Study Guide

Objective

After completing the course, the students can
- work in the AI project
- describe and understand how AI projects are implemented

Content

Practical project related to AI

Materials

The course materials will be announced later during the course.

Exam schedules

No Exam

Student workload

Contact hours:
- 4 times 4h theory and practice: 4 x 4h = 16 hours

Final project: approximately 114 hours

Total: approximately: 130 hours

Content scheduling

A deep learning-focused, project-based course that empowers master's students to explore and develop AI projects, fostering their ability to conceive, implement, and optimize intelligent solutions.

Session 1: AI Project Lifecycle and Data Selection
Session 2: Data Preprocessing and Model Selection
Session 3: Presenting First Results and Discussion
Session 4: Algorithm Optimization and Tuning

Further information

This course is project-based, requiring students to possess knowledge in machine learning and deep learning, specifically in image recognition and Sequential models.
Consequently, it is advisable to enroll in the 'Components and Applications of Artificial Intelligence' course first. In that course, students learn how to employ deep neural networks for image recognition (using CNN) and using RNN for sequential models. This foundational knowledge will better prepare students for the project-based nature of this course

Evaluation scale

Hyväksytty/Hylätty

Objective

After completing the course, the students can
- work in the Data Engineering project
- describe and understand how Data Engineering projects are implemented

Content

Practical project related to Data Engineering

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

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 data engineering methods and pipelines
-different components
-integration of said components (MQ systems)
-data engineering frameworks (Apache family)

Further information

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

The student is required to have a computer capable of running a simple Ubuntu virtual machine.

Evaluation scale

H-5

Assessment methods and criteria

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

Objective

After completing the course, the student can:
- learn about the history and background of Cloud Computing
- discover Cloud Computing including examples of real-world problems
- understand the most commonly used platforms for cloud computing such as Amazon AWS and Microsoft Azure (and possibly CSC Finland).
- understand the various Service models on Cloud as IaaS, PaaS, and SaaS
- learn the basic network security techniques in the cloud environment

Content

- Cloud computing
- Software-as-a-Service (SaaS)
- Platform-as-a-Service (PaaS)
- Infrastructure-as-a-Service (IaaS)
- Cloud computing technologies and tools
- Security in the cloud environment

Materials

Task-specific material to be announced separately in Its Learning

Teaching methods

- Self-paced learning 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

Self paced, FLIP classrooms and learning by doing

Completion alternatives

Self-paced learning

Student workload

Contact hours
- Course introduction: 4 hours
- Introduction to AWS academy: 4 hours
- Group Project Presentations: 2 X 4 hours
- 16 times AWS Academy self paced sessions: 16 x 2h = 32 hours
- 12 times 2h theory self paced: 12 x 2h = 24 hours
Home work:
- Working with assignments: approximately 80 hours

Total: approximately 142 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.

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 basic concepts and processes related to Data Engineering and AI

Content

- Data Engineering process
- Basics of AI
- Fields and evolution of AI
- Big data
- Basics of Machine Learning

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)

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

Additionally, some other materials will be available via the learning environment (ITS).

Teaching methods

- Short lectures are delivered by the teacher (theory and practice)
- Self-study tasks (theory and practice)
- Practical classwork
- Teamwork Project (including three assignments)

Exam schedules

No exam

Student workload

Contact hours:
- 4 times 3h theory and practice: 4 x 4h = 12 hours

Assignments for the final project: approximately 118 hours

Total: approximately: 130 hours

Content scheduling

Course Outline and Schedule: (preplan)
Session 1: Introduction to the Course, Data, and Data Preprocessing:
• Project overview and requisites
• Initiation of project's initial phase: Data preprocessing and visualization (assignment 1)
Session 2: Machine Learning I
• Students' presentations showcasing outputs and implementation of the first assignment
• Commencement of phase two: Supervised and unsupervised learning (assignment 2)
Session 3: Machine Learning II
• Students' presentations showcasing outputs and implementation of the second assignment
• Tuning and optimizing your Machine Learning algorithms (assignment 3)
Session 4: Students' presentations showcasing outputs and implementation of the third assignment, followed by discussions encompassing all project phases.

