h practice)+ Exam

Contact hours:
- Course start-up (week 36): 2h
- Weeks 37 - 48: Theory & practice (3h/week): 12 x 3h = 36h
- Week 49: Exam: 2h
- In addition, about 5 support and inquiry hours (biweekly): 5x 1h = 5h

Total contact hours: 45 hours
Independent study and homework: about 90 h

Content scheduling

The course will cover the following concepts:
-- Introduction to Deep Learning
-- Tensors and tensor operations
-- Multi layer Perceptron
-- Gradient based Optimization
-- Back-propagation
-- Loss Functions
-- Activation Functions
-- Convolutional Neural Networks
-- Recurrent Neural Networks
-- DNN Architectures
-- Hyperparameter Fine-Tuning
-- Transfer Learning
Practical Aspects of Deep Learning will be also covered during the course and exercise.

Evaluation scale

H-5

Assessment methods and criteria

After the course, students should understand the main principles of deep learning and steps needed for applying them in real applications. The student especially learns the core concepts of deep neural networks, gradient descent, model evaluation, overfitting, and underfitting and is able to find a suitable balance between these extremes in a given problem at hand.

This course comprises 100 study points including:
-- 24 points (1+1p each contact class: Lecture and Practical Session)
-- 36 points for exercises
-- 40 points for the exam

-Participation and exercise (50% of total to pass): Students must achieve at least 50% of the points to pass the course. Participation is

- Exam (50% of total points to pass): Students must achieve at least 50% of the points (20 points) in order to pass the course.

The course is graded on a scale of 0-5.

Grading will be according to the total points collected by the student during the course as well as the exam.
1: 50% (minimum to pass the course)
2: 60-70%
3: 70-80%
4: 80-90%
5: 90- 100%

Objective

After completing the course, the student can:

Describe what DevOps is and how it relates to software engineering,
Use tools and environments needed for DevOps,
Use DevOps in a software project.

Content

Basics of software production
DevOps model
Tools and Environments for DevOps
DevOps in practice
Project work

Materials

In part I: DevOps theory

1. Learning DevOps: A comprehensive guide to accelerating DevOps culture adoption with Terraform, Azure DevOps, Kubernetes, and Jenkins, 2nd Edition
Mikael Krief

Only the chapter 1: The DevOps Culture and Infrastructure as Code Practices. There are some licenses for this book in our institution's eBook Central database.


In part II: Testing and testing automation

Testing JavaScript Applications
Lucas da Costa
Publisher : Manning (April 13, 2021)

Only selected chapters. There are some licenses for this book in our institution's eBook Central database.


Part III: CI/CD ja GitHub Actions

GitHub's material on the Net


Part IV: Docker and containerization

DevOps with Docker 22
Helsingin yliopisto / mooc.fi
Kousa Jami

Part 1 + Part 2 (only partly)

https://devopswithdocker.com/

Teaching methods

- reading the course books and other reading material, watching videos
- participating in the lectures
- configuring, using commands, and programming together with instructor
- working independently
- participating in the teamwork

Exam schedules

No exam.

Completion alternatives

The student can complete the course by demonstrating his knowledge and skills of the subjects of the course, for example with the work samples they have made. However, this must be agreed with the instructor during the first 4 weeks of the course.

The student can include a corresponding course taken elsewhere at some educational institution that is acceptable by our educational institution. This happens via AHOT process. Also this matter should be initiated immediately at the beginning of the course.

Student workload

45 h contact lessons and independent work in the presence of the instructor
2-4 h presenting and following team works
40 h preparing teamwork
45 h doing personal exercises

Content scheduling

Contents

I. DevOps theory:
- 1 lecture
- 1 personal exercise as a multiple-choice exam: 10 points

II. Testing and its automation
- Basics of software testing in theory and practice
- The importance of testing in DevOps
- 3-4 lectures and demos:= 4-5 lessons in a classroom
- 2 personal exercises: 2 * 10 = 20 points


III. CI/CD and GitHub Actions
- Implementing continuous integration and continuous delivery by using a workflows
- 3 lectures and Demos = 4 lessons in a classroom
- 2 personal exercises: 2 * 10 = 20 points


IV. Containerization - Dockers
- Containers and their use in DevOps
- 2-3 lectures and Demos = 3-4 lessons in a classroom
- 2 personal exercises 2 * 10 = 20 points

V. Team work :
- In groups of 4 people
- Group-specific DevOps-related commission
- Presentation to other students of the course
- 30 points

A total of 100 points + max 20 points for attendance. Must be present at demos.

Evaluation scale

H-5

Assessment methods and criteria

The maximum number of points available from course is 120.

Of that maximum, 70 points comes from the personal exercises, 30 points from the teamwork, and 20 points from being present on the lectures.

The course evaluation scale is the following:

Min points -> Grade

0 -> 0
40 -> 1
56 -> 2
72 -> 3
88 -> 4
104 -> 5

Please note this additional condition: You must get at least 20 points from the exercises and 10 points from the teamwork to pass the course.

The points from being present are calculated using the following scale:

Percentage of being present on the normal lectures -> points

20% -> 5
40% ->10
60%->15
80%->20

Please also note that by being present you can earn some of the points available from the personal exercises working together with the instructor.

You must be present in demonstration. It does not accumulate your points of being present. If you are not present in the demonstrations, then there is a reduction of 25 % of the points of your returned exercises on these demos. There is also a reduction of 25 % for the exercises that are returned late. No exercises are accepted after the end date of the course implementation. After the end date of the course, no substitute or supplementary assignments will be given either. The student must therefore make sure that he collects enough points from different performances during the time of the course.

Assessment criteria, fail (0)

The student has not managed to accumulate enough points to pass the course during the time of the course. Consequently, they have not been able to demonstrate the kind of competence on the basis of which an acceptable grade could be given.

Assessment criteria, satisfactory (1-2)

The student knows what DevOps is
The student knows the different forms of software testing and their purposes
The student knows how to prepare simple tests
The student knows the CI/CD process and understands its purpose
The student knows how to prepare a simple CI/CD pipeline
The student knows what containerization is
The student knows how to create a container

Assessment criteria, good (3-4)

The student knows what DevOps is
The student knows the different forms of software testing and their purposes
The student can do some software testing in practice
The student knows the CI/CD process and understands its purpose
The student can implement a CI/CD pipeline that e.g. runs tests
The student knows what containerization is
The student knows how to create and use containers
The student knows how to implement functional automation solutions
The student is knows security issues related to automation solutions

Assessment criteria, excellent (5)

The student knows what DevOps is
The student knows the different forms of software testing and their purposes
The student can design software testing and do it in practice
The student knows the CI/CD process and understands its purpose
The student can prepare e.g. a CI/CD pipeline running tests
The student knows what containerization is
The student knows how to create and use containers
The student knows how to implement automation solutions that meet the requirements and that combine automatic testing, containerization, automation sliding belts and possible other technologies and principles into effective solutions
The student knows how to take into account issuesrelated to the information security of automation

Objective

The aim of the course is to activate and develop the students’ field-relevant English language and communication skills. The students will gain professional skills in various spoken and written communicative situations encountered in working life and society. In addition, they will learn to utilize tools and techniques to further develop their skills in authentic, field-specific contexts. More specifically, students will focus on developing their language and communication skills in.
Upon completing the course, the students should have acquired skills to communicate at level B2 according to European Framework of Reference for Languages, which states that at B2-level students should be able to produce clear, coherent and well-structured texts, present detailed descriptions related to one’s field of interest, express and exchange opinions using fluent language, follow complex arguments, and read longer articles and reports.

Content

- writing professional, field-specific texts
- presenting and discussing field-specific topics in a professional manner
- reading field-specific texts and utilizing the information in professional contexts
- learning and using key terminology of the field
- becoming professional, autonomous language users in working life

Materials

The course material is available in Its.

International connections

The course topics include sustainability with study on the key vocabulary, and discussion on the main aspects of sustainability.

Student workload

The students will work on contact meeting tasks, self-study tasks and be tested on their skills with spoken and written assignments on the course topics. The meeting tasks and assignments are completed during the contact meetings only. The self-study tasks are homework.

Content scheduling

The course will run from 2 September to 13 December.

The course consists of contact meetings (12x2h = 24h) and individual/group work in Its.

The course topics are professional emailing and reporting, meetings at work, and professional presentation. The topics include aspects of sustainability and/or digitalization, and field-specific vocabulary.

The aim of the course is to activate and develop the students’ field-relevant English language and communication skills. The students gain professional skills in various spoken and written communicative situations encountered in working life and society.

Learning objectives:

Spoken communication

The student
- presents topics in a structured way
- discusses topics using related terminology
- uses functional language e.g. signposting
- participates actively in discussions by commenting, asking, and reacting
- expresses themselves in a spontaneous way
- expresses themselves in a clear and logical way
- expresses themselves in their own words

Written communication

The student
- follows the structure and language of professional email and reporting
- recognizes and applies the appropriate style for the situation e.g. in terms of the vocabulary
- expresses themselves in a clear and logical way
- writes in their own words using sources correctly

Further information

The channel of communication during the course is Its.

Note that no general attendance is required but the assignments and meeting tasks are completed in contact meetings only (see evaluation).

Evaluation scale

H-5

Assessment methods and criteria

The evaluation is based on

1) four assignments

- email message, mini-report, meeting and presentation
- the evaluation scale for the assignments is 1-5
- specific evaluation criteria is given in connection with the instructions
- the average of individual assignment evaluations forms the final evaluation
- the assignments are completed in contact meetings only

2) meeting tasks and self-study tasks

- 8 tasks
- the evaluation scale is passed/failed
- if you return less than 6 tasks, the final evaluation will drop by one grade
- the meeting tasks are completed in contact meetings only

Points to be noted:

- For full final evaluation complete ALL course work within the given deadlines.
- Note that course work cannot be done again or replaced by other work, or an exam.
- With problems of completing course work, contact the teacher BEFORE the deadlines.
- Assignments are not accepted after 15 December 2024.

Objective

The aim of the course is to activate and develop the students’ field-relevant English language and communication skills. The students will gain professional skills in various spoken and written communicative situations encountered in working life and society. In addition, they will learn to utilize tools and techniques to further develop their skills in authentic, field-specific contexts. More specifically, students will focus on developing their language and communication skills in.
Upon completing the course, the students should have acquired skills to communicate at level B2 according to European Framework of Reference for Languages, which states that at B2-level students should be able to produce clear, coherent and well-structured texts, present detailed descriptions related to one’s field of interest, express and exchange opinions using fluent language, follow complex arguments, and read longer articles and reports.

Content

- writing professional, field-specific texts
- presenting and discussing field-specific topics in a professional manner
- reading field-specific texts and utilizing the information in professional contexts
- learning and using key terminology of the field
- becoming professional, autonomous language users in working life

Materials

The course material is available in Its.

International connections

The course topics include sustainability with study on the key vocabulary, and discussion on the main aspects of sustainability.

Student workload

The students will work on contact meeting tasks, self-study tasks and be tested on their skills with spoken and written assignments on the course topics. The meeting tasks and assignments are completed during the contact meetings only. The self-study tasks are homework.

Content scheduling

The course will run from 2 September to 13 December.

The course consists of contact meetings (12x2h = 24h) and individual/group work in Its.

The course topics are professional emailing and reporting, meetings at work, and professional presentation. The topics include aspects of sustainability and/or digitalization, and field-specific vocabulary.

The aim of the course is to activate and develop the students’ field-relevant English language and communication skills. The students gain professional skills in various spoken and written communicative situations encountered in working life and society.

Learning objectives:

Spoken communication

The student
- presents topics in a structured way
- discusses topics using related terminology
- uses functional language e.g. signposting
- participates actively in discussions by commenting, asking, and reacting
- expresses themselves in a spontaneous way
- expresses themselves in a clear and logical way
- expresses themselves in their own words

Written communication

The student
- follows the structure and language of professional email and reporting
- recognizes and applies the appropriate style for the situation e.g. in terms of the vocabulary
- expresses themselves in a clear and logical way
- writes in their own words using sources correctly

Further information

The channel of communication during the course is Its.

Note that no general attendance is required but the assignments and meeting tasks are completed in contact meetings only (see evaluation).

Evaluation scale

H-5

Assessment methods and criteria

The evaluation is based on

1) four assignments

- email message, mini-report, meeting and presentation
- the evaluation scale for the assignments is 1-5
- specific evaluation criteria is given in connection with the instructions
- the average of individual assignment evaluations forms the final evaluation
- the assignments are completed in contact meetings only

2) meeting tasks and self-study tasks

- 8 tasks
- the evaluation scale is passed/failed
- if you return less than 6 tasks, the final evaluation will drop by one grade
- the meeting tasks are completed in contact meetings only

Points to be noted:

- For full final evaluation complete ALL course work within the given deadlines.
- Note that course work cannot be done again or replaced by other work, or an exam.
- With problems of completing course work, contact the teacher BEFORE the deadlines.
- Assignments are not accepted after 15 December 2024.

Objective

By the end of this course, students will be able:

· Configure single-area OSPFv2 in both point-to-point and multiaccess networks.
· Explain how to mitigate threats and enhance network security using access control lists and security best practices.
· Implement standard IPv4 ACLs to filter traffic and secure administrative access.
· Configure NAT services on the edge router to provide IPv4 address scalability.
· Explain techniques to provide address scalability and secure remote access for WANs.
· Explain how to optimize, monitor, and troubleshoot scalable network architectures.
· Explain how networking devices implement QoS.
· Implement protocols to manage the network.
· Explain how technologies such as virtualization, software defined networking, and automation affect evolving networks.

Content

Enterprise Networking, Security, and Automation (ENSA) describes the architecture, components, operations, and security to scale for large, complex networks, including wide area network (WAN) technologies. The course emphasizes network security concepts and introduces network virtualization and automation. Students learn how to configure, troubleshoot, and secure enterprise network devices and understand how application programming interfaces (API) and configuration management tools enable network automation. The course includes activities using Packet Tracer, hands-on lab work, and a wide array of assessment types and tools.

Materials

All needed material will be available online in https://www.netacad.com
Further course enrollment instructions are provided by instructor.
Please register to the site using school email.

Exam schedules

Theory final exam and Packet Tracer exam will held in course.
You can do one re-exam within course deadline.
NOTE: Course ending time shown in academy system is not real, please check the course plan for end date!

Student workload

Lecturing and laboratory work each week
Independent studying, including:

- Studying the course material
- Completing exercises
- Preparation for finals exam(s)

Content scheduling

Course describes the architecture, components, operations, and security to scale for large, complex networks, including wide area network (WAN) technologies. The course emphasizes network security concepts and introduces network virtualization and automation. Students learn how to configure, troubleshoot, and secure enterprise network devices and understand how application programming interfaces (API) and configuration management tools enable network automation.

By the end of this course, students will be able:

- Configure single-area OSPFv2 in both point-to-point and multiaccess networks.
- Explain how to mitigate threats and enhance network security using access control lists and security best practices.
- Implement standard IPv4 ACLs to filter traffic and secure administrative access.
- Configure NAT services on the edge router to provide IPv4 address scalability.
- Explain techniques to provide address scalability and secure remote access for WANs.
- Explain how to optimize, monitor, and troubleshoot scalable network architectures.
- Explain how networking devices implement QoS.
- Implement protocols to manage the network.
- Explain how technologies such as virtualization, software defined networking, and automation affect evolving networks.

Evaluation scale

H-5

Assessment methods and criteria

Laboratory assignments in laboratory room
Packet tracer assignments done at home
Module exams
Practice final exams
Theory final exam and Packet Tracer final exam.


The overall result is the sum of the all results of the assignments and exams, passing limit is 60%.
Detailed grading limits will be provided in course plan when course starts but past grading limits have been the following:

Less than 60% Fail
60-67.4% Grade 1
68-75.4% Grade 2
76-83.4% Grade 3
84-91.4% Grade 4
91.5% or higher Grade 5

Qualifications

Courses Internet Networks and Security (5051215) and Introduction to Networks (TE00BU42) ,or equivalent skills.

Objective

After completing the course the student:
masters JavaScript and can use some of the most important libraries in developing browser user interfaces
can implement dynamic and responsive browser user interfaces that are usable in variety of devices
masters AJAX technology and JSON data-interchange format
can use efficient tools in browser scripting

Content

JavaScript
jQuery
Doing asynchronous requests with AJAX
JSON data-interchange format
Tools for developing browser interfaces
Implementing a small scale dynamic and responsive browser user interface

Materials

The course material (Only selected parts from the books)
*
Eloquent JavaScript
Marijn Haverbeke
No Starch Press; 4 edition (Nov 5, 2024)
Available on the Net: http://eloquentjavascript.net
*
Professional JavaScript for Web Developers
5th Edition
Matt Frisbie
Published by Wrox
Available in ProQuest EBook Central
*
Selected project-based React-tutorial
*
Learning React : Modern Patterns for Developing React Apps
2nd edition
Alex Banks and Eve Porcello
Available in ProQuest EBook Central
*

Teaching methods

- reading the course books and other reading material, watching videos
- participating in the lectures
- programming together with instructor
- programming alone
- participating in the teamwork

Exam schedules

No exam.

Completion alternatives

The student can complete the course by demonstrating his knowledge and skills of the subjects of the course, for example with the work samples they have made. However, this must be agreed with the instructor during the first 4 weeks of the course.

The student can include a corresponding course taken elsewhere at some educational institution that is acceptable by our educational institution. This happens via AHOT process. Also this matter should be initiated immediately at the beginning of the course.

Student workload

39 h contact lessons (Each 3h = 2h learning and 1h individual working with the presence of the instructor)
4 h presenting and following team works
40 h preparing teamwork
54 h doing personal exercises

Content scheduling

Contents

I. JavaScript (Lectures and personal exercises)

- Basics
- Strings
- Objects, destructuring
- Arrays, array operations
- Programming functions
- Error handling
- DOM, event handling
- Modules
- Asynchronous programming
- Tools

II. React (Lectures and a teamwork)

- Basics
- JSX
- Components
- Modularization
- Tools
- Managing state
- Hooks

III. Teamwork: A simple single page web application with React (without backend)

7 personal JavaScript exercises.
React-based Teamwork.

Further information

itsLearning and email

Evaluation scale

H-5

Assessment methods and criteria

The maximum number of points available from course is 120.

Of that maximum, 70 points comes from individual exercises, 30 points from teamwork, and 20 points from being present on the lectures.

The course evaluation scale is the following:

Min points -> Grade

0 -> 0
40 -> 1
56 -> 2
72 -> 3
88 -> 4
104 -> 5

Please note this additional condition: You must get at least 20 points from the exercises and 10 points from the teamwork to pass the course.

The points from being present are calculated using the following scale:

Percentage of being present on the normal lectures -> points

20% -> 5
40% ->10
60%->15
80%->20

Please also note that by being present you can earn some of the points available from the individual exercises working together with the instructor.

You must be present in demonstration. It does not accumulate your points of being present. If you are not present in the demonstrations, then there is a reduction of 50 % of the points of your returned exercises on these demos. There is also a reduction of 50 % for exercises that are returned late. No exercises are accepted after the end date of the course implementation. After the end date of the course, no substitute or supplementary assignments will be given either. The student must therefore make sure that he collects enough points from different performances during the time of the course.

Assessment criteria, fail (0)

The student has not managed to accumulate enough points to pass the course during the time of the course. Consequently, they have not been able to demonstrate the kind of competence on the basis of which an acceptable grade could be given.

