Opinto-opas

Tavoitteet

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

Sisältö

Practical project related to AI

Oppimateriaalit

See the English side.

Tenttien ajankohdat ja uusintamahdollisuudet

See the English side.

Opiskelijan ajankäyttö ja kuormitus

See the English side.

Sisällön jaksotus

See the English side.

Viestintäkanava ja lisätietoja

See the English side.

Arviointiasteikko

Hyväksytty/Hylätty

Tavoitteet

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

Sisältö

Practical project related to Data Engineering

Oppimateriaalit

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.

Opetusmenetelmät

Contact learning, practical exercises, independent study

Pedagogiset toimintatavat ja kestävä kehitys

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

Opiskelijan ajankäyttö ja kuormitus

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

Sisällön jaksotus

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

Viestintäkanava ja lisätietoja

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.

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

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

Tavoitteet

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

Sisältö

- 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

Oppimateriaalit

Task-specific material to be announced separately in Its Learning

Opetusmenetelmät

- 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

Tenttien ajankohdat ja uusintamahdollisuudet

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

Pedagogiset toimintatavat ja kestävä kehitys

Self paced, FLIP classrooms and learning by doing

Toteutuksen valinnaiset suoritustavat

Self-paced learning

Opiskelijan ajankäyttö ja kuormitus

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

Sisällön jaksotus

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

Viestintäkanava ja lisätietoja

Course material and assignments in Its Learning and AWS academy.

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

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

Hylätty (0)

Fail < 50 points

Arviointikriteerit, tyydyttävä (1-2)

50 points -> 1
60 points -> 2

Arviointikriteerit, hyvä (3-4)

70 points -> 3
80 points -> 4

Arviointikriteerit, kiitettävä (5)

90 points -> 5

Tavoitteet

After completing the course, the student can:
- describe basic concepts and processes related to Data Engineering and AI

Sisältö

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

Oppimateriaalit

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).

Opetusmenetelmät

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

Tenttien ajankohdat ja uusintamahdollisuudet

No exam

Opiskelijan ajankäyttö ja kuormitus

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

Sisällön jaksotus

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.

Viestintäkanava ja lisätietoja

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.

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

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.

Hylätty (0)

Less than 50% in assignments not passed.

Arviointikriteerit, tyydyttävä (1-2)

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

Arviointikriteerit, hyvä (3-4)

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

Arviointikriteerit, kiitettävä (5)

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

Tavoitteet

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

Sisältö

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

Oppimateriaalit

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.

Opetusmenetelmät

Contact learning, practical assignments, independent study

Opiskelijan ajankäyttö ja kuormitus

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

Sisällön jaksotus

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.

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

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

Tavoitteet

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

Sisältö

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

Oppimateriaalit

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.

Opetusmenetelmät

Contact learning, practical assignments, independent study

Opiskelijan ajankäyttö ja kuormitus

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

Sisällön jaksotus

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.

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

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

Tavoitteet

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

Sisältö

Practical project related to Data Engineering

Oppimateriaalit

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.

Opetusmenetelmät

Contact learning, practical exercises, independent study

Pedagogiset toimintatavat ja kestävä kehitys

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

Opiskelijan ajankäyttö ja kuormitus

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

Sisällön jaksotus

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

Viestintäkanava ja lisätietoja

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.

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

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

Tavoitteet

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

Sisältö

Practical project related to AI

Oppimateriaalit

See the English side.

Tenttien ajankohdat ja uusintamahdollisuudet

See the English side.

Opiskelijan ajankäyttö ja kuormitus

See the English side.

Sisällön jaksotus

See the English side.

Viestintäkanava ja lisätietoja

See the English side.

Arviointiasteikko

Hyväksytty/Hylätty

Tavoitteet

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

Sisältö

- 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

Oppimateriaalit

Task-specific material to be announced separately in Its Learning

Opetusmenetelmät

- 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

Tenttien ajankohdat ja uusintamahdollisuudet

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

Pedagogiset toimintatavat ja kestävä kehitys

Self paced, FLIP classrooms and learning by doing

Toteutuksen valinnaiset suoritustavat

Self-paced learning

Opiskelijan ajankäyttö ja kuormitus

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

Sisällön jaksotus

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

Viestintäkanava ja lisätietoja

Course material and assignments in Its Learning and AWS academy.

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

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

Hylätty (0)

Fail < 50 points

Arviointikriteerit, tyydyttävä (1-2)

50 points -> 1
60 points -> 2

Arviointikriteerit, hyvä (3-4)

70 points -> 3
80 points -> 4

Arviointikriteerit, kiitettävä (5)

90 points -> 5

Tavoitteet

After completing the course, the student can:
- describe basic concepts and processes related to Data Engineering and AI

Sisältö

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

Oppimateriaalit

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).

Opetusmenetelmät

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

Tenttien ajankohdat ja uusintamahdollisuudet

No exam

Opiskelijan ajankäyttö ja kuormitus

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

Sisällön jaksotus

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.

Viestintäkanava ja lisätietoja

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.

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

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.

Hylätty (0)

Less than 50% in assignments not passed.

Arviointikriteerit, tyydyttävä (1-2)

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

Arviointikriteerit, hyvä (3-4)

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

Arviointikriteerit, kiitettävä (5)

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