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 prerequisite courses and topics:

1. Python Programming: Proficiency in basic Python syntax and programming constructs, understanding of Object-Oriented Programming (OOP) concepts.

2. Data Management: Experience with data manipulation libraries such as Pandas for handling datasets. Data manipulation involves transforming data, cleaning it, organizing it, and preparing it for analysis.

3. Basic Linear Algebra: Understanding of vectors, matrices, and basic operations on them.

4. Statistics and Probability: Knowledge of descriptive statistics (mean, median, mode, variance, etc.), and familiarity with probability distributions.

Evaluation scale

H-5

Assessment methods and criteria

The course is graded on a scale of 0-5.
*
In order to receive an approved performance and pass the course, the student must receive an acceptable mark for the three assignments.
*
You can get at most 40, 30, and 30 points for each phase of the project (assignments 1-3). You can therefore get a maximum of 100 points from all phases of the project.
*
The passive students of the groups won't earn the assignment points.

Assessment criteria, fail (0)

Less than 50% in assignments not passed.

Assessment criteria, satisfactory (1-2)

50% - 65% from the total points of the assignments

Assessment criteria, good (3-4)

66% - 80% from the total points of the assignments

Assessment criteria, excellent (5)

81%- 100% from the total points of the assignments

Objective

After completing the course, the students can
- describe and understand how MLOps projects operate

Content

- Process and tools for Machine Learning Engineering for Production (MLOps)

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.

Itslearning and contact classes are the main communication channels used on this course.
The student is required to have a computer capable of running a simple Ubuntu virtual machine.

Teaching methods

Contact learning, practical assignments, independent study

Student workload

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

Content scheduling

After completing the course, the students can
- describe and understand how MLOps projects operate

Process and tools for Machine Learning Engineering for Production (MLOps)
Scheduling:Chosen topics from MLops, Dataops and Devops for artificial intelligence and data engineering students.

Evaluation scale

H-5

Assessment methods and criteria

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

Objective

After completing the course, the students can
- describe and understand how MLOps projects operate

Content

- Process and tools for Machine Learning Engineering for Production (MLOps)

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.

Itslearning and contact classes are the main communication channels used on this course.
The student is required to have a computer capable of running a simple Ubuntu virtual machine.

Teaching methods

Contact learning, practical assignments, independent study

Student workload

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

Content scheduling

After completing the course, the students can
- describe and understand how MLOps projects operate

Process and tools for Machine Learning Engineering for Production (MLOps)
Scheduling:Chosen topics from MLops, Dataops and Devops for artificial intelligence and data engineering students.

Evaluation scale

H-5

Assessment methods and criteria

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

Objective

After completing the course, the students can
- work in the Data Engineering project
- describe and understand how Data Engineering projects are implemented

Content

Practical project related to Data Engineering

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

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 data engineering methods and pipelines
-different components
-integration of said components (MQ systems)
-data engineering frameworks (Apache family)

Further information

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

The student is required to have a computer capable of running a simple Ubuntu virtual machine.

Evaluation scale

H-5

Assessment methods and criteria

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

Objective

After completing the course, the students can
- work in the AI project
- describe and understand how AI projects are implemented

Content

Practical project related to AI

Materials

The course materials will be announced later during the course.

Exam schedules

No Exam

Student workload

Contact hours:
- 4 times 4h theory and practice: 4 x 4h = 16 hours

Final project: approximately 114 hours

Total: approximately: 130 hours

Content scheduling

A deep learning-focused, project-based course that empowers master's students to explore and develop AI projects, fostering their ability to conceive, implement, and optimize intelligent solutions.