Assessment criteria, satisfactory (1-2)

The student knows the application areas and the application environments of the JavaScript programming language
The student knows the basics of the modern JavaScript programming language
The student knows at least of the central front-end libraries of the JavaScript programming language
The student knows some of the key tools used in JavaScript programming
The student knows how to program simple applications with JavaScript or its library

Assessment criteria, good (3-4)

The student knows the application areas and the application environments of the JavaScript programming language
The student masters the basics of the modern JavaScript programming and some of the JavaScript's advanced features
The student can apply one of the central front-end libraries of the JavaScript programming language
The student knows how to search for information to develop his JavaScript and programming skills and to solve problems
The student knows how to use some key tools used in JavaScript programming
The student knows how to program applications with JavaScript and its libraries
The student knows how to work in a JavaScript programming project

Assessment criteria, excellent (5)

The student knows the application areas and the application environments of the JavaScript programming language
The student masters the of the modern JavaScript programming extensively and can utilize efficiently its libraries
The student knows how to efficiently search for information to develop his JavaScript and programming skills and to solve problems
The student knows how to effectively use and search for different tools used in JavaScript programming
The student knows how to design and program modularized applications with JavaScript and its libraries
The student knows how to work proactively and responsibly in a JavaScript programming project

Objective

Deepening knowledge of ICT project work and the most used methods and technologies of software projects.

Content

Project work in an international project team in ICT projects.
Substance knowledge of different ICT field -related topics.
Working life skills (team working, communication, time management, professional attitude and taking responsibility) and problem-solving skills.

Materials

- Various internet sources, links & descriptions online.
- Lecture slides.
- theFIRMA's and course's Itslearning.

Teaching methods

Theory part consists of lectures, independent work, and group work
In project, the student works together with the project team mainly onsite.

Exam schedules

No exam

To successfully pass the course, students must achieve passing grade in both the assignments and the project work components.

If a student does not pass the course, they are required to re-enroll and participate in the course during the next available offering, typically the following academic year.

International connections

Practical assignments and reports
Project work
Self study

Completion alternatives

Project work part can be done in a company, if student has a ICT-related job. This has to be agreed separately with responsible teacher.

Student workload

This course is in total 10 ECTS: 10 x 27h = 270 hours of work.
The course will run during the spring semester 2025.

To pass the course, the student must submit the required assignments on time. Moreover, there are also assignments that are done together during the lecture.

The theory part of the course consists of lectures and activities during the lecture (14x3h), assignments and self-study (91 hours), small group meetings (in total of 2 hours).
Additionally the student is expected to participate in 4 guest lectures and write a report about them (27 hours)

The practical part of the course consists of 108 hours of project work
Majority of project work is done on-site.
For the project managers working in theFirma projects, there is obligatory weekly meeting that they are expected to participate. For other project members, the weekly meeting is voluntary.

Weekly working hours are 20.8 hours/week.

Content scheduling

This course enhances project work skills in the ICT field, as well as deepening knowledge of various ICT-related topics.

Students will collaborate on customer projects related to ICT sector. These projects help develop students’ professional skills, including technical abilities, teamwork, communication, time management, professional attitude, responsibility, and problem-solving.

Project teams are typically international, and the primary language of communication is usually English. This fosters students’ abilities in multicultural communication and collaboration.

Further information

the course's and theFIRMA itslearning and Microsoft Teams

Evaluation scale

H-5

Assessment methods and criteria

The course consists of two parts: the theory part and the project work part.

The theory part includes 6 assignments:
Each assignment is evaluated on a scale of 0-30 points.
Therefore, the maximum number of points from the assignments are 180.
Late submission for the assignments will result in 50% reduction in points.

Additionally, the course includes guest lectures, for which the studenta are required to write a report. The report is evaluated on a scale of 0 – 30 points.

Attendance at theory lectures and small group meetings is recorded. The first and last lectures award students 3 points each, while other lectures and small group meetings are worth 2 points each. In total, there are 40 points for attendance.

Altogether students can earn a maximum of 250 points. These points are evaluated as follows:
Fail: 0 – 74 points
grade 1: 75 – 112 points
grade 2: 113 – 149 points
grade 3: 150 – 187 points
grade 4: 188 – 224 points
grade 5: 225 – 250 points.

Project work:
The students are required to work on the project for a total of 108 hours.
- One working hour equals 1 point.
- Participating in one Tech Club session equals 2 points.
- Hosting one Tech Club session equals 5 points (this includes the time spent on planning the content and presenting).

The formative assessment of students' performance in the projects is based on self and peer assessment, customer feedback (if available), and project manager feedback.

Completing all required hours equals 108 points; every 15 hours missed results in a one grade-point reduction from the student's evaluation.

The final grade of the course is weighted average:
- Assignments, attendance, and the guest lecture report 60%
- Project work (evaluation based on self- and peer assessment) and project hours 40%

Accepted grade cannot be raised.

Assessment criteria, fail (0)

Less than 75 points from the theory part and project working hours not completed.

No show, not carrying out responsibilities, disappearing from team work, lack of communication with other team members.

Student has to pass the theory part and the project work part to complete the course.

Assessment criteria, satisfactory (1-2)

Grade 1: under 113 points from the theory part and completing project work hours.
Grade 2: under 150 points from the theory part and completing project work hours.

Poor, but satisfactory performance both in independent work and team work. Low participation on lectures and other activities.

Student has to pass assignments and project work to complete the course.

Assessment criteria, good (3-4)

Grade 3: under 188 points from the theory part and completing project work hours.
Grade 4: under 225 points from the theory part and completing project work hours.

Good performance both in team work and independent work. Active participation on lectures and other activities.

Student has to pass assignments and project work to complete the course.

Assessment criteria, excellent (5)

Grade 5: 225 or over points from from the theory part and completing project work hours.

Excellent performance both in team work and independent work. Active participation on lectures and other activities.

Student has to pass assignments and project work to complete the course.

Objective

By the end of this course, the student will:

understand the basics of the CDIO concept.
obtain foundational ideas of problem-solving skills and design thinking.
familiarize with usual ICT project management ideologies.
familiarize with basic concepts in software development.
familiarize with the curriculum contents and structure of their degree programme.
know the opportunities for accreditation of studies and recognition of competence, as well as for cross-institutional studying.
understands the importance of study skills and can assess their own areas for improvement.

Content

This course introduces the CDIO (Conceive-Design-Implement-Operate) framework with a specific focus on the 'Conceive and Design' aspects. Students will learn the principles of the CDIO model and its application in engineering and technological disciplines. Students gain practical experience in conceptualizing and designing solutions for real-world challenges. The student receives information about the curriculum, study and support opportunities, and the necessary study skills.

Completion alternatives

-

Content scheduling

This course is instructed in Finnish. Choose 'Finnish' in the language setting to see detailed course information.

Evaluation scale

Hyväksytty/Hylätty

Qualifications

None.

Objective

By the end of this course, the student will:

understand the basics of the CDIO concept.
obtain foundational ideas of problem-solving skills and design thinking.
familiarize with usual ICT project management ideologies.
familiarize with basic concepts in software development.
familiarize with the curriculum contents and structure of their degree programme.
know the opportunities for accreditation of studies and recognition of competence, as well as for cross-institutional studying.
understands the importance of study skills and can assess their own areas for improvement.

Content

This course introduces the CDIO (Conceive-Design-Implement-Operate) framework with a specific focus on the 'Conceive and Design' aspects. Students will learn the principles of the CDIO model and its application in engineering and technological disciplines. Students gain practical experience in conceptualizing and designing solutions for real-world challenges. The student receives information about the curriculum, study and support opportunities, and the necessary study skills.

Materials

Digital material shared in ItsLearning

Teaching methods

Active attendance at contact sessions
Lecture activities and assignments

Exam schedules

-

International connections

Course relies on active and regular participation in contact sessions.

All materials of this course are digital. In addition, student activity is monitored with online tools to reduce the carbon footprint caused by travel.

Completion alternatives

-

Student workload

Contact lessons:
- tutoring sessions 3 x 1h = 3h
- info sessions 4 x 1h = 4h
- lectures 6 x 2h = 12h

In addition, individual work: 63h

TOTAL: 18h + 63h = 81h

Assessment criteria, approved/failed

The final assessment of the course is based on:
1. active and regular participation in contact sessions: presence in at least 9 contact sessions
2. assignments given in contact sessions returned in time and they are acceptable: at least 2 weekly tasks and 2 lecture tasks must be submitted

In addition, student must write a report about recruitment fair / guest lecture

All requirements must be fulfilled to pass the course.

Content scheduling

The student receives information about the curriculum, study and support opportunities, and the necessary study skills.

This course introduces the CDIO (Conceive-Design-Implement-Operate) framework with a specific focus on the 'Conceive and Design' aspects. Students will learn the principles of the CDIO model and its application in engineering and technological disciplines. Students gain practical experience in conceptualizing and designing solutions for real-world challenges.

Practical Training (Basic, Field-Specific and Professional) is started with possibility to demonstrate of previous work experience.

Further information

ITS

Evaluation scale

Hyväksytty/Hylätty

Assessment methods and criteria

1. Active attendance
2. Assignment submission in time

Qualifications

None.

Objective

After participating in the course, the student:
* understands basics of innovation-based entrepreneurship, business idea generation, target customer's analysis, prototype design and planning, and technology development
* is familiar with use of NABC and Lean canvases for business ideas validation
TRL (technology readiness level) and MVP (minimum viable product) concepts
* has got experience on Innovation and business pitching skills for private funding,
as well as grant application from public funding.

Content

Ideation and Teaming up
Problem and Customer Validation
Lean-canvas and business model
Prototyping and Minimum Viable Product
Pitching and Way Forward

Materials

Teachers will provide powerpoints and additiional materials via the ItsLearning environment

Additional literature, for instance:
Maurya, A. (2022). Running lean. " O'Reilly Media, Inc.".
Fitzpatrick, R. (2013). The Mom Test: How to talk to customers & learn if your business is a good idea when everyone is lying to you. Robfitz Ltd.
Ries, E. (2011). The lean startup. New York: Crown Business, 27, 2016-2020.

Teaching methods

Experiential Learning: Direct engagement with real-world problems and solutions.
Project-Based Learning: Long-term development of a business project.
Collaborative Learning: Team-based work to develop and refine ideas.
Active Learning: Interactive workshops and hands-on activities.
Problem-Based Learning: Solving real-world problems through innovation.
Formative Assessment: Ongoing feedback and iterative improvement.
Flipped Classroom: Pre-class preparation for interactive in-class work.
Coaching & Mentorship: Guidance from experienced instructors and mentors.
Pitch Practice: Development of presentation and pitching skills.

Exam schedules

There are not exams. The evaluation is based on the performance of the student, both at individual level and as part of a team.

International connections

Pedagogic Approaches: Emphasis on student-centered, collaborative, experiential, and problem-based learning, supported by coaching and iterative feedback.
Sustainable Development: Encourages students to develop eco-friendly, ethical, and socially responsible business ideas that align with the principles of long-term value creation and systems thinking.

Completion alternatives

There are not alternative methods. Assistance to the events, to at least 80% is mandatory. See all instructions.

Student workload

Teamwork and Assignments:
You’ll be working in teams formed around a business idea from Week 1.
Collaborate with your team on assignments presented during the odd weeks and continue refining these in preparation for presentations in the even weeks.

Odd Weeks (1, 3, 5, 7, 9):
Attend presentations on a new topic by the instructors (e.g., ideation, customer validation, business models).
Work with your team to apply the concepts presented to your business idea (e.g., creating a Lean Canvas, conducting customer interviews).
Prepare a team presentation for the next even week session.

Even Weeks (2, 4, 6, 8):
Present your progress (e.g., business idea, validation results, MVP) to the class.
Receive feedback from instructors and peers to refine your work.

Between Sessions:
After receiving feedback, continue refining your team’s work during the remainder of the week.
Be prepared to apply new concepts from the upcoming session to your ongoing project.

Week 10: Final Pitch and Submission:
Pitch your business idea during the final session.
Submit all your project materials (e.g., business model, customer interviews, MVP) to the instructors for evaluation.

Summary of Obligations:
Collaborate with your team on each assignment.
Present your work during the even weeks.
Refine and improve your work based on feedback.
Submit the final version of all project materials by the end of Week 10.

Content scheduling

Overall Structure
Odd weeks (1, 3, 5, 7, 9): New topic presentations by the instructors, introducing key concepts.
Even weeks (2, 4, 6, 8, 10): Student group presentations where they present the work done, receive feedback, and refine their ideas.

Detailed Weekly Plan
Week 1: Ideation and Teaming Up (16th January 2025)
Objective: Generate initial business ideas, form teams, and understand entrepreneurship basics.
Instructor Presentation:
Introduce entrepreneurship and the Lean Startup methodology.
Lead ideation workshop (NABC Ideation Canvas).
Form teams based on business ideas.
Homework: Teams refine their business ideas and prepare a list of potential ideas with a short description of the chosen one.

Week 2: Ideation Results & Feedback (23rd January 2025)
Student Presentations:
Each team presents their chosen business idea, including initial thoughts on the problem and market.
Instructor Feedback:
Provide feedback on the viability, innovation, and potential of their ideas.
Guide them in refining their business concepts.

Week 3: Problem and Customer Validation (30th January 2025)
Objective: Validate the identified problem and understand target customers.
Instructor Presentation:
Problem validation techniques and customer persona creation.
How to conduct effective customer discovery interviews.
Homework: Teams conduct customer interviews and refine their problem statements and customer personas.

Week 4: Problem & Customer Validation Results (6th February 2025)
Student Presentations:
Each team presents their refined problem statement, customer personas, and the feedback they gathered from interviews.
Instructor Feedback:
Evaluate how well the teams validated their problems and customer assumptions.
Offer suggestions for additional customer validation or problem refinement.

Week 5: Lean Canvas and Business Model (13th February 2025)
Objective: Develop and validate core components of the business model using the Lean Canvas.
Instructor Presentation:
Introduce the Lean Canvas and its 9 components.
Guide teams through filling out their Lean Canvas.
Homework: Teams complete their Lean Canvas and develop a draft business model.

Week 6: Business Model Results (27th February 2025)
Student Presentations:
Each team presents their Lean Canvas, focusing on customer segments, value propositions, and revenue models.
Instructor Feedback:
Provide feedback on how well their business model aligns with the customer problem.
Suggest improvements and refinements.

Week 7: Prototyping and MVP Development (6th March 2025)
Objective: Create a basic prototype and MVP development plan.
Instructor Presentation:
Introduction to prototyping and the concept of Minimum Viable Product (MVP).
Examples of successful MVPs.
Homework: Teams begin prototyping and creating an MVP development plan.

Week 8: Prototyping Results (13th March 2025)
Student Presentations:
Teams present their prototypes and MVP plans, showcasing their progress.
Instructor Feedback:
Evaluate the prototypes and MVP plans, offering suggestions for next steps and refinement.

Week 9: Pitch Preparation (20th March 2025)
Objective: Develop and polish pitch decks for final presentations.
Instructor Presentation:
Teach the art of pitching and essential components of a compelling pitch (value proposition, business model, market potential, team strength).
Provide guidance on creating a professional pitch deck.
Homework: Teams finalize their pitch decks and practice their presentations.

Week 10: Final Pitch Presentations (27th March 2025)
Student Presentations:
Teams deliver their final pitches, simulating a real-world pitch competition.
Instructor Feedback:
Provide comprehensive feedback on their pitch, business model, and overall project.
Offer next steps for further development and growth.
Submission:
Teams submit their full set of materials (business model, customer interviews, MVP plan, etc.) for final evaluation.

Further information

Active team participation and high-quality work are essential for a good grade.
80% attendance is required to avoid failing the course.
The grading reflects both your individual effort and your team’s overall success.

Evaluation scale

H-5

Assessment methods and criteria

Team-Based Performance:
Your grade will be based on two key factors:
Your individual contribution as a valuable team member.
Your team’s overall performance and achievements throughout the course.

Evaluation Criteria:
Grading is about 50 % on team merits and 50 % performance performance, always related to the quality of work done. Course feedback is both giving by teachers at individual level, group level, and collective level. Overall, how well you work with your team to complete tasks and achieve goals, and how you did towards that. We both evaluate your individual efforts and on how your team performs. A strong team effort can lead to higher grades for all members, while poor team performance will negatively impact everyone’s grade.

Attendance Requirement:
You must attend at least 80% of the contact sessions to pass the course. Missing more than 20% of these sessions may result in failing, as active participation is crucial.

Logic behind the course:
The course search for students from all areas from Turku UAS. Innovations need several types of profiles, from programing to bio fields, going to food processing and automated manufacturing, and obviously now AI-based (why not). Optimally, several students would continue with the innovation further, and so this course worked as a bridge for them to develop innovation towards commercialization.

Assessment criteria, fail (0)

Not meeting attendance requirements or unsatisfactory performance within the team.

Assessment criteria, satisfactory (1-2)

Basic contribution but lacking in collaboration, progress, or overall team achievements.

Assessment criteria, good (3-4)

Consistent participation and contribution, with your team achieving solid results.

Assessment criteria, excellent (5)

Outstanding individual collaboration and contribution to a high-performing team, delivering excellent results throughout the course.

Qualifications

-

Objective

After completing the course the student is able to:
Understand and describe the data engineering process life cycle

Content

What is Data Engineering
Data Storage and Retrieval
Data Engineering Lifecycle
Extract, Transform and Load (ETL) process
Introduction to Big Data Frameworks

Materials

- The learning materials including slides and exercises will be prepared by the lecturer from various sources such as online courses and articles, books, videos, etc. The material will be introduced during the lectures and will be available via the learning environment (ITS).

- AWS Academy Data Engineering [91081] Course Materials

- Recommended books:
1. Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python, Crickard III, Paul, Packt Publishing, 2020.
Slides provided by teacher can be found via Itslearning.

2. Fundamentals of Data Engineering, Plan and Build Robust Data Systems
By Joe Reis and Matt Housley, Publisher: O’Reily, First edition, 2022.

Teaching methods

- Participating in lectures (theory and practice)
- Learning through hands-on programming (classwork assignments)
- Completing homework assignments or AWS Academy Course
- Interacting with the teacher and classmates
- Enhancing knowledge through teamwork projects
- Following the flipped-classroom model (pre-session self-study of theoretical concepts followed by in-class practical application)

Exam schedules

No exam, and retake not possible after evaluation grade is published.

There is a final teamwork project where students must demonstrate their work during a presentation event in week 48.

International connections

- The course includes approximately 12 theory and practice sessions, where students engage with practical tasks.
- Additionally, there are 4 online Q&A sessions to provide extra support.
- Homework exercises will be assigned, with some parts demonstrated during contact sessions.
- Integration of Cloud-based data engineering through the AWS Academy course.
- A teamwork project will be introduced in the second month, requiring students to apply their teamwork skills and the knowledge gained from the course to implement their final project.
- A flipped-classroom model may be used for some lectures, where students study the theoretical content at home and focus on practical implementation and discussions during class.

Completion alternatives

The practice works and exercises are mainly performed using VS Code, Jupyter Notebook, Apache Airflow, and AWS services.

Student workload

- Contact teaching:
• We have 12 theory and practice sessions, each lasting 3 hours, conducted weekly: 12 x 3 = 36
• Additionally, there are 4 online Q&A sessions, each lasting 1 hour.
• Total contact teaching hours per course: 40 hours.

- Homework and teamwork assignment:
• Personal assignments (homework) and independent studies: 75 hours
• Teamwork assignment: 20 hours

Total: approximately 135 hours (5 x 27h)

Content scheduling

Course Overview
This course provides an introduction to data engineering, combining theoretical concepts with practical applications. The course is divided into two main parts, each with a distinct focus:

- Part I: Theories and Practice
• Instructor-Led Sessions: Covering general topics in data engineering, taught and supervised by the instructor.

- Part II: Optional AWS Academy Self-Paced Course
• Self-Paced Learning: Students have the option to independently complete the AWS Academy Data Engineering course, gaining in-depth knowledge and earning a certification. This can replace the requirement to complete standard homework assignments.

Course Structure
Part I: Theories and Practice (Instructor Supervision & AWS Academy)
• Week 36: Course Overview and Introduction to AWS Academy Data Engineering
• Week 37: The Data Engineering Ecosystem & AWS Practice
• Week 38: ETL Processes & AWS Practice + Exercise Demo (I)
• Week 39: Introduction to Apache Airflow & AWS Integration
• Week 40: Data Engineering Life Cycle: Data Wrangling & ETL + AWS Practice
• Week 41: Data Wrangling and ETL in Apache Airflow + AWS Practice
• Week 42: Autumn Break
• Week 43: Data Governance and Compliance in Data Engineering + AWS Practice
• Week 44: Exercise Demo + AWS Practice
• Week 45: Continued AWS Course Study
• Weeks 46 & 47: Group Work on Final Projects (in-class) + AWS Practice
• Week 48: Final Project Presentations

Part II: Optional AWS Academy Data Engineering [91081]
- Self-Paced Modules: Students can choose to complete the full AWS Academy Data Engineering course, covering the following modules.
- Module Timeline:
• Week 36: Module 1 - Welcome to AWS Academy Data Engineering
• Week 37: Module 2 - Data-Driven Organizations
• Week 38: Module 3 - The Elements of Data
• Week 39: Module 4 - Design Principles and Patterns for Data Pipelines
• Week 40: Module 5 - Securing and Scaling the Data Pipeline
• Week 41: Module 6 - Ingesting and Preparing Data
• Week 42: Module 7 - Ingesting by Batch or by Stream
• Week 43: Module 8 - Storing and Organizing Data
• Week 44: Module 9 - Processing Big Data
• Week 45: Module 10 - Processing Data for ML
• Week 46: Module 11 - Analyzing and Visualizing Data
• Week 47: Module 12 - Automating the Pipeline

Student Responsibilities
1. Class Participation and Assignments:
• Active participation in all classes, including the completion of in-class assignments, which must be submitted during class hours.
2. Homework Assignments:
• Option A: Complete eight individual homework exercises, partially demonstrated during contact sessions.
• Option B: Complete the full AWS Academy Data Engineering course as a substitute for the homework assignments. To do this, students must follow the weekly schedule and upload their AWS Academy course certificate to the Itslearning platform.
3. Final Project:
• A group project (3-4 students) to be completed over Weeks 46 & 47, culminating in a presentation in Week 48.
________________________________________
Additional Notes
• Flexibility: The option to replace homework with the AWS Academy course allows students to tailor their learning experience to their interests and career goals.
• Integration of AWS: The inclusion of AWS Academy in both the core and optional parts of the course provides a strong foundation in cloud-based data engineering, which is highly relevant in today's industry.
• Project Work: The group project encourages collaboration and the practical application of the skills learned throughout the course.

Further information

Qualifications:
Before taking an "Introduction to Data Engineering with Python" course, students typically need a foundational understanding of several key areas. Here are the mandatory and recommended prerequisite courses and topics.

1. Mandatory Prerequisites: 
1.1. Programming:
1.1.1. Introduction to Programming: Knowledge of programming fundamentals,
including concepts like variables, loops, conditionals, and functions. 
1.1.2. Python Programming: Familiarity with Python, including basic syntax, data
types, control structures, and function and modules
1.1.3. Error Handling 
1.1.4. Object-oriented programming (OOP) 
1.1.5. Data Manipulation: Skills in using Pandas library including DataFrames and
Series, reading, writing, filtering, and transforming data
1.2. Databases: Knowledge of how databases work, including concepts like tables, keys, normalization, and indexing.

2. Recommended Topics:
2.1. Algorithms and Data Structures: Basic understanding of algorithms and data
structures such as arrays, lists, trees, and graphs, which are crucial for data
processing
2.2. Having the fundamental knowledge of cloud services or passing the Cloud Services
Course in TUAS (Lecturer: Ali Khan)
2.3. Version Control Systems: Basic understanding of tools like Git for version control.
2.4. Basic Algebra and Calculus: Fundamental math skills to handle data transformations
and calculations.
2.5. Statistics: Understanding of basic statistical concepts like mean, median, standard
deviation, and probability distributions.

Evaluation scale

H-5

Assessment methods and criteria

1) The course is graded on a scale of 0-5

2) Students can achieve 100 points from this course that contains:
- Participation and classwork assignments: participating on each lecture and submitting the related classwork assignment during the class hours 3p => 12 X 3 = 36 points.
- Homework assignments: each homework assignment has 4-6 points. There are 6-8 homework assignments => 8 x 4 (or 6 x6)= 36 points. (or Completing the AWS Academy Course labs and and uploading the certificate on ITS: 36 points)
- Teamwork assignment: 28 points
Note: the teamwork assignment will be graded on scale 0-5 on Itslearning.

The assignments must be returned by the deadline to get the points. The assignments returned after the deadline will give you only half of the points.
Demonstrations of exercises during the contact session is mandatory without demonstration you will lose 50% of your marks.

3) Evaluation:
50% of total to pass: 50% from participation and classwork + 50% from homework assignments (or AWS Academy Course) + 50% from the teamwork projects to pass

Note: Grades will be rounded down if they include decimals less than 0.5; otherwise, they will be rounded up. (e.g., 3.4 is rounded down to 3.0, but 3.5 or higher is rounded up to 4.0)

Assessment criteria, fail (0)

The student did NOT get at least 50% of the points in teamwork assignment OR did not get at least 50% of the points in the homework assignments (or did not get the AWS Academy course certificate) OR did not get at least 50% of the points in participation and classwork submission.

Assessment criteria, satisfactory (1-2)

The student got 50-65% of the points for the homework assignments (or got the AWS Academy course certificate) AND got 50-65% of the points for the participation and classwork assignments submission AND got a grade of 1 - 3 for the teamwork assignment.

Assessment criteria, good (3-4)

The student got 66-85% of the points for the homework assignments (or got the AWS Academy course certificate) AND got 66-85% of the points for the participation and classwork assignments submission AND got a grade of 4 for the teamwork assignment.

Assessment criteria, excellent (5)

The student got at least 86% of the points for the homework assignments (or got the AWS Academy course certificate) AND got at least 86% of the points for the participation and classwork assignments submission AND got a grade of 5 for the teamwork assignment.

Objective

After completing the course the student is able to:
Understand and describe the data engineering process life cycle

Content

What is Data Engineering
Data Storage and Retrieval
Data Engineering Lifecycle
Extract, Transform and Load (ETL) process
Introduction to Big Data Frameworks

Materials

- The learning materials including slides and exercises will be prepared by the lecturer from various sources such as online courses and articles, books, videos, etc. The material will be introduced during the lectures and will be available via the learning environment (ITS).

- AWS Academy Data Engineering [91081] Course Materials

- Recommended books:
1. Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python, Crickard III, Paul, Packt Publishing, 2020.
Slides provided by teacher can be found via Itslearning.

2. Fundamentals of Data Engineering, Plan and Build Robust Data Systems
By Joe Reis and Matt Housley, Publisher: O’Reily, First edition, 2022.

Teaching methods

- Participating in lectures (theory and practice)
- Learning through hands-on programming (classwork assignments)
- Completing homework assignments or AWS Academy Course
- Interacting with the teacher and classmates
- Enhancing knowledge through teamwork projects
- Following the flipped-classroom model (pre-session self-study of theoretical concepts followed by in-class practical application)

Exam schedules

No exam, and retake not possible after evaluation grade is published.

There is a final teamwork project where students must demonstrate their work during a presentation event in week 48.

International connections

- The course includes approximately 12 theory and practice sessions, where students engage with practical tasks.
- Additionally, there are 4 online Q&A sessions to provide extra support.
- Homework exercises will be assigned, with some parts demonstrated during contact sessions.
- Integration of Cloud-based data engineering through the AWS Academy course.
- A teamwork project will be introduced in the second month, requiring students to apply their teamwork skills and the knowledge gained from the course to implement their final project.
- A flipped-classroom model may be used for some lectures, where students study the theoretical content at home and focus on practical implementation and discussions during class.

Completion alternatives

The practice works and exercises are mainly performed using VS Code, Jupyter Notebook, Apache Airflow, and AWS services.

Student workload

- Contact teaching:
• We have 12 theory and practice sessions, each lasting 3 hours, conducted weekly. (36 hours)
• Additionally, there are 4 online Q&A sessions, each lasting 1 hour.
• Total contact teaching hours per course: 40 hours.

- Homework and teamwork assignment:
• Personal assignments (homework) and independent studies: 75 hours
• Teamwork assignment: 20 hours

Total: approximately 135 hours (5 x 27h)

Content scheduling

Course Overview
This course provides an introduction to data engineering, combining theoretical concepts with practical applications. The course is divided into two main parts, each with a distinct focus:

- Part I: Theories and Practice
• Instructor-Led Sessions: Covering general topics in data engineering, taught and supervised by the instructor.
• AWS Academy Modules: Select topics integrated into practice sessions, enhancing hands-on experience with Cloud-based data engineering.

- Part II: Optional AWS Academy Self-Paced Course
• Self-Paced Learning: Students have the option to independently complete the AWS Academy Data Engineering course, gaining in-depth knowledge and earning a certification. This can replace the requirement to complete standard homework assignments.

Course Structure
Part I: Theories and Practice (Instructor Supervision & AWS Academy)
• Week 36: Course Overview and Introduction to AWS Academy Data Engineering
• Week 37: The Data Engineering Ecosystem & AWS Practice
• Week 38: ETL Processes & AWS Practice + Exercise Demo (I)
• Week 39: Introduction to Apache Airflow & AWS Integration
• Week 40: Data Engineering Life Cycle: Data Wrangling & ETL + AWS Practice
• Week 41: Data Wrangling and ETL in Apache Airflow + AWS Practice
• Week 42: Autumn Break
• Week 43: Data Governance and Compliance in Data Engineering + AWS Practice
• Week 44: Exercise Demo + AWS Practice
• Week 45: Continued AWS Course Study
• Weeks 46 & 47: Group Work on Final Projects (in-class) + AWS Practice
• Week 48: Final Project Presentations

Part II: Optional AWS Academy Data Engineering [91081]
- Self-Paced Modules: Students can choose to complete the full AWS Academy Data Engineering course, covering the following modules.
- Module Timeline:
• Week 36: Module 1 - Welcome to AWS Academy Data Engineering
• Week 37: Module 2 - Data-Driven Organizations
• Week 38: Module 3 - The Elements of Data
• Week 39: Module 4 - Design Principles and Patterns for Data Pipelines
• Week 40: Module 5 - Securing and Scaling the Data Pipeline
• Week 41: Module 6 - Ingesting and Preparing Data
• Week 42: Module 7 - Ingesting by Batch or by Stream
• Week 43: Module 8 - Storing and Organizing Data
• Week 44: Module 9 - Processing Big Data
• Week 45: Module 10 - Processing Data for ML
• Week 46: Module 11 - Analyzing and Visualizing Data
• Week 47: Module 12 - Automating the Pipeline

Student Responsibilities
1. Class Participation and Assignments:
• Active participation in all classes, including the completion of in-class assignments, which must be submitted during class hours.
2. Homework Assignments:
• Option A: Complete eight individual homework exercises, partially demonstrated during contact sessions.
• Option B: Complete the full AWS Academy Data Engineering course as a substitute for the homework assignments. To do this, students must follow the weekly schedule and upload their AWS Academy course certificate to the Itslearning platform.
3. Final Project:
• A group project (3-4 students) to be completed over Weeks 46 & 47, culminating in a presentation in Week 48.
________________________________________
Additional Notes
• Flexibility: The option to replace homework with the AWS Academy course allows students to tailor their learning experience to their interests and career goals.
• Integration of AWS: The inclusion of AWS Academy in both the core and optional parts of the course provides a strong foundation in cloud-based data engineering, which is highly relevant in today's industry.
• Project Work: The group project encourages collaboration and the practical application of the skills learned throughout the course.

Further information

Qualifications:
Before taking an "Introduction to Data Engineering with Python" course, students typically need a foundational understanding of several key areas. Here are the mandatory and recommended prerequisite courses and topics.

1. Mandatory Prerequisites: 
1.1. Programming:
1.1.1. Introduction to Programming: Knowledge of programming fundamentals,
including concepts like variables, loops, conditionals, and functions. 
1.1.2. Python Programming: Familiarity with Python, including basic syntax, data
types, control structures, and function and modules
1.1.3. Error Handling 
1.1.4. Object-oriented programming (OOP) 
1.1.5. Data Manipulation: Skills in using Pandas library including DataFrames and
Series, reading, writing, filtering, and transforming data
1.2. Databases: Knowledge of how databases work, including concepts like tables, keys, normalization, and indexing.

2. Recommended Topics:
2.1. Algorithms and Data Structures: Basic understanding of algorithms and data
structures such as arrays, lists, trees, and graphs, which are crucial for data
processing
2.2. Having the fundamental knowledge of cloud services or passing the Cloud Services
Course in TUAS (Lecturer: Ali Khan)
2.3. Version Control Systems: Basic understanding of tools like Git for version control.
2.4. Basic Algebra and Calculus: Fundamental math skills to handle data transformations
and calculations.
2.5. Statistics: Understanding of basic statistical concepts like mean, median, standard
deviation, and probability distributions.

Evaluation scale

H-5

Assessment methods and criteria

1) The course is graded on a scale of 0-5

2) Students can achieve 100 points from this course that contains:
- Participation and classwork assignments: participating on each lecture and submitting the related classwork assignment during the class hours 1+2 = 3p => 12 X 3 = 36 points.
- Homework assignments: each homework assignment has 4-6 points. There are 6-8 homework assignments => 8 x 4 (or 6 x6)= 36 points. (or Completing the AWS Academy Course labs and uploading the certificate on ITS: 36 points)
- Teamwork assignment: 28 points
Note: the teamwork assignment will be graded on scale 0-5 on Itslearning.

The assignments must be returned by the deadline to get the points. The assignments returned after the deadline will give you only half of the points.
Demonstrations of exercises during the contact session is mandatory without demonstration you will lose 50% of your marks.

3) Evaluation:
To pass the course, you need to achieve 50% of total points: 50% from participation and classwork = 18p AND 50% from homework assignments (or AWS Academy Course) = 18p AND 50% from the teamwork projects = 14p.

Note: Grades will be rounded down if they include decimals less than 0.5; otherwise, they will be rounded up. (e.g., 3.4 is rounded down to 3.0, but 3.5 or higher is rounded up to 4.0)

Assessment criteria, fail (0)

The student did NOT get at least 50% of the points in teamwork assignment OR did not get at least 50% of the points in the homework assignments/ the AWS Academy Labs OR did not get at least 50% of the points in participation and classwork submission.

Assessment criteria, satisfactory (1-2)

The student got 50-65% of the points for the homework assignments/ the AWS Academy Labs AND got 50-65% of the points for the participation and classwork assignments submission AND got a grade of 1 - 3 for the teamwork assignment.

Assessment criteria, good (3-4)

The student got 66-85% of the points for the homework assignments/ the AWS Academy Labs AND got 66-85% of the points for the participation and classwork assignments submission AND got a grade of 4 for the teamwork assignment.

Assessment criteria, excellent (5)

The student got at least 86% of the points for the homework assignments/ the AWS Academy Labs AND got at least 86% of the points for the participation and classwork assignments submission AND got a grade of 5 for the teamwork assignment.

Objective

After completing the course the student can:
Work as a team member and/or project manager in an ICT project.
Customer communication.
Substance knowledge on ICT field.
Work-life skills and soft skills including team work, communication, communication in English, time management, professional attitude and self-management skills.

Content

Project work in an international project team in ICT projects.
Substance knowledge of different ICT field -related topics.
Working life skills (team working, communication, time management, professional attitude and taking responsibility) and problem-solving skills.

Materials

- Various internet sources, links & descriptions online
- Lecture slides
- theFIRMA's and course's itslearning

Teaching methods

Theory part consists of lectures, independent work, and group work.
In project, the student works together with the project team mainly onsite.

Exam schedules

No exam

To successfully pass the course, students must achieve passing grade in both the assignments and the project work components.

If a student does not pass the course, they are required to re-enroll and participate in the course during the next available offering, typically the following academic year.

International connections

Practical assignments and reports
Project work
Self-study

Completion alternatives

Project work part can be done in a company, if student has a ICT-related job. This has to be agreed separately with responsible teacher.

Student workload

This course is in total 10 ECTS: 10 x 27h = 270 hours of work.
The course will run during the autumn semester 2024.

To pass the course, the student must submit the required assignments on time. Moreover, there are also assignments that are done together during the lecture.

The theory part of the course consists of lectures and activities during the lecture(13x3h), assignments and self-study (136 hours), small group meetings (in total of 5 hours), and theFirma information sessions (in total of 9 hours).
Additionally the student is expected to participate in 4 guest lectures and write a report about them (27 hours)

The practical part of the course consists of 81 hours of project work
Majority of project work is done on-site.
For the project managers working in theFirma projects, there is obligatory weekly meeting that they are expected to participate. For other project members, the weekly meeting is voluntary.

Weekly working hours are 20.8 hours/week.

Content scheduling

This course provides students with an understanding how to be a team member in customer projects in ICT field as well as substance knowledge of different ICT field -related topics.

Students will collaborate in teams to undertake ICT field project work on customer projects.

Engaging in the customer projects develop students’ working life skills (for example, such as team working, communication, time management, professional attitude and taking responsibility) and problem solving skills.

Project teams are usually international and the official communication language is usually English. This develops students’ ability to multicultural communication and collaboration.

Further information

the course's and theFIRMA itslearning and Microsoft Teams

Evaluation scale

H-5

Assessment methods and criteria

The course consists of two parts, the theory part and the project work part.

The theory part includes 6 assignments:
- Two of the assignments are assessed by pass/fail scale, meaning that a pass equals 10 points and a fail equals 0 points.
- Four of the assignments the assessments is evaluated with 0-30 points.
Thus, the maximum number of points from the assignments are 140.
Late submission for the assignments will reduce the points by 50%.

In addition, the course includes guest lectures of which the student is required to write a report. The report is evaluated with the scale 0 – 30 points.

The presence in the theory lectures and small group meetings are marked down. The first and last lectures give the student 2 points, other lectures, small group meetings and theFirma information sessions are worth 1 point. In total, there are 30 points from presence.

Altogether these will give the students the maximum of 200 points. These points are evaluated in the following way:
Fail: 0-59 points — Fail
grade 1: 60 – 89 points
grade 2: 90 – 119 points
grade 3: 120 – 149 points
grade 4: 150 – 179 points
grade 5: 180 – 200 points.

Project work:
The students are required to work in the project a total of 81 hours.
- One working hour equals 1 point.
- Participating in one Tech Club session equals 2 points.
- Hosting one Tech Club session equals 5 points (this includes the time spent on planning the content and presenting).

The formative assessment of students' performance in the projects is based on self and peer assessment, customer’s feedback (if available) and project manager’s feedback.
All the required hours done equals to 81 points, every missing 15 hours means one grade point reduction of the grade student has received from the evaluation.

The final grade of the course is weighted average:
- Assignments, presence and the guest lecture report 70%
- Project work (evaluation based on self- and peer assessment) and project hours 30%

Accepted grade cannot be raised.

Assessment criteria, fail (0)

Less than 60 points from the theory part and project working hours not completed.

No show, not carrying out responsibilities, disappearing from team work, lack of communication with other team members.

Student has to pass assignments and project work to complete the course.

Assessment criteria, satisfactory (1-2)

Grade 1: under 90 points from the theory part and completing project work hours.
Grade 2: under 120 points from the theory part and completing project work hours.

Poor, but satisfactory performance both in independent work and team work. Low participation on lectures and other activities.

Student has to pass assignments and project work to complete the course.

Assessment criteria, good (3-4)

Grade 3: under 150 points from the theory part and completing project work hours.
Grade 4: under 180 points from the theory part and completing project work hours.

Good performance both in team work and independent work. Active participation on lectures and other activities.

Student has to pass assignments and project work to complete the course.

Assessment criteria, excellent (5)

Grade 5: 180 - 200 points from from the theory part and completing project work hours.

Excellent performance both in team work and independent work. Active participation on lectures and other activities.

Student has to pass assignments and project work to complete the course.

Objective

After having completed the course, the student:
• can describe different topical areas of ICT, their interfaces to other fields and application opportunities from different perspectives
• can identify channels to contribute to the development of the society in general and own professional field in particular
• is familiar with the ICT degree programme's competence tracks' contents, applications and business cooperation
• analyze one’s own competences, interests, strengths and development areas

Content

* getting familiar with competence tracks' contents, laboratories, and projects
* career planning and choosing one's preference for competence track

Materials

All materials are distributed through the learning environment (ItsLearning).