Session 1: AI Project Lifecycle and Data Selection
Session 2: Data Preprocessing and Model Selection
Session 3: Presenting First Results and Discussion
Session 4: Algorithm Optimization and Tuning

Further information

This course is project-based, requiring students to possess knowledge in machine learning and deep learning, specifically in image recognition and Sequential models.
Consequently, it is advisable to enroll in the 'Components and Applications of Artificial Intelligence' course first. In that course, students learn how to employ deep neural networks for image recognition (using CNN) and using RNN for sequential models. This foundational knowledge will better prepare students for the project-based nature of this course

Evaluation scale

Hyväksytty/Hylätty

Objective

After completing the course, the student can:
- learn about the history and background of Cloud Computing
- discover Cloud Computing including examples of real-world problems
- understand the most commonly used platforms for cloud computing such as Amazon AWS and Microsoft Azure (and possibly CSC Finland).
- understand the various Service models on Cloud as IaaS, PaaS, and SaaS
- learn the basic network security techniques in the cloud environment

Content

- Cloud computing
- Software-as-a-Service (SaaS)
- Platform-as-a-Service (PaaS)
- Infrastructure-as-a-Service (IaaS)
- Cloud computing technologies and tools
- Security in the cloud environment

Materials

Task-specific material to be announced separately in Its Learning

Teaching methods

- Self-paced learning 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

Self paced, FLIP classrooms and learning by doing

Completion alternatives

Self-paced learning

Student workload

Contact hours
- Course introduction: 4 hours
- Introduction to AWS academy: 4 hours
- Group Project Presentations: 2 X 4 hours
- 16 times AWS Academy self paced sessions: 16 x 2h = 32 hours
- 12 times 2h theory self paced: 12 x 2h = 24 hours
Home work:
- Working with assignments: approximately 80 hours

Total: approximately 142 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.

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 basic concepts and processes related to Data Engineering and AI

Content

- Data Engineering process
- Basics of AI
- Fields and evolution of AI
- Big data
- Basics of Machine Learning

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)

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

Additionally, some other materials will be available via the learning environment (ITS).

Teaching methods

- Short lectures are delivered by the teacher (theory and practice)
- Self-study tasks (theory and practice)
- Practical classwork
- Teamwork Project (including three assignments)

Exam schedules

No exam

Student workload

Contact hours:
- 4 times 3h theory and practice: 4 x 4h = 12 hours

Assignments for the final project: approximately 118 hours

Total: approximately: 130 hours

Content scheduling

Course Outline and Schedule: (preplan)
Session 1: Introduction to the Course, Data, and Data Preprocessing:
• Project overview and requisites
• Initiation of project's initial phase: Data preprocessing and visualization (assignment 1)
Session 2: Machine Learning I
• Students' presentations showcasing outputs and implementation of the first assignment
• Commencement of phase two: Supervised and unsupervised learning (assignment 2)
Session 3: Machine Learning II
• Students' presentations showcasing outputs and implementation of the second assignment
• Tuning and optimizing your Machine Learning algorithms (assignment 3)
Session 4: Students' presentations showcasing outputs and implementation of the third assignment, followed by discussions encompassing all project phases.

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 prerequisite courses and topics:

1. Python Programming: Proficiency in basic Python syntax and programming constructs, understanding of Object-Oriented Programming (OOP) concepts.

2. Data Management: Experience with data manipulation libraries such as Pandas for handling datasets. Data manipulation involves transforming data, cleaning it, organizing it, and preparing it for analysis.

3. Basic Linear Algebra: Understanding of vectors, matrices, and basic operations on them.

4. Statistics and Probability: Knowledge of descriptive statistics (mean, median, mode, variance, etc.), and familiarity with probability distributions.

Evaluation scale

H-5

Assessment methods and criteria

The course is graded on a scale of 0-5.
*
In order to receive an approved performance and pass the course, the student must receive an acceptable mark for the three assignments.
*
You can get at most 40, 30, and 30 points for each phase of the project (assignments 1-3). You can therefore get a maximum of 100 points from all phases of the project.
*
The passive students of the groups won't earn the assignment points.

Assessment criteria, fail (0)

Less than 50% in assignments not passed.

Assessment criteria, satisfactory (1-2)

50% - 65% from the total points of the assignments

Assessment criteria, good (3-4)

66% - 80% from the total points of the assignments

Assessment criteria, excellent (5)

81%- 100% from the total points of the assignments