Teaching methods

Lectures and assignments.

Exam schedules

No exam.

Taking the course requires attendance and finishing the given tasks.
There is no chance for a retake after the course has ended.

International connections

The methods and assignment requirements for each path are announced in Itslearning

Completion alternatives

None.

Student workload

Weekly contact hours (twice per competence path) and the assignments for each path.
- Course introduction: 1 hour
- Learning paths: 4 x 2 x 3 hours = 24 hours

Independent work: approximately 35 hours

Total: approximately 60 hours

Assessment criteria, approved/failed

To achieve a passing grade, the following are required:
1. Participation in contact hours: 75% (7/9)
2. Successful completion of assignments according to the schedule: 75% of the tasks

There will be one assignment per each class.

Based on these, an average percentage is calculated, and if it is at least 75%, the course is passed.

Content scheduling

In the course, the learning paths are introduced according to the following weekly schedule:
3 + 4: DNCS - Data Networks and Cybersecurity
10 + 11: SWIS - Software Development and Information Systems
14 + 15: SEPM - Software Engineering and Project Management
16 + 17: DEAI - Data Engineering & AI

Further information

ItsLearning

Evaluation scale

Hyväksytty/Hylätty

Assessment methods and criteria

The course assessment is influenced by:
1. Participation in contact hours
2. Completion of assignments according to the schedule

Objective

After completing the course, the student

understand the basics of business and the role of ICT services through business development.

is familiar with the company's mission and vision, business models, operational processes and product marketing.

have experience in developing one's own business idea through tasks related to the above topics and present reflections related to the company's product distribution, digital marketing and ICT services as part of the business plan.

Content

Business planning

Business models

Business Processes

Software business and digital products

Digital Marketing

Evaluation scale

H-5

Objective

By the end of this course, students can:

understand the role of no-code and low-code tools in design processes.
design basic solutions in NCLC platforms.
collaborate in teams on an NCLC project.
consider NCLC implementation constraints and opportunities.
evaluate their NCLC-implemented projects.
reflect on the entire process.

Content

This course delves into the "Implement" and "Operate" stages of the CDIO framework using low-code/no-code tools. The students will complete a NCLC project including the following steps:
*Introduction to Software Development and No-Code/Low-Code Platforms
*Implementation with No-Code/Low-Code Platform
*Testing, Feedback, and Iteration

Materials

Material provided by the teacher and shared in learning environment (ITS).

Teaching methods

This course consists of:
- lectures: theoretical background and demonstrating the use of various low code/ no-code frameworks and environments that can be used for example in software / game / web development.
- practice sessions: practicing the use of LCNC tools introduced in lecture
- group work: students will complete a LCNC project where they use the tools and skills learned in lectures and practice

Exam schedules

-

International connections

In the course, skills are learned through exercises and practical project work.

Only electronic materials are used in the implementation.

Completion alternatives

-

Student workload

The student completes tasks related to the course, with an estimated workload:
- Theory lessons: 8 x 2h = 16h
- Practical lessons: 10 x 2h = 20h
- Group work: 2 x 2h = 4h
- Final event: 4h
- Other independent/group work: approximately 90h
TOTAL: approximately 135h

Content scheduling

This course delves into the "Implement" and "Operate" stages of the CDIO framework using Low-Code/No Code (LCNC) tools. The students will complete a LCNC project including the following steps:
- Introduction to Software Development and LCNC Platforms
- Implementation with LCNC Platform
- Testing, Feedback, and Iteration

The course is implemented during weeks 39 - 50 as follows:
- Theory, weeks 39 - 47: 2h contact session for 8 weeks
- Practice, weeks 39 - 49: 2h contact session with your own tutor
- Group work, weeks 48-49: 2h independent working
- Final event, week 50

Further information

Available in ITS.

Evaluation scale

H-5

Assessment methods and criteria

The course is assessed by
1. Active participation
2. Timely submission of practice tasks
3. The final LCNC project outcome

See details in Finnish.

Objective

By the end of this course, students can:

understand the role of no-code and low-code tools in design processes.
design basic solutions in NCLC platforms.
collaborate in teams on an NCLC project.
consider NCLC implementation constraints and opportunities.
evaluate their NCLC-implemented projects.
reflect on the entire process.

Content

This course delves into the "Implement" and "Operate" stages of the CDIO framework using low-code/no-code tools. The students will complete a NCLC project including the following steps:
*Introduction to Software Development and No-Code/Low-Code Platforms
*Implementation with No-Code/Low-Code Platform
*Testing, Feedback, and Iteration

Materials

Materiaali jaetaan oppimisympäristön (ITS) avulla.

Teaching methods

This course consists of:
- lectures: theoretical background and demonstrating the use of various low code/ no-code frameworks and environments that can be used for example in software / game / web development.
- practice sessions: practicing the use of LCNC tools introduced in lecture
- group work: students will complete a LCNC project where they use the tools and skills learned in lectures and practice

Exam schedules

-

International connections

In the course, skills are learned through exercises and practical project work.

Only electronic materials are used in the implementation.

Student workload

The student completes tasks related to the course, with an estimated workload:
- Theory lessons: 8 x 2h = 16h
- Practical lessons: 10 x 2h = 20h
- Group work: 2 x 2h = 4h
- Final event: 4h
- Other independent/group work: approximately 90h
TOTAL: approximately 135h

Content scheduling

This course delves into the "Implement" and "Operate" stages of the CDIO framework using Low-code/No-code (LCNC) tools. The students will complete a LCNC project including the following steps:
- Introduction to Software Development and LCNC Platforms
- Implementation with LCNC Platform
- Testing, Feedback, and Iteration

The course is implemented as follows:
- Theory: 2h contact session for 8 weeks
- Practice: 2h contact session with your own tutor
- Group work: 2h independent working
- Final event, week 50

Further information

ITS, email

Evaluation scale

H-5

Assessment methods and criteria

The course is assessed by
- active participation and timely submission of practice tasks
- the final LCNC project outcome

Additional information about the assessment is provided in the first lecture.

Assessment criteria, fail (0)

The student fails the course if he/she fails
- to attend enough contact sessions (lecture, practice, group work)
- to complete enough practice tasks (less than 50%)

LCNC-project contribution and result:
No active participation in the LCNC project OR there is no product to present in the final session.

Assessment criteria, satisfactory (1-2)

Participation: Lectures and practice sessions are mostly attended at the rate greater than 75%
Weekly practice tasks: 50% - 70% are submitted in time

LCNC-project contribution and result:
Based on peer-reviews, the student is contributing less than other group members AND the project was presented in the final session.

Assessment criteria, good (3-4)

Participation: Lectures and practice sessions are attended at the rate greater than 85%
Weekly practice tasks: 70% - 95% are submitted in time

LCNC-project contribution and result: Active contribution in project work and is at least at average level based on peer-reviews AND the project was presented in the final session.

Assessment criteria, excellent (5)

Participation: Lectures and practice sessions are attended at the rate greater than 90%
Weekly practice tasks: at least 95% of the tasks are submitted in time

LCNC-project contribution and result:
* Active contribution in project work and is above average level based on peer-reviews AND
* The project was presented in the final session AND
* The student showed innovative ways of using LCNC tools, in addition to technical and project management skills.

Objective

After completing the study unit the student can:
- implement or participate in project where new technology is utilized for business development
- utilize latest technologies and environments in the project (such as artificial intelligence, machine learning, internet of things and cloud services)

Teaching methods

Lectures
Assignments
Practical Work / Final Report

Completion alternatives

Students have the opportunity to propose their own target (their own internship, workplace, their own innovation, and entrepreneurship) or participate in selected projects, or take a relevant course elsewhere.

Content scheduling

The course content can be customized to meet the needs of individual students or student groups.

Where possible, the course implementation is aimed at integrating it into an innovation project, public organizations, private companies, or Turku University of Applied Sciences' own research and development projects. The goal is for the student, either individually or in a group, to participate in a development project where they have the opportunity to familiarize themselves with new technologies, digital entrepreneurship, or otherwise assist in practical tasks related to business and product development.

Students have the opportunity to propose their own target (their own internship, workplace, their own innovation, and entrepreneurship) or participate in selected projects or explore a current topic of interest as part of another course.

During the course meetings, topics such as artificial intelligence and its application in business, the search for innovations, and digital entrepreneurship will be discussed. The content will be specified as the course progresses, and meetings can also be separately arranged guidance sessions.

Evaluation scale

H-5

Assessment methods and criteria

The assessment is based on assignments and the final report as well as the student's own activity. The possible client's statement carries significant weight (>50%).

Assessment criteria, fail (0)

The report does not meet the needs of the client or contains insufficient analysis.
The argumentation is weak or inadequate, failing to present clear solutions for business development.
The structure of the report is unclear and confused.
The language and expression are unclear or erroneous, making the report difficult to understand.

Assessment criteria, satisfactory (1-2)

The report partially addresses the assignment but offers only superficial solutions to business issues.
Analysis and argumentation are partly present, but they are not deep or convincing.
The report does not present concrete actions or innovative solutions.
The report mentions concrete actions, operations, or measures, but their implementation or impact on the business remains unclear or is entirely missing.

Assessment criteria, good (3-4)

The report provides a reasonably in-depth analysis and presents clear solutions to the challenges related to the assignment.
The argumentation is clear and well-founded, and the report introduces some innovative perspectives.
The report includes concrete actions and plans for business development.
The report mentions developed, implemented, or tested actions, operations, or measures and their impact on the business, but a more in-depth evaluation of these may be lacking.

Assessment criteria, excellent (5)

The report provides excellent in-depth analysis and innovative solutions to the challenges related to the assignment.
The argumentation is extremely convincing and well-founded, presenting clear and new perspectives.
The report includes concrete and practical measures for business development, and their impacts are clearly reported.
The report contains clear plans and implementations for business development, and their impacts are thoroughly evaluated.
The report presents innovative practical implementations and their impacts on the business, demonstrating concrete results and improvements.
The references convincingly support the argumentation of the report.

Materials

Learning material consists of material produced by the Lecturer as well as extra material obtainable from TUAS resources (ebooks).

Teaching methods

Learning is achieved through contact lectures, written home assignments and laboratory assignments.

Student workload

There will be roughly 3 written home assignments and 7 laboratory assignments, each valued on average at 10 points.

Contact sessions and independent studying adds up to 135 hours (5 CU) of work.

Content scheduling

The course consists of contact lectures and contact laboratory sessions. There will be 9 lectures, which will cover theory and technologies behind Network Security. In 5 laboratory sessions, students will practice performing security controls in simulated enterprise networks through laboratory assignments.

The course will begin on week 2/2025 and end by week 18/2025.

Evaluation scale

H-5

Assessment methods and criteria

Course grading will be based on home assignments and laboratory assignments.

On-site course attendance is required minimum 50% to pass the course. Exceptions are to be agreed with the Lecturer individually.

Maximum points score (excl. bonus labs) for the course is 100p, and grading is as follows:

49 and less = Failed
50-59 pts = 1
60-69 pts = 2
70-79 pts = 3
80-89 pts = 4
90 and more = 5

Objective

After completing the course the student can:
- describe different software development methodologies and evaluate their feasibility to software projects

- describe software development project phases

- understand and draw UML diagrams

- understand the importance of specification and planning to software development

- work in different phases of a software project

Content

- Software development project

- Specification, planning, programming, testing, deployment, and maintenance of a software project

- UML modelling

Materials

Erikseen jaettavat ja linkatut lähiopetuskertojen materiaalit, ohjelmistotyökalut, dokumentointipohjat.

Teaching methods

Opintojaksolla harjoitellaan toimimista ohjelmistoprojektin eri vaiheissa.
Harjoitustyönä toteutetaan ryhmässä hallittu ohjelmistoprojekti alusta loppuun: Ohjelmiston määrittely, suunnittelu, ohjelmointi, testaus, ja käyttöönotto.

Exam schedules

ei tenttiä

International connections

Harjoitustyö toteutetaan ryhmässä. Projektissa noudatetaan SCRUM menetelmän periaatteita. Viikoittaisilla opetuskerroilla tehdään ohjelmistuotantoon liittyviä pienempiä tehtäviä, jotka palvelevat harjoitustyön tekemisen osia sekä edistetään omaa harjoitustyöprojektia. Kestävän kehityksen näkökulmaa voidaan edistää harjoitustyöprojektin aiheisiin liittyvissä suunnittelu- ja toteutusratkaisuissa.

Completion alternatives

(Katso tutkintosääntö.)

Student workload

Opintojakson laajuus on 5 opintopistettä.
Työmäärä on 5 * 27 tuntia eli 135 tuntia.
Opintojakson kesto on noin 14 viikkoa.
Viikottainen työmäärä on noin 10 tuntia, josta
viikkoharjoitukset noin 3 tuntia ja oma harjoitustyönä oleva
ohjelmistoprojekti noin 7 tuntia.

Content scheduling

Opintojakson suoritettuaan opiskelija :
- tuntee ohjelmistoprojektimalleja ja arvioida niiden soveltuvuutta ohjelmistoprojekteihin
- osaa toimia ohjelmistoprojektin eri vaiheissa ja rooleissa
- ymmärtää määrittelyn, suunnittelun ja testauksen merkityksen ohjelmistoprojektissa
- osaa laatia ohjelmistotuotannossa käytettyjä suunnittelukaavioita (UML), käyttöliittymäprototyyppejä ja käyttää versionhallintaa yhteistyössä muiden kanssa
- käyttää ketterää kehitystä tukevaa työkalua ohjelmistoprojektin ja vaatimustenhallintaan

Further information

Osallistumisen edellytyksenä ovat perustaidot jostakin ohjelmointikielestä. Kurssilla toteutetaan ryhmätyönä ohjelmisto, mutta ohjelmoinnin opetus ei ole varsinaisesti kurssin sisältöä.

Evaluation scale

H-5

Assessment methods and criteria

Harjoitustyö muodostaa 60 % arvosanasta. Opintojaksosta läpipääsy edellyttää hyväksytysti suoritettua harjoitustyötä
Aktiivinen osallistuminen lähiopetukseen muodostaa 40 % arvosanasta
Harjoitustyön välipalautukset (sprinttien tuotokset) arvostellaan pistein 0-2, jossa:
2 pistettä: Erittäin hyvä (tehtävät palautettu ajoissa, tehty tehtävänannon mukaisesti, ei puutteita)
1 piste: Hyväksytty (tehtävät palautettu ajoissa, tehty tehtävänannon mukaisesti, pienehköjä puutteita)
0 pistettä: Hylätty (tehtäviä ei palautettu ollenkaan, osa tehtävistä puuttuu, tehtävät palautettu myöhässä tai tehtävät ovat erittäin puutteellisia)
Lähiopetukseen osallistumisesta saa 1 pisteen / kerta. Pisteen saaminen edellyttää aktiivista osallistumista ja tehtävien tekemistä lähiopetustunteihin liittyen. Niinä kertoina, kun erillistä oppituntitehtävää ei ole, pisteen saa läsnäolosta. Läsnäolot kerätään joka oppitunnilla, mutta ainoastaan tehtävittöminä oppituntikertoina niistä saa suoraan pisteen.
Kurssin arvosana muodostuu seuraavasti:
Harjoitustyön pistemäärä = arvosana: 0-5p. = 0, 6-8p. = 1, 9-10p. = 2, 11-12p. = 3
Lähiopetuksen pistemäärä = arvosana lisäys: 0-6p. = +0; 7-11p. = +1; 12p. tai enemmän = +2 harjoitustyön arvosanaan. Jos jostain syystä yhteenlasketut maksimipistemäärät poikkeavat yllä olevista, kertyneet pisteet skaalataan yllä ilmoitetulle välille lineaarisesti.

Assessment criteria, fail (0)

Opiskelija ei tiedä, miten opintojakson tietämystä sovelletaan eikä osaa käyttää opittaviksi asetettuja metodeja arviointikriteerin 1-2 täyttävästi.

Assessment criteria, satisfactory (1-2)

Opiskelija tuntee ohjelmistotuotannon peruskonseptin ja tuntee yleisimpiä suunnittelumenetelmiä siihen liittyen. Opiskelija osaa soveltaa hankkimaansa tietämystä aiheesta ja osaa käyttää metodeja yksinkertaisessa kontekstissa. Opiskelija saavuttaa arvosanan alarajaksi määritellyt pistemäärät kurssin tehtävissä ja aktiviteeteissa.

Assessment criteria, good (3-4)

Opiskelija tuntee ohjelmistotuotannon peruskonseptin keskeisimpiä yksityiskohtia myöten ja tuntee yleisimmät suunnittelumenetelmät siihen liittyen. Opiskelija osaa soveltaa hankkimaansa tietämystä aiheesta ja osaa käyttää metodeja tehtäväksi annetuissa konteksteissa. Opiskelija saavuttaa arvosanan alarajaksi määritellyt pistemäärät kurssin tehtävissä ja aktiviteeteissa.

Assessment criteria, excellent (5)

Opiskelija tuntee ohjelmistotuotannon peruskonseptin keskeisimpiä yksityiskohtia myöten ja tuntee yleisimmät suunnittelumenetelmät siihen liittyen suvereenisti. Opiskelija osaa soveltaa hankkimaansa tietämystä aiheesta ja osaa käyttää metodeja tehtäväksi missä tahansa annetussa kontekstissa. Opiskelija saavuttaa arvosanan alarajaksi määritellyt pistemäärät kurssin tehtävissä ja aktiviteeteissa.

Objective

After completing the course, the student will be able to:
describe the program flow on a diagram
discuss the choice of an applicable solution model
define basic concepts related to programming such as source code, compiler, variable and type
read and understand the finished program code
utilize selection and loop structures
use exception handling mechanisms
design and implement simple application programs, and document and test them

Content

structure of software application
different development environments
reception of input data required in the program
processing of data on the program in order to solve the given problem
presentation of result
data variables and data types, simple data structures
functions and parameters
conditional clauses and loops
exceptions
file handling
testing and documentation of the program

Materials

Recommended literature:
Python Basics: A Practical Introduction to Python3 4th edition by David Amos,Dan Bader,Joanna Jablonski, Fletcher Heisler, ISBN:9781775093329 (paperback), ISBN:9781775093336 (electronic)

AI-Assisted Programming by Tom Taulli, Released April 2024, Publisher(s): O'Reilly Media, Inc., ISBN: 9781098164560

Programming tools
- Python 3.12.x
- Visual Studio Code (with Extensions)
- Git / Github
- Github Copilot or other AI programming assistants

Teaching methods

Learning by programming
Learning efficient and responsible use of AI programming copilots
Theory and practical examples shared during lectures
Practical understanding gained in practice sessions

Exam schedules

No exam

International connections

Students will learn theoretical concepts and gain programming good practices in lectures.
Students will put their understanding into practice in weekly assignments.

Course material will be entirely digital.

Student workload

Contact hours 14x2h (theory) + 12x2h (practice) = 52h
Independent study continuously throughout the course 80h

TOTAL approx. 130h

Theory lectures are held onsite.
Programming labs in subgroups A, B and C are also onsite.

Content scheduling

TOPICS / CONTENTS
week 3: Introduction
week 4: Built-in functions print and input, variables and types
week 5: Arithmetic and bitwise operators
week 6: Branching
week 7: Loops
week 8: Winter Break - no teaching
week 9: Breaking loops
week 10: Collections
week 11: Collections and loops
week 12: Functions
week 13: Function parameters and return values
week 14: Modularity and unit testing
week 15: GUI, events and callback functions
week 16: File IO (text and binary files)
week 17-: Basics of exception handling, try-except-finally, with

Further information

ItsLearning
Email

Evaluation scale

H-5

Assessment methods and criteria

The course consists of 10 weekly assignment series, which include studying programming theory and completing related programming tasks. Students earn points by demonstrating their work to the instructor.

During the course, students complete a project in which they create a more extensive application (applying the knowledge they have gained during the course and innovatively utilizing AI programming copilots). This project allows students to apply what they have learned and demonstrate their skills.

Each weekly assignment can earn a maximum of 10 points. The overall assessment of the weekly assignment series follows the following scale:

40 points -> grade 1
55 points -> grade 2
70 points -> grade 3
80 points -> grade 4
90 points -> grade 5
The project is assessed separately on a scale of 1-5.

The student's course grade is the average of the weekly assignments and the project. Both components must be passed.

If, during the course, it appears that a student will not achieve the required points to pass, they will be given the opportunity to complete missing assignments retroactively. For these assignments, they can earn up to half of the available points. The student must collect the required points before the course end date.

Assessment criteria, fail (0)

Student
• does not know the basic concepts of programming
• cannot read or write simple programs

Assessment criteria, satisfactory (1-2)

Student
• knows such basic concepts of programming as variables, control structures and functions
• understands how the program flow is going
• can read or write simple programs

Assessment criteria, good (3-4)

Student
• knows such basic concepts of programming as variables, control structures, functions, classes, objects and arrays
• understands how the program flow is going and can find easy errors
• can make simple programs

Assessment criteria, excellent (5)

Student
• knows such basic concepts of programming as variables, control structures and functions, classes and objects
• utilizes some data Structures such as arrays, lists and hash tables.
• understands how the program flow is going and can use debugger and error-handling
• can make programs that include aforesaid concepts.

Objective

After completing the course, the student will be able to:
describe the program flow on a diagram
discuss the choice of an applicable solution model
define basic concepts related to programming such as source code, compiler, variable and type
read and understand the finished program code
utilize selection and loop structures
use exception handling mechanisms
design and implement simple application programs, and document and test them

Content

structure of software application
different development environments
reception of input data required in the program
processing of data on the program in order to solve the given problem
presentation of result
data variables and data types, simple data structures
functions and parameters
conditional clauses and loops
exceptions
file handling
testing and documentation of the program

Materials

Suositeltu kirjallisuus:
"Python Basics: A Practical Introduction to Python3", 4. painos, David Amos, Dan Bader, Joanna Jablonski, Fletcher Heisler, ISBN:9781775093329 (paperback), ISBN:9781775093336 (sähköinen)

"AI-Assisted Programming" by Tom Taulli, Julkaistu huhtikuussa 2024, Kustantaja: O'Reilly Media, Inc., ISBN: 9781098164560

Ohjelmointityökalut:
Python 3.12.x
Visual Studio Code (laajennuksilla)
Git / Github
Github Copilot tai muu AI-ohjelmointiassistentti

Teaching methods

Oppiminen ohjelmoimalla
Tehokkaan ja vastuullisen AI-ohjelmointiassistenttien käytön oppiminen
Teoriaa ja käytännön esimerkkejä jaetaan luentojen aikana
Käytännön ymmärrystä hankitaan harjoitustunneilla

Exam schedules

Ei tenttiä

International connections

Opiskelijat oppivat teoreettisia käsitteitä ja saavat ohjelmoinnin parhaita käytäntöjä luennoilla.
Opiskelijat soveltavat ymmärrystään käytäntöön viikkotehtävissä.

Kurssimateriaali on täysin digitaalinen.

Student workload

Kontaktiopetustunnit 14x2h (teoria) + 12x2h (harjoitukset) = 52h
Itseopiskelu jatkuvasti kurssin ajan 80h

YHTEENSÄ noin 130h

Teorialuennot pidetään lähiopetuksena.
Ohjelmointiharjoitukset alaryhmissä A, B ja C ovat myös lähiopetuksena.

Content scheduling

AIHEET / SISÄLLÖT
viikko 3: Johdanto
viikko 4: Sisäänrakennetut funktiot print ja input, muuttujat ja tyypit
viikko 5: Aritmeettiset ja bittitason operaattorit
viikko 6: Haarautuminen
viikko 7: Toistorakenteet
viikko 8: Talviloma - ei opetusta
viikko 9: Toistorakenteiden katkaisu
viikko 10: Kokoelmat
viikko 11: Kokoelmat ja toistorakenteet
viikko 12: Funktiot
viikko 13: Funktion parametrit ja paluuarvot
viikko 14: Modularisuus ja yksikkötestaus
viikko 15: Käyttöliittymä, tapahtumat ja callback-funktiot
viikko 16: Tiedostojen käsittely (teksti- ja binääritiedostot)
viikko 17-: Poikkeusten käsittelyn perusteet, try-except-finally, with

Further information

ItsLearning
Sähköposti

Evaluation scale

H-5

Assessment methods and criteria

urssi sisältää 10 viikkotehtäväsarjaa, joihin sisältyy ohjelmoinnin teorian opiskelua ja aiheeseen liittyvien ohjelmointitehtävien suorittamista. Opiskelija saa pisteitä opettajalle demonstroimalla tehtäviä.

Kurssilla tehdään harjoitustyö, jossa opiskelija luo laajemman sovelluksen (soveltaen kurssin aikana opittuja tietoja ja hyödyntäen innovatiivisesti AI-ohjelmointiassistentteja). Tämä harjoitustyö antaa opiskelijalle mahdollisuuden soveltaa oppimaansa ja osoittaa osaamisensa.

Jokaisesta viikkotehtävästä voi saada maksimissaan 10 pistettä. Viikkotehtäväsarjan kokonaisarviointi noudattaa seuraavaa kaavaa:

40 pistettä -> arvosana 1
55 pistettä -> arvosana 2
70 pistettä -> arvosana 3
80 pistettä -> arvosana 4
90 pistettä -> arvosana 5
Harjoitustyö arvioidaan erikseen asteikolla 1-5.

Opiskelijan kurssiarvosana muodostuu viikkotehtävien ja harjoitustyön keskiarvosta. Molemmat osiot täytyy olla hyväksyttyjä.

Jos kurssin edetessä näyttää siltä, että opiskelija ei saavuta läpäisyyn vaadittavaa pistemäärää, hänelle annetaan mahdollisuus suorittaa puuttuvat tehtävät takautuvasti. Näistä tehtävistä hän voi saada maksimissaan puolet tarjolla olevista pisteistä. Opiskelijan tulee kerätä vaadittavat pisteet ennen kurssin päättymispäivää.

Assessment criteria, fail (0)

Opiskelija
• ei tunne ohjelmoinnin peruskäsitteitä
• ei pysty lukemaan tai kirjoittamaan yksinkertaisia ohjelmia

Assessment criteria, satisfactory (1-2)

Opiskelija
• tuntee ohjelmoinnin peruskäsitteet, kuten muuttujat, kontrollirakenteet ja funktiot
• ymmärtää ohjelman kulun
• osaa lukea ja kirjoittaa yksinkertaisia ohjelmia

Assessment criteria, good (3-4)

Opiskelija
• tuntee ohjelmoinnin peruskäsitteet, kuten muuttujat, kontrollirakenteet, funktiot, luokat, oliot ja taulukot
• ymmärtää ohjelman kulun ja löytää yksinkertaiset virheet
• osaa tehdä yksinkertaisia ohjelmia

Assessment criteria, excellent (5)

Opiskelija
• tuntee ohjelmoinnin peruskäsitteet, kuten muuttujat, kontrollirakenteet, funktiot, luokat ja oliot
• hyödyntää joitain tietorakenteita, kuten taulukot, listat ja hajautustaulut
• ymmärtää ohjelman kulun ja osaa käyttää debuggeria ja virheenkäsittelyä
• osaa tehdä ohjelmia, jotka sisältävät edellä mainittuja käsitteitä.

Objective

After completing the course, the student will be able to:
describe the program flow on a diagram
discuss the choice of an applicable solution model
define basic concepts related to programming such as source code, compiler, variable and type
read and understand the finished program code
utilize selection and loop structures
use exception handling mechanisms
design and implement simple application programs, and document and test them

Content

structure of software application
different development environments
reception of input data required in the program
processing of data on the program in order to solve the given problem
presentation of result
data variables and data types, simple data structures
functions and parameters
conditional clauses and loops
exceptions
file handling
testing and documentation of the program

Materials

Suositeltu kirjallisuus:
"Python Basics: A Practical Introduction to Python3", 4. painos, David Amos, Dan Bader, Joanna Jablonski, Fletcher Heisler, ISBN:9781775093329 (paperback), ISBN:9781775093336 (sähköinen)

"AI-Assisted Programming" by Tom Taulli, Julkaistu huhtikuussa 2024, Kustantaja: O'Reilly Media, Inc., ISBN: 9781098164560

Ohjelmointityökalut:
Python 3.12.x
Visual Studio Code (laajennuksilla) tai jokin vastaava

Teaching methods

Keskeisin oppimismenetelmä on ohjelmointitehtävien ratkaisemisen yrittäminen käytännössä, virheiden tekeminen ja niiden korjaamisen harjoittelu sekä toisinaan valmiin ratkaisun tai osaratkaisun hyödyntäminen. Tehokkaan ja vastuullisen AI-ohjelmointiassistenttien käytön pohtiminen ja oppiminen. Ohjelmoinnin asioista keskusteleminen.

Exam schedules

Ei tenttiä

International connections

Luentotunnilla keskitytään uusien asioiden sisäistämiseen. Opittua sovelletaan itse oman algoritmisen ongelmanratkaisuharjoittelun osasina ja keinoina. Harjoitustuntia ennen harjoitellaan tehtävien avulla ja harjoitustunnilla on mahdollista saada ohjausta ja tukea, jotta osaaminen täydentyy ja kehittyy mahdollisimman suotuisasti.

Kurssimateriaali on sähköisenä annetuilla alustoilla. Kestävää kehitystä pohditaan yleisen ohjelmointiin liittyvän kestävyyden, esim. koodin uudelleenkäytön alueella.

Student workload

Kontaktiopetustunnit 14x2h (teoria) + 12x2h (harjoitukset) = 52h
Itseopiskelu jatkuvasti kurssin ajan 80h

YHTEENSÄ noin 130h

Teorialuennot pidetään lähiopetuksena.
Ohjelmointiharjoitukset alaryhmissä D, E ja F ovat myös lähiopetuksena.

Content scheduling

Opintojaksolla opetellaan käytännön ohjelmointitaito. Tässä keskeisimpänä asiana on algoritminen ajattelu ja algoritminen ongelmanratkaisutaito. Taidon oppiminen edellyttää tietämystä ohjelmointiin liittyvistä perusasioista, joista opiskellaan esimerkiksi muuttujat, tyypit, operaattorit, kontrollirakenteet, aliohjelmat (funktiot), parametrit, modulaarisuus, testaus, syöttö näppäimistöltä, tulostus ruudulle, listat, kokoelmat, poikkeukset ja tiedostonkäsittely. Opetuksen apuvälineenä on Python-kieli. Opintojaksolla käytetään spiraalioppimista, mikä on tavallista ohjelmoinnin opettelussa, joten aiheiden käsittelyn tarkkaa ajoitusta ei ole mahdollista antaa viikkotasolla.

Further information

ItsLearning
Sähköposti

Evaluation scale

H-5

Assessment methods and criteria

Opintojakso sisältää 10 viikkotehtäväsarjaa, joihin sisältyy ohjelmoinnin teorian opiskelua ja aiheeseen liittyvien ohjelmointitehtävien suorittamista. Opiskelija saa pisteitä opettajalle demonstroimalla eli esittämällä tehtyjä tehtäviä tai toisinaan osittain tehtyjä ja kattavasti yritettyjä tehtäviä.

Opintojaksoon sisältyy harjoitustyö, jossa opiskelija luo laajemman sovelluksen (soveltaen kurssin aikana opittuja tietoja ja mahdollisesti jopa osittain hyödyntäen innovatiivisesti AI-ohjelmointiassistentteja). Tämä harjoitustyö antaa opiskelijalle mahdollisuuden soveltaa oppimaansa ja osoittaa osaamistaan.

Jokaisesta viikkotehtävästä voi saada maksimissaan 10 pistettä. Viikkotehtäväsarjan kokonaisarviointi noudattaa seuraavaa kaavaa:

40 pistettä -> arvosana 1
55 pistettä -> arvosana 2
70 pistettä -> arvosana 3
80 pistettä -> arvosana 4
90 pistettä -> arvosana 5
Harjoitustyö arvioidaan erikseen asteikolla 1-5.

Opiskelijan kurssiarvosana muodostuu viikkotehtävien ja harjoitustyön keskiarvosta. Molemmat osiot täytyy olla hyväksyttyjä.

Jos kurssin edetessä näyttää siltä, että opiskelija ei saavuta läpäisyyn vaadittavaa pistemäärää, hänelle annetaan mahdollisuus suorittaa puuttuvat tehtävät takautuvasti. Näistä tehtävistä hän voi saada maksimissaan puolet tarjolla olevista pisteistä. Opiskelijan tulee kerätä vaadittavat pisteet ennen kurssin päättymispäivää.

Assessment criteria, fail (0)

Opiskelija
• ei tunne ohjelmoinnin peruskäsitteitä
• ei pysty lukemaan tai kirjoittamaan yksinkertaisia ohjelmia

Assessment criteria, satisfactory (1-2)

Opiskelija
• tuntee ohjelmoinnin peruskäsitteet, kuten muuttujat, kontrollirakenteet ja funktiot
• ymmärtää ohjelman kulun
• osaa lukea ja kirjoittaa yksinkertaisia ohjelmia ja testejä

Assessment criteria, good (3-4)

Opiskelija
• tuntee ohjelmoinnin peruskäsitteet, kuten muuttujat, kontrollirakenteet, funktiot, luokat, oliot ja listat
• ymmärtää ohjelman kulun ja löytää yksinkertaiset virheet
• osaa laatia yksinkertaisia ohjelmia ja testata niiden toimivuutta

Assessment criteria, excellent (5)

Opiskelija
• tuntee ohjelmoinnin peruskäsitteet, kuten muuttujat, kontrollirakenteet, funktiot, luokat, oliot ja listat
• hyödyntää joitain tietorakenteita, kuten taulukot, listat ja hajautustaulut
• ymmärtää ohjelman kulun ja osaa käyttää debuggeria ja virheenkäsittelyä
• osaa laatia ohjelmia, jotka sisältävät edellä mainittuja käsitteitä ja osoittaa toimivuuden testaamalla

Objective

After completing the course, the student will be able to:
describe the program flow on a diagram
discuss the choice of an applicable solution model
define basic concepts related to programming such as source code, compiler, variable and type
read and understand the finished program code
utilize selection and loop structures
use exception handling mechanisms
design and implement simple application programs, and document and test them

Content

structure of software application
different development environments
reception of input data required in the program
processing of data on the program in order to solve the given problem
presentation of result
data variables and data types, simple data structures
functions and parameters
conditional clauses and loops
exceptions
file handling
testing and documentation of the program

Materials

Kaikki oppimateriaali ilmoitetaan itslearningissä.

Teaching methods

Materiaalin lukeminen ja ohjelmointiharjoitusten tekeminen ohjatusti sekä itsenäisesti.

Harjoitustyö opettaa soveltamaan opittuja asioita laajemman sovelluksen näkökulmasta.

Materiaali pitää sisällään olio-ohjelmoinnin teoriaa sekä teoriaa valaisevia kuvia ja esimerkkikoodeja.

Exam schedules

Opintojaksolla ei ole tenttiä.

International connections

Itsearviointi
Opiskelijan tulee kiinnittää säännöllisesti huomiota omaan opiskeluunsa ja oppimiseensa.
Opiskelijaa pyydetään tekemään muistiinpanoja jokaisen viikkotehtäväsarjan kohdalla pohtien omaa oppimistaan ja edistymistään.

Toteutuksella noudatetaan jatkuvan tekemisen ja arvioinnin mallia siten, että opiskelijan tulee tehdä, palauttaa ja demota kurssin tehtäviä säännöllisesti noudattaen kurssin tehtäville annettuja aikatauluja. Tällä tähdätään opiskelijan mahdollisuuteen seurata itsenäisesti omaa edistymistään ja oppimistaan kurssin aikana.

Jatkuva aktiivinen työskentely ja oppiminen tukevat ohjelmoinnissa esiintyvää spiraalioppimisen mallia.

Completion alternatives

1) Antamalla näytön esim. tekemästään työelämän projektista, jolla opiskelija osoittaa hallitsevansa opintojakson sisällön.
2) Sivustolla mooc.fi kuvataan ohjelmoinnin MOOC, joka vastaa sisällöltään Helsingin yliopiston tietojenkäsittelytieteen laitoksen kursseja Ohjelmoinnin perusteet ja Ohjelmoinnin jatkokurssi. Kurssit vastaavat yhteensä kymmentä opintopistettä (5+5).
Ohjelmoinnin perusteet voi suorittaa tekemällä ohjelmoinnin MOOCin alkuosan.

Student workload

Opiskelijan työn mitoitus

5 opintopistettä: 27 * 5 = 135 tuntia

Opintojakson kesto: 13.1. - 30.4.2025 (14 viikkoa + opetukseton viikko 8)

Viikkotyömäärä: 135 tuntia / 14 viikkoa = 9,5 tuntia viikossa

Content scheduling

Opintojaksolla opiskellaan ohjelmoinnin ajattelutapaa sekä tutustutaan ohjelmistokehityksen pääpiirteisiin.

Opintojakson harjoitukset tehdään Pythonilla.

Tulostaminen näytölle ja tiedostoon.
Tiedon lukeminen näppäimistöltä ja tiedostosta.
Ohjelmoinnin valintarakenteet.
Ohjelmoinnin toistorakenteet.
Aliohjelmat ja niiden käyttö.
Poikkeukset.
Listat ja merkkijonot.
Ohjelman dokumentointi.
Ohjelman testaus.

Further information

Kurssin viestintä tapahtuu pääsääntöisesti itslearningissä.

Evaluation scale

H-5

Assessment methods and criteria

Opintojakso pitää sisällään 10 viikkotehtäväsarjaa, joihin sisältyy ohjelmoinnin teorian opiskelua sekä aiheeseen liittyviä ohjelmointitehtäviä. Opiskelija saa pisteitä opettajalle demoamistaan tehtävistä.

Kurssilla tehdään harjoitustyö, jossa opiskelija tekee laajemman sovelluksen. Harjoitustyössä opiskelija pääsee soveltamaan oppimiaan asioita ja osoittamaan osaamistaan. Harjoitustyön tuotos demotaan opiskelijaryhmälle.

Viikkotehtävistä saa kustakin maksimissaan 10 pistettä. Kotitehtäväsarjan kokonaisarviointi noudattaa seuraavaa kaavaa:
40 pistettä -> 1
55 pistettä -> 2
70 pistettä -> 3
80 pistettä -> 4
90 pistettä -> 5

Harjoitustyö arvioidaan erikseen skaalalla 1-5.

Opiskelijan opintojakson arvosana muodostuu kotitehtäväsarjan ja harjoitustyön keskiarvosta. Molempien osioiden pitää olla hyväksyttyjä.

Jos kurssin edetessä näyttää siltä, että opiskelija ei saavuta kurssin läpäisyyn tarvittavaa pistemäärää, niin hänen kanssaan sovitaan puuttuvien tehtävien tekemisestä takautuvasti. Näistä tehtävistä hän voi saada maksimissaan puolet tarjolla olevasta pistemäärästä. Opiskelijan tulee kerätä läpäisyyn vaadittava pistemäärä ennen kurssin loppumispäivämäärää.

Assessment criteria, fail (0)

Hyväksytysti suoritettuja harjoituksia puuttuu ja/tai harjoitustyö puuttuu.

Assessment criteria, satisfactory (1-2)

Suomeksi
Hyväksytysti suoritettuja harjoituksia on tehty minimimäärä tai niiden laatu on tyydyttävä ja/tai harjoitustyön laatu on tyydyttävää tasoa.

Assessment criteria, good (3-4)

Hyväksytysti suoritettuja harjoituksia on tehty riittävästi ja niiden laatu on hyvää ja harjoitustyön laatu on hyvää tasoa.

Assessment criteria, excellent (5)

Hyväksytysti suoritettuja harjoituksia on tehty 90 % ja niiden laatu on kiitettävää ja harjoitustyön laatu on kiitettävää tasoa.

Objective

After completing the course the student can:
- explain the different phases and main methods of the development of a software product
- explain the concepts class hierarchy, inheritance, and polymorphism, and utilize these in software development
- discuss the possibilities of parallel programming
- apply exception handling mechanisms
- design, implement and document a simple user interface application.

Content

- object-oriented structures
- error handling with exceptions
- threads
- database connection
- software development process and methods
- UML diagrams

Materials

Opintojakson työtilassa itslearningissa.

Teaching methods

Materiaalin lukeminen ja ohjelmointiharjoitusten tekeminen ohjatusti sekä itsenäisesti.

Harjoitustyö opettaa soveltamaan opittuja asioita laajemman sovelluksen näkökulmasta.

Materiaali pitää sisällään olio-ohjelmoinnin teoriaa sekä teoriaa valaisevia kuvia ja esimerkkikoodeja.

Exam schedules

Opintojaksolla ei ole tenttiä.

International connections

Itsearviointi
Opiskelijan tulee kiinnittää säännöllisesti huomiota omaan opiskeluunsa ja oppimiseensa.
Opiskelijaa pyydetään tekemään muistiinpanoja jokaisen viikkotehtäväsarjan kohdalla pohtien ainakin seuraavia asioita
* Tehtävän teema: Mitä tiesin teemasta etukäteen? Mitä ajatuksia teema herätti?
* Tehtävää tehdessä: Esiin nousseet ajatukset kohdistuen tehtävien vaativuustasoon ja mielenkiintoon
* Tehtävän jälkeen: Mitä opin?
sekä antamaan itselleen arvosana työskentelystään jokaisen viikkotehtäväsarjan kohdalla asteikolla: Erinomainen - Hyvä - Tyydyttävä.

Toteutuksella noudatetaan jatkuvan tekemisen ja arvioinnin mallia siten, että opiskelijan tulee tehdä, palauttaa ja demota kurssin tehtäviä säännöllisesti noudattaen kurssin tehtäville annettuja aikatauluja. Tällä tähdätään opiskelijan mahdollisuuteen seurata itsenäisesti omaa edistymistään ja oppimistaan kurssin aikana.

Jatkuva aktiivinen työskentely ja oppiminen tukevat ohjelmoinnissa esiintyvää spiraalioppimisen mallia.

Completion alternatives

1) Antamalla näytön esim. tekemästään työelämän projektista, jolla opiskelija osoittaa hallitsevansa opintojakson sisällön.
2) Sivustolla mooc.fi kuvataan ohjelmoinnin MOOC, joka vastaa sisällöltään Helsingin yliopiston tietojenkäsittelytieteen laitoksen kursseja Ohjelmoinnin perusteet ja Ohjelmoinnin jatkokurssi. Kurssit vastaavat yhteensä kymmentä opintopistettä (5+5).
Olio-ohjelmoinnin voi suorittaa tekemällä ohjelmoinnin MOOCin Java-kielisen loppuosan.

Toteutuksen valinnaisista suoritustavoista pitää aina keskustella kurssin opettajan kanssa heti toteutuksen alkaessa.

Student workload

Opintojakson suoritus edellyttää hyväksytysti suoritettujen harjoitustehtäväsarjojen tekemistä ja palauttamista määräaikaan mennessä. Lisäksi opiskelijan tulee esitellä tekemänsä tehtäväsarjat kurssin demotuntien aikana.

5 opintopistettä: 27 * 5 = 135 tuntia

Opintojakso ajoittuu aikavälille 1.9.2024 - 16.12.2024

Viikkotyömäärä: 135 tuntia / 13 viikkoa = 10,4 tuntia viikossa

Kurssilla annetaan kontaktiopetusta 5h viikkotasolla. Tämä koostuu luento-osiosta (3 h) ja koodiklinikasta (2 h). Luento-osio koostuu uusien asioiden teoriaopetuksesta ja näihin liittyvistä koodiesimerkeistä. Koodiklinikalla tehdään ohjelmoinnin harjoituksia yhdessä ja opiskelijat demoavat itsenäisesti tekemiään tehtäviä.

Toteutuksella seurataan opiskelijan läsnäoloa.

Content scheduling

This course is held in Finnish

Opintojakson suoritettuaan opiskelija osaa:
- kuvailla ohjelmistotuotteen kehitystyön vaiheet ja keskeiset menetelmät
- selittää käsitteet luokkahierarkia, periytyvyys ja monimuotoisuus sekä soveltaa niitä ohjelmistokehityksessä
- havainnoida säännöllisten lausekkeiden ja funktionaalisen ohjelmoinnin käyttömahdollisuuksia
- soveltaa poikkeusrakenteita
- suunnitella, toteuttaa ja dokumentoida yksinkertaisen käyttöliittymäsovelluksen.
Sisältö
- keskeisimmät oliorakenteet
- virheiden käsittely poikkeuksilla
- tietokantayhteyden luominen
- ohjelmistotuotannon prosessi ja menetelmät
- UML kaavioiden ymmärtäminen ja niiden piirtäminen

Further information

Kurssin viestintäkanava on itslearning.

Avoimen AMK:n opiskelijoita otetaan mukaan opintojaksolle max 3.
Lue myös opintojakson edeltävyysehdot.

Evaluation scale

H-5

Assessment methods and criteria

Arvosanan muodostuminen

Opintojakso pitää sisällään 8 viikkotehtäväsarjaa, joihin sisältyy ohjelmoinnin teorian opiskelua sekä aiheeseen liittyviä ohjelmointitehtäviä. Opiskelija saa pisteitä opettajalle demoamistaan tehtävistä.
Jokaisen viikkotehtäväsarjan maksimipistemäärä on 10 pistettä.
Viikkotehtäväsarjojen maksimipistemäärä on 80 pistettä.

Kurssilla tehdään harjoitustyö, jossa opiskelija tekee laajemman sovelluksen. Harjoitustyössä opiskelija pääsee soveltamaan oppimiaan asioita ja osoittamaan osaamistaan. Harjoitustyön tuotos demotaan opiskelijaryhmälle.
Harjoitustyön maksimipistemäärä on 40 pistettä.

Jos kurssin edetessä näyttää siltä, että opiskelija ei saavuta kurssin läpäisyyn tarvittavaa pistemäärää, niin hänen kanssaan sovitaan puuttuvien tehtävien tekemisestä takautuvasti. Näistä tehtävistä hän voi saada maksimissaan puolet tarjolla olevasta pistemäärästä. Opiskelijan tulee kerätä läpäisyyn vaadittava pistemäärä ennen kurssin loppumispäivämäärää.

Koko kurssilla on siis jaossa 120 pistettä.

Pistemäärä 48 (40%) -> arvosana 1
Pistämäärä 66 (55%) -> arvosana 2
Pistemäärä 84 (70%) -> arvosana 3
Pistemäärä 96 (80%) -> arvosana 4
Pistemäärä 108 (90%) -> arvosana 5

Assessment criteria, fail (0)

Katso yllä arvioinnin kohteet:

Opintojakson arvosana muodostuu opiskelijan tekemien, palauttamien ja demoamien tehtävien lukumäärän ja laadun perusteella.

Opiskelijan arvosana on hylätty, mikäli pistemäärä on alle 40 % maksimipistemäärästä.

Assessment criteria, satisfactory (1-2)

Katso yllä arvioinnin kohteet:

Opintojakson arvosana muodostuu opiskelijan tekemien, palauttamien ja demoamien tehtävien lukumäärän ja laadun perusteella.

Opiskelijan arvosana on 1, mikäli pistemäärä on yli 40 % mutta alle 55% maksimipistemäärästä.
Opiskelijan arvosana on 2, mikäli pistemäärä on yli 55 % mutta alle 70% maksimipistemäärästä.

Assessment criteria, good (3-4)

Katso yllä arvioinnin kohteet:

Opintojakson arvosana muodostuu opiskelijan tekemien, palauttamien ja demoamien tehtävien lukumäärän ja laadun perusteella.

Opiskelijan arvosana on 3, mikäli pistemäärä on yli 70 % mutta alle 80% maksimipistemäärästä.
Opiskelijan arvosana on 4, mikäli pistemäärä on yli 80 % mutta alle 90% maksimipistemäärästä.

Assessment criteria, excellent (5)

Katso yllä arvioinnin kohteet:

Opintojakson arvosana muodostuu opiskelijan tekemien, palauttamien ja demoamien tehtävien lukumäärän ja laadun perusteella.

Opiskelijan arvosana on 5, mikäli pistemäärä on vähintään 90% maksimipistemäärästä.

Qualifications

Introduction to Programming or equivalent programming skills

Objective

After completing the course the student can:
- explain the different phases and main methods of the development of a software product
- explain the concepts class hierarchy, inheritance, and polymorphism, and utilize these in software development
- discuss the possibilities of parallel programming
- apply exception handling mechanisms
- design, implement and document a simple user interface application.

Content

- object-oriented structures
- error handling with exceptions
- threads
- database connection
- software development process and methods
- UML diagrams

Materials

In the workspace of this course.

Teaching methods

Reading the given material, studying and trying in practice.

Coding the programming exercises (weekly) and problem solving in practice.

Coding, documenting and demonstrating the exercise work (set task). Attendance.

Exam schedules

No exam.

International connections

Self assessment
Student regularly pays attention to own learning and studying.
Students reflect their own learning, for example, by answering the following questions:
- Topic area: What did I know about the topic beforehand? What did I think about the area?
- While coding: Thoughts about the challenge level and interest of the exercise.
- Afterwards: What did I learn?
Moreover, students learn to assess their own work regarding every assignment using scale: Excellent - Good - Sufficient.

Sustainable development is discussed for example by considering the efficiency of algorithmic solutions (electricity, computing resources), code re-use with the mechanisms of the object-oriented paradigm (work, human resources) and avoiding the unnecessary use of AI (electricity).

Completion alternatives

1) Demonstrate e.g. a real work life project and showing that student masters the contents of this course.
2) mooc.fi contains a programming MOOC, whose contents covers the Introduction to Programming and Advanced Programming courses of the University of Helsinki. Courses are 10 credits together (5+5). MOOC in question contains object oriented material at the end (I.e., parts 8-14).

Student workload

To pass this course requires acceptably finished coding exercises as well as exercise work and return in time.

5 credits: 27 * 5 = 135 hours

Weekly work amount: 135 hours / 14 weeks = 9.6 hours a week.
The course includes 3 hours of guided instruction and 2 hours of assignments where teacher(s) are present.

Content scheduling

The course is given between Jan - Apr 2025.

After passing the course a student:
- knows the basic concepts of object-oriented programming, for example: object, class, attribute, class variable, method, constructor, inheritance, class hierarchy, polymorphism, exceptions
- can apply the previous concepts in practice
- can design and define classes and construct coherent wholes of classes
- recognizes methods that can affect code re-use
- can apply object-oriented paradigm as a part of algorithmic problem solving
- can describe the main phases of the development of software product and the most important methods
- can discuss about the possibilities of parallel programming
- can use exceptions in error handling
- can plan, implement, test and document an application
- uses UML for planning and documentation
- uses style guide and good coding practices

Further information

Maximum of 3 open university students can take this course.

Also take into account that students shall have passed introduction to programming course or have similar skills.

Evaluation scale

H-5

Qualifications

Introduction to Programming or equivalent programming skills

Objective

After completing the Bachelor’s Thesis the student can:
- apply her/his knowledge and skills to fulfill professional engineering assignments
- study independently state-of-the-art engineering challenges and propose functional, economically rational, and ethically sustainable solutions to solve those challenges
- work innovatively and systematically according to the agreed project schedule
- utilize literature and other information sources
- select and use proper working methods and tools
- evaluate of the source material, the knowledge base of the work, the methods used, and the results critically
- communicate clearly with other professionals and customers.

Content

The Bachelor’s Thesis is a research and development project that is completed independently supported by the possible stakeholder’s instructor and the supervising teacher. The thesis project is carried out on a topic related to the student's professional field. The work must be useful for the customer as well as exhibit the student's understanding of the field. The thesis can also be on a topic of the student’s own choice.
Contents:
- Topic selection
- Thesis agreement
- Planning of the work and information gathering
- Implementation of the work
- Reporting of the thesis results
- Final seminar
- Maturity test
- Publication of the thesis

Materials

Material provided by the teacher and shared in learning environment (ITS).
In addition, thesis authors must participate in the Research Communication course either before or during their thesis work, where they will receive guidance on thesis writing

Teaching methods

Research and reporting

Exam schedules

-

International connections

Thesis supervisors guide students in the preparation of their thesis.

Completion alternatives

Types of thesis:
- Research-based thesis
- Functional thesis
- Learning diary
- Portfolio thesis
- Thesis as a demonstration

Student workload

The tasks are described in the section "content".

The scope of the work is 15 credits, which corresponds to approximately 400 hours.

Assessment criteria, approved/failed

Approved: The thesis report has been published in Theseus.

Content scheduling

Completion the thesis work according to the ICT unit's thesis process, including the following tasks:
1. Preparation of the thesis plan and contract preparation form
2. Thesis contract
3. Agreement the follow-up with the supervisor
4. Thesis for comments in Finnish
5. Thesis for comments in English
6. Final seminar presentation on Teams
7. Statement from the commissioner
8. Finalizing the thesis and submitting it for evaluation
9. Maturity test
10. Publishing the thesis in Theseus

Further information

ItsLearning & Teams

Evaluation scale

Hyväksytty/Hylätty

Assessment methods and criteria

The actual evaluation of the thesis is done separately for the thesis according to the instructions of the Turku AMK intranet.
This study unit is marked as approved when the thesis report has been published in Theseus.

Qualifications

Student must participate in the Research Communication course when starting to work with thesis or student must demonstrate in some other way that they have the necessary skills for the work (e.g., completion of a previous higher education degree or an equivalent course).

Objective

After completing the course the student is able to:
- describebasic tasks and concepts of operating systems
- install and configure an operating system for desktop and server
- act as operating systems administrator

Content

- Operating system tasks and concepts
- Installing and configuring operating system for desktops and servers
- System administrating using command line interface
- User access management
- Server installation and management

Materials

I . Course books:

The Linux Command Line: A Complete Introduction
William Shotts
2nd edition
2019

Mastering Ubuntu Server
Jay LaCroix
4th edition
2022

Only selected parts of the course books are read.

The course books can be read in our institution's eBook Central service.

II. Theory part tutorial

Operating System Tutorial: https://www.tutorialspoint.com/operating_system/index.htm

III. Teacher created assignments.

Teaching methods

- reading the course books and other reading material, watching videos
- participating in the lectures
- working with operating system together with instructor
- working alone
- participating in the teamwork

Exam schedules

A multiple-choice exam on the theory of operating systems around february/march.

Exam retake at the end of the course.

International connections

Practical work in classroom under the guidance of the teacher
Practical exercises done at home
Theory of operating systems: giving a presentation in a group
Reading material
Taking the exam

Completion alternatives

The student can complete the course by demonstrating his knowledge and skills of the subjects of the course, for example with the work samples they have made. However, this must be agreed with the instructor during the first 4 weeks of the course.

The student can include a corresponding course taken elsewhere at some educational institution that is acceptable by our educational institution. This happens via AHOT process. Also this matter should be initiated immediately at the beginning of the course.

Student workload

Practical teaching on site 24 h
Theory on site including a presentation in a group 10h
Exam (and a possible resit) 4 hours
Preparing and maintaining of your own virtual environment 7h
Going through the material and doing practical tasks 70 hours
Exam preparation 20h

Content scheduling

Linux operating system is used on the course

In general, the practical teaching proceeds in the following order:

Installing a Linux operating system on a virtual image
Introduction to Operating Systems
Basic use of the Bash shell
Working as root user
Management of software and processes
Managing users and privileges
Advanced use of the Bash shell
Managing network connections
Introduction to servers
Installing the database server
Installing and managing a web server
Basics of information security

In general, the theory teaching proceeds as follows

Teamwork: presentation in a group on a theory topic
Watching other presentations and familiarizing yourself with the theory based on the tutorial
Theory exam and a resit

Evaluation scale

H-5

Assessment methods and criteria

The maximum number of points available from course is 120.

Of that maximum, 80 points comes from 8 individual exercises, 20 points from an exam, 10 points from a teamwork, and 20 points from being present on the lectures.

The course evaluation scale is the following:

Minumum points -> Grade

Less than 40 -> 0
40 -> 1
56 -> 2
72 -> 3
88 -> 4
104 -> 5

Please note this additional condition: You must get at least 28 points from the personal exercises and 12 points from the teamwork and the exam together to pass the course.

The points from being present are calculated using the following scale:

Percentage of being present on the normal lectures -> points

20% ->5
40% ->10
60% ->15
80% ->20

Please also note that by being present you can earn some of the points available from the individual exercises working together with the instructor.

You must be present in demonstration. It does not accumulate your points of being present. If you are not present in the demonstrations, then there is a reduction of 25 % of the points of your returned exercises on these demos. There is also a reduction of 25 % for exercises that are returned late. No exercises are accepted after the end date of the course implementation. After the end date of the course, no substitute or supplementary assignments will be given either. The student must therefore make sure that he collects enough points from different performances during the time of the course.

Assessment criteria, fail (0)

The student has not managed to accumulate enough points to pass the course during the time of the course. Consequently, they have not been able to demonstrate the kind of competence on the basis of which an acceptable grade could be given.

Assessment criteria, satisfactory (1-2)

The student knows what an operating system is and what its main tasks are
The student knows how to install the operating system.
The student knows the basic use of the operating system from the command line.
The student knows how to install software
The student has an understanding of the security of the operating system

Assessment criteria, good (3-4)

The student knows what an operating system is and what its main tasks are
The student knows how to install the operating system
The student knows how to use the operating system in a versatile way.
The student knows how to install, remove and update software
The student knows the good practices of using the operating system.
The student knows how to search for information to complete various tasks.
The student knows the tasks and responsibilities of the main users.
The student knows the basics of managing a multi-user operating system.
The student knows what a server is.
The student is ready to install and maintain the server's various services
The student has an understanding of operating system security and the skills to maintain it

Assessment criteria, excellent (5)

The student knows what an operating system is and what its main tasks are
The student knows how to install the operating system
The student knows how to use the operating system in a versatile way.
The student knows the good practices of using the operating system.
The student knows how to evaluate, install, remove and update software from different sources
The student knows how to search and apply information to perform various tasks.
The student knows the tasks and responsibilities of the main users.
The student knows how to manage a multi-user operating system quite extensively.
The student knows what a server is.
The student knows how to install and maintain various services on the server
The student has an understanding of the security of the operating system and the skills to maintain it
The student knows how to manage a multi-user operating system.
The student knows how to automate operating system tasks.
The student knows how to install, monitor and maintain various services

Objective

After completing the course the student:
Knows the main alternatives technologies on the server-side in developing web applications.
Masters one server-side scripting language and can use some important libraries.
Understands the basics of web application architectures.
Can use a content management system or an application framework in implementing a web application.
Can use efficient tools in server-side scripting.

Content

Learning a server-side scripting language.
Introduction to web application architectures.
Integrating a database server to a web application.
Using a content management system or an application framework in implementing a web application.
Tools for server-side scripting.
Implementing a small scale web application.

Materials

The course books are

Get Programming with Node.js
Jon Wexler
Manning Publications
1st edition (March 15, 2019)

This book is unfortunately not available via TUAS (TuAMK) as ebook. There are few printed copies in out library.

The book is also already quite old, so that we need to modify some code and solutions during the course. However, the book is otherwise very good for being a course book.

Node.js for Beginners: A comprehensive guide to building efficient, full-featured web applications with Node.js
Ulises Gascón
Packt
May 2024

Other material will be announced during the course

Teaching methods

- reading the course books and other reading material
- participating in the lectures
- programming together with instructor
- programming alone
- participating in the teamwork

Exam schedules

No exam.

Completion alternatives

The student can complete the course by demonstrating his knowledge and skills of the subjects of the course, for example with the work samples they have made. However, this must be agreed with the instructor during the first 4 weeks of the course.

The student can include a corresponding course taken elsewhere at some educational institution that is acceptable by our educational institution. This happens via AHOT process. Also this matter should be initiated immediately at the beginning of the course.

Student workload

45 h contact lessons (Each 3h = 2h learning and 1h individual working with the presence of the instructor)
4 h presenting and following team works
40 h preparing teamwork
54 h doing personal exercises

Content scheduling

The course builds on 15 supervised lessons, 7 personal exercises each having multiple tasks, and a teamwork.
*
The teamwork is done in groups of 3 to 4 students. The teamwork commission is published in the middle of the course.
*
The planned course content:
*
Course introduction and the creation of the development environment
Node.js
NPM
ES6 modules
Express Web Framework and the MVC
Routing and handling http requests
Views and Templates
Error handling
Controllers
Models
CRUD and Data persistence
User sessions and authentication
Login and logout
Authorization
*
Three lessons are used for demos.
*
The teamwork is evaluated in an 15 minutes long evaluation event where the group presents its application to the instructor. Each member must clarify his or her input to the result. The source code must be presented too.

Evaluation scale

H-5

Assessment methods and criteria

The maximum number of points available from course is 120.

Of that maximum, 70 points comes from individual exercises, 30 points from teamwork, and 20 points from being present on the lectures.

The course evaluation scale is the following:

Min points -> Grade

0 -> 0
40 -> 1
56 -> 2
72 -> 3
88 -> 4
104 -> 5

Please note this additional condition: You must get at least 20 points from the exercises and 10 points from the teamwork to pass the course.

The points from being present are calculated using the following scale:

Percentage of being present on the normal lectures -> points

20% -> 5
40% ->10
60%->15
80%->20

Please also note that by being present you can earn some of the points available from the individual exercises working together with the instructor.

You must be present in demonstration. It does not accumulate your points of being present. If you are not present in the demonstrations, then there is a reduction of 25 % of the points of your returned exercises on these demos. There is also a reduction of 25 % for exercises that are returned late. No exercises are accepted after the end date of the course implementation. After the end date of the course, no substitute or supplementary assignments will be given either. The student must therefore make sure that he collects enough points from different performances during the time of the course.

Assessment criteria, fail (0)

The student has not managed to accumulate enough points to pass the course during the time of the course. Consequently, they have not been able to demonstrate the kind of competence on the basis of which an acceptable grade could be given.

Assessment criteria, satisfactory (1-2)

The student knows the operating principles and application areas of the Node.js runtime environment
The student knows how configuration files can be utilized in the development and deployment of web applications
The student knows the key issues related to the development of Web applications
The student knows what a web application framework is
The student knows the basic principles of the MVC architecture model
The student knows some of the key program libraries needed for programming web applications
The student knows how to use development tools suitable for tasks
The student knows how to program a simple dynamic website using the web application framework

Assessment criteria, good (3-4)

The student knows the operating principles and application areas of the Node.js runtime environment
The student knows how configuration files can be utilized in the development and deployment of web applications
The student knows the key issues and questions related to the development of Web applications and knows how to implement related solutions
The student knows how to use the web application framework
The student masters the basic principles of the MVC architecture model and knows how to implement them in practice
The student can use software libraries that are needed for programming web applications
The student knows how to use tools suitable for tasks
The student knows how to program a dynamic website using the web application framework
The student can implement user authentication, sessions and permanent storage
The student is well prepared to participate in a project developing a web application.

Assessment criteria, excellent (5)

The student knows the operating principles and the application areas of the Node.js runtime environment and can evaluate its suitability for the task
The student knows how to use configuration files in the development and implementation of web applications
The student masters the key principles and questions related to the development of Web applications and knows how to implement and evaluate related solutions
The student knows how to use the web application framework effectively and evaluate its suitability for the task
The student masters the basic principles of the MVC architecture model and knows how to implement it in practice
The student can evaluate and effectively use the program libraries needed for programming web applications
The student knows how to effectively use the tools suitable for the tasks
The student knows how to program a dynamic website using the web application framework
The student knows how to design and program a dynamic website with user authentication, sessions and permanent storage
The student is excellently prepared to participate in a project developing a web application.

Objective

After completing the course a student can:
- find him/herself a workplace in the field of technology
- enter into a contract with the employer
- act upon the contractual obligations and responsibilities
- be present at the work place on the agreed dates and at the agreed times
- complete work assignments, at least under supervision
- describe functions of an organization and social relationships there
- evaluate the results of the work placement period.

Content

Getting familiar with working life and the profession in the field of technology.
The minimum total extent of practice included in the B.Eng. degree is 30 cr.

Evaluation scale

Hyväksytty/Hylätty

Objective

After completing the course, the student can:

Describe what Cloud Services are, how they affect business and which new opportunities it may enable.
Describe use cases and benefits of cloud services,
Describe SAAS, PAAS, IAAS.
Use some cloud services platforms.
Develops a solution that utilizes cloud services.

Content

Introduction to cloud services
Software as a service (SAAS)
Platform as a service (PAAS)
Infrastructure as a service (IAAS)
Virtual machines and containers
Security of cloud services
Project work

Materials

Task-specific material to be announced separately in Its Learning and in AWS academy.

Teaching methods

- Weekly face-to-face meetings with lecture teaching and small group work
- Learning by doing and experimenting (exercise tasks, project work, information search)
- Small group work and peer learning
- Self-study material
- Teacher guidance and examples

Exam schedules

No exam, and retake not possible after evaluation grade is published.

International connections

FLIP classrooms and learning by doing

Completion alternatives

Not possible

Student workload

Contact hours
- Course introduction: 3 hours
- 12 times 3h theory and AWS support: 12 x 3h = 36 hours
- 16 times AWS Academy self paced sessions: 16 x 1h = 16 hours

Home work:
- Working with assignments: approximately 80 hours

Total: approximately 135 hours

Content scheduling

The course content is divided into four learning objectives(CLOs):

CLO1 Analyze classic data centers and cloud data center solutions.

Introduction to Cloud Computing
1.1 Understand the limitations of traditional computing and evolution of cloud computing
1.2 Understand the concepts of Cluster, Grid and Cloud Computing, its benefits and challenges

Cloud Computing Models and Services
1.3 Explore the standard cloud model, cloud deployment and service delivery models
1.4 Understand service abstraction

Resource Virtualization and Pooling
1.5 Implement physical computing resources virtualization
1.6 Implement machine, server level and operating system virtualization
1.7 Understand resource pooling, sharing and resource provisioning

CLO2 Design a cloud data center based on specific technical requirements.

Resource Virtualization and Pooling
2.1 Implement physical computing resources virtualization
2.2 Implement machine, server level and operating system virtualization

Scaling and Capacity Planning
2.3 Understand the foundation of cloud scaling
2.4 Explore scaling strategies and implement scalable applications
2.5 Explore approaches for capacity planning

Load Balancing
2.6 Explore the goals and categories of load balancing. Explore parameters for consideration.

File System and Storage
2.7 Understand the need for high performance processing and Big Data
2.8 Explore storage deployment models and differentiate various storage types

CLO3 Discuss the need for security, reliability and legal compliance of a cloud data center.
Database Technologies
3.1 Explore database models
3.2 Implement relational and non-relational database as a service

Cloud Computing Security
3.3 Understand the threats to cloud security
3.5 Explore and develop a cloud security model
3.6 Understand Trusted Cloud Computing

Privacy and Compliance
3.7 Explore key privacy concerns in the cloud
3.8 Differentiate security vs. privacy
3.9 Develop a privacy policy

CLO4 Design strategies for the implementation of effective cloud solutions to support business requirements.

Content Delivery Model
4.1 Understand and explore content delivery network models in the cloud

Portability and Interoperability
4.2 Explore portability and interoperability scenarios
4.3 Understand machine imaging
4.4 Differentiate virtual machine and virtual appliance

Cloud Management
4.5 Understand cloud service life cycle
4.6 Understand asset management in the cloud
4.7 Explore cloud service management
4.8 Develop disaster recovery strategies

SELF PACED / FLIP CLASSROOM
In addition to the above theoretical content the students will learn and practice the cloud concepts in AWS academy. The AWS academy online course covers the following modules.

Module 1 - Global Infrastructure
Module 2 - Structures of the Cloud
Module 3 - AWS Console
Module 4 - Virtual Servers
Module 5 - Content Delivery
Module 6 - Virtual Storage
Module 7 - Security 1
Module 8 - Security 2
Module 9 - Monitoring the Cloud
Module 10: Databases
Module 11 - Load Balancers and Caching
Module 12 - Elastic Beanstalk and Cloud Formation
Module 13 - Emerging Technologies in the Cloud
Module 14 - Billing and Support
Module 15 - Other Cloud Features
Module 16 - Optimizing the Cloud with the AWS CDK

Further information

Course material and assignments in Its Learning and AWS academy.

Evaluation scale

H-5

Assessment methods and criteria

Personal assignments: 50 points
AWS Academy Course labs: 30 points
Project: 20 points

The assignments must be returned by the deadline to get the points. The assignments returned after the deadline will give you only half of the points.

Demonstrations of exercises during the contact session is mandatory without demonstration you will lose 50% of your marks.

The grading scale (points -> grade):

50 points -> 1
60 points -> 2
70 points -> 3
80 points -> 4
90 points -> 5

Assessment criteria, fail (0)

Fail < 50 points

Assessment criteria, satisfactory (1-2)

50 points -> 1
60 points -> 2

Assessment criteria, good (3-4)

70 points -> 3
80 points -> 4

Assessment criteria, excellent (5)

90 points -> 5

Objective

After completing the course the student can:
Describe different project management methods and frameworks and their feasibility to different projects.
Ability to function as a project manager in different phases of a project and produce the project related specifications and documents.
Describe different project management association and certifications.
Risk analysis and quality assurance.

Content

The course covers aspects of different project management methods, such as waterfall and agile as well as different project management associations and certifications. Furthermore, the course covers topics about project management, including project planning, budgeting, scheduling and WBS, risk assessment and quality assurance.

Materials

Various internet sources, links & descriptions online.
Lecture slides.
theFIRMA's and course's Itslearning.

Teaching methods

Lectures, simulation, group work and independent work, assignment-based learning, project work, online activities and ICT guest lectures.

Exam schedules

No exam.

If a student does not pass the course, they are required to re-enroll and participate in the course during the next available offering, typically the following academic year.

International connections

Practical assignments and reports
Project work
Team learning
Self study

Completion alternatives

No optional ways for implementation

Student workload

Lectures and on-site activities: 58 hours
Assignments and self study: 67 hours
Guest lectures + report: 10 hours
TOTAL 135 hours

Course includes 8 assignments: 2 individual assignments and 6 group assignments.

Content scheduling

The course starts with the course introduction and team building followed by Scrum simulation. The focus is set on agile project management, especially Scrum framework. There after the aspects of waterfall project management methodology are discussed. The student learns how to create a realistic project plan, budget, timeframe and risks for the project. In addition, student learns setting up goals for the project that create customer value, scope management, resource allocation and division of work and sizing. IPMA International Project Management Association and other project management institutions and activities are presented as well as IPMA standards – Individual Competence Baseline. Students also practice risk management and risk analysis in more detail. Lastly, quality management in software engineering is handled in lectures and group work.

Further information

The course's and theFIRMA itslearning and Microsoft Teams.

Evaluation scale

H-5

Assessment methods and criteria

Assignments and reports: diagnostic assessment.

Course includes 8 assignments: 2 individual assignments and 6 group assignments. Maximum points of each assignment is 30 points. Thus, the maximum amount of points from assignments is 240. Late submission for the assignments will reduce the points by 50%.

In addition, the course includes guest lectures of which the student is required to write a report. The report is evaluated with the scale 0 – 30 points.

The presence in the lectures, Scrum simulation and facilitated group meetings are marked down. The first and last lectures and facilitated group meetings give the student 2 points, Scrum simulation gives the student 3 points and other lectures give the student 1 point each. In total, there are 30 points from presence.

Altogether these will give the students the maximum of 300 points. These points are evaluated in the following way:
Fail: 0 – 80 points
grade 1: 81 – 123 points
grade 2: 124 – 167 points
grade 3: 168 – 211 points
grade 4: 212 – 255 points
grade 5: 256 – 300 points.

Assessment criteria, fail (0)

Less than 81 points.

No show, not carrying out responsibilities, disappearing from team work, lack of communication with other team members.

Assessment criteria, satisfactory (1-2)

Grade 1: 81-123 points
Grade 2: 124-167 points

Poor, but satisfactory performance both in independent work and team work. Low participation on lectures and other activities. Satisfactory guest lecture report.

Assessment criteria, good (3-4)

Grade 3: 168-211 points
Grade 4: 212-255 points

Good performance both in team work and independent work. Active participation on lectures and other activities. Good guest lecture report.

Assessment criteria, excellent (5)

Grade 5: 256-300 points

Excellent performance both in team work and independent work. Active participation on lectures and other activities. Excellent guest lecture report.

Objective

By the end of the course, students will be able to:

· Configure VLANs and Inter-VLAN routing applying security best practices.
· Troubleshoot inter-VLAN routing on Layer 3 devices.
· Configure redundancy on a switched network using STP and EtherChannel.
· Troubleshoot EtherChannel on switched networks.
· Explain how to support available and reliable networks using dynamic addressing and first-hop redundancy protocols.
· Configure dynamic address allocation in IPv6 networks.
· Configure WLANs using a WLC and L2 security best practices.
· Configure switch security to mitigate LAN attacks.
· Configure IPv4 and IPv6 static routing on routers.

Content

CCNAv7: Switching, Routing, and Wireless Essentials (SRWE) covers the architecture, components, and operations of routers and switches in small networks and introduces wireless local area networks (WLAN) and security concepts. Students learn how to configure and troubleshoot routers and switches for advanced functionality using security best practices and resolve common issues with protocols in both IPv4 and IPv6 networks. The course includes activities using Packet Tracer, hands-on lab work, and a wide array of assessment types and tools.

Materials

All needed material will be available online in https://www.netacad.com
Further course enrollment instructions are provided by instructor.
Please register to the site using school email.

Exam schedules

Theory final exam and Packet Tracer exam will held in course.
You can do one re-exam within course deadline.
NOTE: Course ending time shown in academy system is not real, please check the course plan for end date!

Student workload

Lecturing and laboratory work each week
Independent studying, including:

- Studying the course material
- Completing exercises
- Preparation for finals exam(s)

Content scheduling

Course covers the architecture, components, and operations of routers and switches in small networks and introduces wireless local area networks (WLAN) and security concepts. Students learn how to configure and troubleshoot routers and switches for advanced functionality using security best practices and resolve common issues with protocols in both IPv4 and IPv6 networks. The course includes activities using Packet Tracer, hands-on lab work, and a wide array of assessment types and tools. By the end of the course, students will be able to:

- Configure VLANs and Inter-VLAN routing applying security best practices.
- Troubleshoot inter-VLAN routing on Layer 3 devices.
- Configure redundancy on a switched network using STP and EtherChannel.
- Troubleshoot EtherChannel on switched networks.
- Explain how to support available and reliable networks using dynamic addressing and first-hop redundancy protocols.
- Configure dynamic address allocation in IPv6 networks.
- Configure WLANs using a WLC and L2 security best practices.
- Configure switch security to mitigate LAN attacks.
- Configure IPv4 and IPv6 static routing on routers.

Evaluation scale

H-5

Assessment methods and criteria

Laboratory assignments in laboratory room
Packet tracer assignments done at home
Module exams
Practice final exams
Theory final exam and Packet Tracer final exam.

The overall result is the sum of the all results of the assignments and exams, passing limit is 60%.
Detailed grading limits will be provided in course plan when course starts but past grading limits have been the following:

Less than 60% Fail
60-67.4% Grade 1
68-75.4% Grade 2
76-83.4% Grade 3
84-91.4% Grade 4
91.5% or higher Grade 5

Qualifications

Courses Internet Networks and Security (5051215) and Introduction to Networks (TE00BU11) or equivalent skills.

Objective

After completing the course the student:
- can program JavaScript programming language
- can use the React application framework
- is able to design modular front-end software
- knows the special features of front-end development
- is able to use efficient and modern programming tools

Content

- Basics of JavaScript
- Modern features of JavaScript
- The basics of the React library
- Front-end software design and modularization
- Front-end development features
- Development tools

Materials

The course material (Only selected parts from the books)
*
Eloquent JavaScript
Marijn Haverbeke
No Starch Press; 3 edition (December 4, 2018)
Available on the Net: http://eloquentjavascript.net
*
Professional JavaScript for Web Developers
5th Edition
Matt Frisbie
Published by Wrox
Available in ProQuest EBook Central
*
Selected project-based React-tutorial
*
Learning React : Modern Patterns for Developing React Apps
2nd edition
Alex Banks and Eve Porcello
Available in ProQuest EBook Central
*

Teaching methods

- reading the course books and other reading material, watching videos
- participating in the lectures
- programming together with instructor
- programming alone
- participating in the teamwork

Exam schedules

No exam

Completion alternatives

The student can complete the course by demonstrating his knowledge and skills of the subjects of the course, for example with the work samples they have made. However, this must be agreed with the instructor during the first 4 weeks of the course.

The student can include a corresponding course taken elsewhere at some educational institution that is acceptable by our educational institution. This happens via AHOT process. Also this matter should be initiated immediately at the beginning of the course.

Student workload

39 h contact lessons (Each 3h = 2h learning and 1h individual working with the presence of the instructor)
4 h presenting and following team works
40 h preparing teamwork
54 h doing personal exercises

Content scheduling

Contents

I. JavaScript (Lectures and personal exercises)

- Basics
- Strings
- Objects, destructuring
- Arrays, array operations
- Programming functions
- Error handling
- DOM, event handling
- Modules
- Asynchronous programming
- Tools

II. React (Lectures and a teamwork)

- Basics
- JSX
- Components
- Modularization
- Tools
- Managing state
- Hooks

III. Teamwork: A simple single page web application with React (without backend)

7 personal JavaScript exercises.
React-based Teamwork.

Further information

itsLearning and email

Evaluation scale

H-5

Assessment methods and criteria

The maximum number of points available from course is 120.

Of that maximum, 70 points comes from individual exercises, 30 points from teamwork, and 20 points from being present on the lectures.

The course evaluation scale is the following:

Min points -> Grade

0 -> 0
40 -> 1
56 -> 2
72 -> 3
88 -> 4
104 -> 5

Please note this additional condition: You must get at least 20 points from the exercises and 10 points from the teamwork to pass the course.

The points from being present are calculated using the following scale:

Percentage of being present on the normal lectures -> points

20% -> 5
40% ->10
60%->15
80%->20

Please also note that by being present you can earn some of the points available from the individual exercises working together with the instructor.

You must be present in demonstration. It does not accumulate your points of being present. If you are not present in the demonstrations, then there is a reduction of 50 % of the points of your returned exercises on these demos. There is also a reduction of 50 % for exercises that are returned late. No exercises are accepted after the end date of the course implementation. After the end date of the course, no substitute or supplementary assignments will be given either. The student must therefore make sure that he collects enough points from different performances during the time of the course.

Assessment criteria, fail (0)

The student has not managed to accumulate enough points to pass the course during the time of the course. Consequently, they have not been able to demonstrate the kind of competence on the basis of which an acceptable grade could be given.

Assessment criteria, satisfactory (1-2)

The student knows the application areas and the application environments of the JavaScript programming language
The student knows the basics of the modern JavaScript programming language
The student knows at least of the central front-end libraries of the JavaScript programming language
The student knows some of the key tools used in JavaScript programming
The student knows how to program simple applications with JavaScript or its library

Assessment criteria, good (3-4)

The student knows the application areas and the application environments of the JavaScript programming language
The student masters the basics of the modern JavaScript programming and some of the JavaScript's advanced features
The student can apply one of the central front-end libraries of the JavaScript programming language
The student knows how to search for information to develop his JavaScript and programming skills and to solve problems
The student knows how to use some key tools used in JavaScript programming
The student knows how to program applications with JavaScript and its libraries
The student knows how to work in a JavaScript programming project

Assessment criteria, excellent (5)

The student knows the application areas and the application environments of the JavaScript programming language
The student masters the of the modern JavaScript programming extensively and can utilize efficiently its libraries
The student knows how to efficiently search for information to develop his JavaScript and programming skills and to solve problems
The student knows how to effectively use and search for different tools used in JavaScript programming
The student knows how to design and program modularized applications with JavaScript and its libraries
The student knows how to work proactively and responsibly in a JavaScript programming project

Objective

After completing the course, the student can:
- utilize suitable software development processes and tools when working with data engineering and AI

Content

Software development practices
DevOps
MLOps
DataOps
Practical work with suitable tools

Materials

Teacher provided lecture material
Supporting public online material
Teacher provided virtual machines
All needed material (or at least a link to them) will be available in itslearning.

Teaching methods

Contact learning, practical exercises, independent study

Exam schedules

Assignments returned throughout the course
Small project at the end of the course

International connections

Given examples and exercises support each topic studied during the lectures. Additional material in the form of tutorials and reliable information sources is provided.

Student workload

Contact hours 16 h
Inpendent studying 119h, including:
- Studying the course material
- Completing assignments
- Project

Content scheduling

The basic idea of DevOps, MLOps, DataOps

Further information

Itslearning and contact classes are the main communication channels used on this course.

Evaluation scale

H-5

Objective

After completing the course the student can:
Describe different software development methodologies and evaluate their feasibility to software projects
Describe software development project phases
Understand and draw UML diagrams
Understand the importance of specification and planning to software development
Work in different phases of a software project

Content

Software development project
Specification, planning, programming, testing, deployment, and maintenance of a software project
UML modelling

Materials

Lecture slides, material in Itslearning, linked web-based material and several local and remote applications

Teaching methods

Learning by doing: Attendance concerning the classes, doing assignments during the classes and homework and the sprint-based project work in small groups by applying Scrum

Exam schedules

There are no actual exams.

International connections

Using the existing devices, existing software and activity in the campus building in which the students already are with the high probability.

Completion alternatives

There is only one way of completion.

Student workload

Classes 40 hours, assignments during the classes and personal homework and studying (35 hours), project work in small groups (60 hours) with reporting in Itslearning, total 135 hours

Content scheduling

After the course, the student
- knows software project models and can estimate their suitability for the project at hand
- can operate at different stages of the software project
- understands the significance of planning, designing and testing in the software project
- is able to create software specifications (models with UML) and UI prototypes
- is able to apply version management (Git) in cooperation with others
- is able to apply Atlassian Jira Software tool for planning tasks, running Sprints and managing software requirements

Further information

An oral communication is used during the classes. For the written communication Itslearning and e-mail are applied.

Evaluation scale

H-5

Assessment methods and criteria

Grading with scale 0-5.

Group work is 60 % of the grade. To pass the course you need to pass the group work i.e. get at least half of the total points.
- Each sprint is evaluated with 0-2 points. There will be 6 Sprints in total. Passing the group work = min. 6 points.
- Sprint review, for example, 2 points (all tasks done in time, as requested, no weaknesses), 1 point (some shortcomings/omissions in answers), 0 point (not in time, some tasks missing, major faults).
- Points = Grade: 0-5 points = Failed; 6-8 points = 1; 9-10 points = 2; 11-12 points = 3 i.e. with the group work only, a student can get grade 3.

Active presence and/or returning exercises during the teaching session is 40 % of the grade.
- 1 point / session available, which is based on either presence or exercise returns during the session.
- With participating in sessions, a student can get 0-2 grades more on top of the group work grade
- Points = Grade: 0 points = 0 grade, half of the points = 1 grade, max points = 2 grades (and linearly between the lower and the upper bounds)

Assessment criteria, fail (0)

The student does not know how the knowledge or the methods or is not able to apply them on the level required in the criterion for grade of level 1-2.

Assessment criteria, satisfactory (1-2)

The student knows the basic concept of software engineering and knows some of the common design and planning methods of the discipline. The student is able to apply the knowledge and the methods in the simple given context. The student achieves the lower boundary of the points for the grade level concerning the course.

Assessment criteria, good (3-4)

The student knows the basic concept of software engineering with its fundamental properties and knows all the common design and planning methods of the discipline. The student is able to apply the knowledge and the methods in the given context. The student achieves the lower boundary of the points for the grade level concerning the course.

Assessment criteria, excellent (5)

The student knows the basic concept of software engineering with its fundamental properties and knows all the common design and planning methods of the discipline with the sovereign manners. The student is able to apply the knowledge and the methods in the any given context. The student achieves the lower boundary of the points for the grade level concerning the course.

Objective

After completing the course the student can:
* program efficiently in a team
* reuse code and utilize libraries
* understand and apply some design patterns
* use an IDE as a programming tool
* use a version control system

Content

* relevant programming libraries
* learning some common design patterns
* using IDE in programming
* using a version control system in application development
* project work: implementing an application in a team

Materials

The course book:

Microservice APIs: Using Python, Flask, FastAPI, OpenAPI and more
Jose Haro Peralta
Mar 7, 2023

The part 3 of the course book "Designing And Building GraphQL APIs" is going to be totally skipped.

A limited number of book licenses is going to be available via TUAS' electronic library.

Other learning material can be announced during the course.

Teaching methods

- reading the course books and other reading material, watching videos
- participating in the lectures
- programming together with instructor
- programming alone
- defining interactive documents
- participating in the teamwork

Completion alternatives

The student can complete the course by demonstrating his knowledge and skills of the subjects of the course, for example with the work samples they have made. However, this must be agreed with the instructor during the first 4 weeks of the course.

The student can include a corresponding course taken elsewhere at some educational institution that is acceptable by our educational institution. This happens via AHOT process. Also this matter should be initiated immediately at the beginning of the course.

Student workload

30 h contact lessons
4 h presenting and following team works onsite
47 h preparing teamworks
54 h doing personal exercises

Content scheduling

The contents.

REST API
Microservice Architecture
Designing and implementing a back-end service with a REST API
Documenting a REST API
Testing the REST API
Using professional tools

The programming language is Python.

6 personal assignments.
Two teamworks (not compulsory)

Evaluation scale

H-5

Assessment methods and criteria

The maximum number of points available from course is 120.

Of that maximum, 60 points comes from 6 individual exercises, 40 points from teamworks, and 20 points from being present on the lectures.

The course evaluation scale is the following:

Min points -> Grade

0 -> 0
40 -> 1
56 -> 2
72 -> 3
88 -> 4
104 -> 5

An additional condition: You must to get at least 25 points from the exercises to pass the course.

The points from being present are calculated in a following way:

Percentage of being present on the normal lectures -> points

20% -> 5
40% ->10
60%->15
80%->20

Please also note that by being present you can earn some of the points available from the individual exercises working together with the instructor.

You must be present in demonstration. It does not accumulate your points of being present. If you are not present in the demonstrations, then there is a reduction of 50 % of the points of your returned exercises on these demos. There is also a reduction of 50 % for exercises that are returned late. No exercises are accepted after the end date of the course implementation. After the end date of the course, no substitute or supplementary assignments will be given either. The student must therefore make sure that he collects enough points from different performances during the time of the course.

Assessment criteria, fail (0)

The student has not managed to accumulate enough points to pass the course during the time of the course. Consequently, they have not been able to demonstrate the kind of competence on the basis of which an acceptable grade could be given.

Assessment criteria, satisfactory (1-2)

The student understands the basic of the REST API
The student knows what microservice architecture and microservices are
The student can implement a simple REST API and call the services it offers
The student understands the importance of documenting the REST API in a modern way
The student is ready to use the programming, documentation and testing tools necessary for API development

Assessment criteria, good (3-4)

The student understands the basics of the REST API and related authentication
The student knows what microservice architecture and microservices are
The student can implement a simple REST API, the related authentication and call the services it offers
The student understands the importance of REST API documentation in a modern way and knows how to prepare these documents
The student can use the programming, documentation and testing tools necessary for API development
The student knows how to work in a project where a microservice that permanently stores data is implemented

Assessment criteria, excellent (5)

The student understands the basics of the REST API and related authentication
The student knows what microservice architecture and microservices are
The student knows how to implement a simple REST API, the related authentication and call the services it offers
The student understands the importance of REST API documentation in a modern way and knows how to prepare these documents
The student can test the REST API
The student can use efficiently the programming, documentation and testing tools necessary for interface development
The student can effectively search for information to develop their skills and solve problems
The student can work proactively and efficiently in various roles in API programming projects
The student knows how to work in a project where a microservice that permanently stores data is implemented

Objective

In the studies of the Finnish language and communication, you will become familiar with expert and workplace communication. During this course, you will practice communication and interaction skills required in the professional world.

After completing the course, you will be able to:
• operate in communication and interaction situations in your own field
• develop your own communication skills
• consider the recipient, the situation, and the practices of your professional field.

Content

Course content:
- Characteristics of professional communication style
- Analysis and writing of field-specific texts
- Workplace oral communication situations
- Self-assessment of communication skills
- Giving and receiving feedback

Materials

Verkkomateriaali ja opettajan materiaali, jotka jaetaan ItsLearningin kautta.
Mahdollisesti Kauppinen, A.; Nummi, J. & Savola, T. (2012 tai uud.). Tekniikan viestintä. Kirjoittamisen ja puhumisen käsikirja. Helsinki: Edita.

Teaching methods

kontaktiopetus laboratoriotyyppisesti, tehtäväperustaisuus, itsenäinen opiskelu, tiimityö
Suuri osa tehtävistä on mahdollista tehdä vain osallistumalla kontaktiopetukseen, joten läsnäolo tunneilla on tärkeää.

Exam schedules

Opintojaksolla ei ole tenttiä, vaan arviointi perustuu osatehtäviin ja jatkuvaan arviointiin.

International connections

Opintojaksolla opiskellaan laboratoriomaisesti viestinnän ja vuorovaikutuksen perustaitoja, jotka harjaannuttavat oman alan työtehtävissä tarvittaviin vuorovaikutus- ja viestintätaitoihin. Opiskelija tutustuu ennen kontaktitunteja kontaktitunnin aiheisiin, joihin on annettu materiaaleja ja linkkejä Itslearningissa. Opintojaksolla tehtäviä ryhmäharjoituksia tehdään erikokoisissa ja vaihtuvissa pienryhmissä, joissa asiaosaamisen lisäksi harjoitellaan mm. vuorovaikutustaitoja, keskustelu- ja esiintymistaitoja, yhteiskirjoittamisen taitoja, vertaispalautteen antamista suullisesti ja kirjallisesti. Yksilötehtävissä kehitetään yksilöllisiä viestintävalmiuksia. Opintojaksolla käytetään monipuolisesti digitaalista opiskelumateriaalia ja sähköistä oppimisympäristöä videoiden ja tekstien palauttamiseen sekä materiaalin jakamiseen.

Completion alternatives

Ei ole vaillinaista suoritustapaa.

Student workload

Opiskelijan työmäärä koostuu kontaktitunneista (n. 36 t), kotitehtävistä ja itsenäisestä opiskelusta, ennakkotehtävistä, itsenäisestä materiaaliin tutustumisesta ym. (n. 34 t), asiatyylisen tekstin laatimisesta (n. 20 t), puhe-esitykseen valmistautumisesta (n. 20 t) ja kielenhuollon testiin valmistautumisesta (n. 20 t).

Content scheduling

syys–joulukuu 2024
asiantuntija viestijänä, puhe-esityksen valmistautuminen, puhe-esityksiä, asiatyylinen kirjoittaminen ja harjoituksia

Opintojakson tavoitteet, tehtävät ja niiden arviointi, aikataulutus yms. käytännön asiat käydään läpi ensimmäisellä tapaamiskerralla, jolloin opiskelijan on syytä olla paikalla. Ellei opiskelija ole ensimmäisellä kerralla paikalla, on hänen selvitettävä itse opintojakson ensimmäisellä tunnilla käydyt asiat Itslearningin materiaaleista ja Pepistä.

Further information

Participating in the study course is required to have Finnish language skills from the mother tongue level, i.e. C skill level. It is the student's responsibility to check that the prerequisite conditions are met before registering. The teacher has grounds to reject the registration if the prerequisites are not met.

Opintojaksolle osallistuvalta edellytetään äidinkielen tasoista suomen kielen taitoa eli C-taitotasoa. Opiskelijalla on vastuu tarkistaa edeltävyysehtojen täyttyminen ennen ilmoittautumista. Opettajalla on peruste hylätä ilmoittautuminen, jos edeltävyysehdot eivät täyty.

Viestintäkanavana käytetään sähköpostia ja Itslearningia.

Evaluation scale

H-5

Assessment methods and criteria

Arviointi perustuu osatehtäviin ja jatkuvaan arviointiin.
Arvosana muodostuu seuraavasti:
asiatyylinen teksti 25 %
kielenhuollon testi 25 %
suullinen esitys 25 %
tunti- ja kotitehtävät 25 %

Kuhunkin tehtävään liittyvät arviointikriteerit kerrotaan tarkemmin tehtävänannon yhteydessä. Tehtävien palautusajat ovat ehdottomia, myöhästyneitä tehtäviä ei oteta vastaan.
Opiskelijan ilmoittautuminen opintojaksolle poistetaan viimeistään kuukauden kuluttua, ellei ole näyttöä aikomuksesta suorittaa opintojakso.

Assessment criteria, fail (0)

The Finnish language skills of the student participating in the course do not meet the C skill level requirements.

Opiskelija on osallistunut ryhmän työskentelyyn vain vähän tai ei ollenkaan ja/tai ns. tunti-/kotitehtävistä saatu pistemäärä on kokonaisarvioinnissa alle 1 p.

Opiskelija ei ole suorittanut oppimistehtäviä hyväksytysti tai saavuttanut hyväksyttyyn arvosanaan oikeuttavaa pistemäärää oppimistehtävistä..

Opiskelijan ilmoittautuminen opintojaksolle poistetaan viimeistään kuukauden kuluttua, jos hänellä ei ole näyttöä aikomuksesta suorittaa opintojakso.

Assessment criteria, satisfactory (1-2)

Opiskelija pystyy kirjoittamaan asiatyylisen, ohjeiden mukaisen lähdemateriaaliin pohjautuvan raportin. Opiskelija tunnistaa kielenhuollon merkityksen asiatekstissä. Opiskelija pystyy pitämään suullisen esityksen. Hän osallistuu lähitapaamisiin ja keskusteluun osittain.

Assessment criteria, good (3-4)

Opiskelija tietää, mikä on viestinnän merkitys työyhteisössä. Hän pystyy kirjoittamaan ohjeiden mukaisen ja teoriakirjallisuutta hyvin hyödyntävän raportin. Opiskelija osoittaa kielenhuollon tuntemusta. Opiskelija pitää suullisen esityksen ohjeiden mukaisesti. Opiskelija asennoituu viestintään positiivisesti sekä osallistuu lähitapaamisten työskentelyyn ja keskusteluun aktiivisesti.

Assessment criteria, excellent (5)

Opiskelija tietää ja ymmärtää, mikä on viestinnän merkitys työyhteisössä ja innostuu pohtimaan viestintää käytännössä esimerkiksi omassa työskentelyssään. Hän pystyy suunnittelemaan ja kirjoittamaan ohjeiden mukaisen ja erinomaisella tavalla teoriakirjallisuutta hyödyntävän raportin. Opiskelija osaa arvioida kielenhuollon osaamistaan ja käyttää tietoperustaa tekstinsä tarkistamisessa. Opiskelija pitää suullisen esityksen ohjeiden mukaisesti ja osaa soveltaa ohjeita ja kokemuksiaan esitykseensä. Opiskelija asennoituu viestintään erittäin positiivisesti sekä osallistuu lähitapaamisten työskentelyyn ja keskusteluun motivoituneena ja tavoitteellisesti.

Qualifications

Participation in the course requires a language proficiency level of C in Finnish.

Objective

In the studies of the Finnish language and communication, you will become familiar with expert and workplace communication. During this course, you will practice communication and interaction skills required in the professional world.

After completing the course, you will be able to:
• operate in communication and interaction situations in your own field
• develop your own communication skills
• consider the recipient, the situation, and the practices of your professional field.

Content

Course content:
- Characteristics of professional communication style
- Analysis and writing of field-specific texts
- Workplace oral communication situations
- Self-assessment of communication skills
- Giving and receiving feedback

Materials

Verkkomateriaali ja opettajan materiaali, jotka jaetaan ItsLearningin kautta.
Mahdollisesti Kauppinen, A.; Nummi, J. & Savola, T. (2012 tai uud.). Tekniikan viestintä. Kirjoittamisen ja puhumisen käsikirja. Helsinki: Edita.

Teaching methods

kontaktiopetus laboratoriotyyppisesti, tehtäväperustaisuus, itsenäinen opiskelu, tiimityö
Suuri osa tehtävistä on mahdollista tehdä vain osallistumalla kontaktiopetukseen, joten läsnäolo tunneilla on tärkeää.

Exam schedules

Opintojaksolla ei ole tenttiä, vaan arviointi perustuu osatehtäviin ja jatkuvaan arviointiin.

International connections

Opintojaksolla opiskellaan laboratoriomaisesti viestinnän ja vuorovaikutuksen perustaitoja, jotka harjaannuttavat oman alan työtehtävissä tarvittaviin vuorovaikutus- ja viestintätaitoihin. Opiskelija tutustuu ennen kontaktitunteja kontaktitunnin aiheisiin, joihin on annettu materiaaleja ja linkkejä Itslearningissa. Opintojaksolla tehtäviä ryhmäharjoituksia tehdään erikokoisissa ja vaihtuvissa pienryhmissä, joissa asiaosaamisen lisäksi harjoitellaan mm. vuorovaikutustaitoja, keskustelu- ja esiintymistaitoja, yhteiskirjoittamisen taitoja, vertaispalautteen antamista suullisesti ja kirjallisesti. Yksilötehtävissä kehitetään yksilöllisiä viestintävalmiuksia. Opintojaksolla käytetään monipuolisesti digitaalista opiskelumateriaalia ja sähköistä oppimisympäristöä videoiden ja tekstien palauttamiseen sekä materiaalin jakamiseen.

Completion alternatives

Ei ole vaillinaista suoritustapaa.

Student workload

Opiskelijan työmäärä koostuu kontaktitunneista (n. 36 t), kotitehtävistä ja itsenäisestä opiskelusta, ennakkotehtävistä, itsenäisestä materiaaliin tutustumisesta ym. (n. 34 t), asiatyylisen tekstin laatimisesta (n. 20 t), puhe-esitykseen valmistautumisesta (n. 20 t) ja kielenhuollon testiin valmistautumisesta (n. 20 t).

Content scheduling

syys–joulukuu 2024
asiantuntija viestijänä, puhe-esityksen valmistautuminen, puhe-esityksiä, asiatyylinen kirjoittaminen ja harjoituksia

Opintojakson tavoitteet, tehtävät ja niiden arviointi, aikataulutus yms. käytännön asiat käydään läpi ensimmäisellä tapaamiskerralla, jolloin opiskelijan on syytä olla paikalla. Ellei opiskelija ole ensimmäisellä kerralla paikalla, on hänen selvitettävä itse opintojakson ensimmäisellä tunnilla käydyt asiat Itslearningin materiaaleista ja Pepistä.

Further information

Participating in the study course is required to have Finnish language skills from the mother tongue level, i.e. C skill level. It is the student's responsibility to check that the prerequisite conditions are met before registering. The teacher has grounds to reject the registration if the prerequisites are not met.

Opintojaksolle osallistuvalta edellytetään äidinkielen tasoista suomen kielen taitoa eli C-taitotasoa. Opiskelijalla on vastuu tarkistaa edeltävyysehtojen täyttyminen ennen ilmoittautumista. Opettajalla on peruste hylätä ilmoittautuminen, jos edeltävyysehdot eivät täyty.

Viestintäkanavana käytetään sähköpostia ja Itslearningia.

Evaluation scale

H-5

Assessment methods and criteria