Opinto-opas

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

Arviointiasteikko

H-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

Material will be available via the learning environment (ITS) and our institution's eBook Central database.

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:
Week 37: Introduction to the Course and Data Preprocessing:
• Project overview and requisites
• Initiation of project's initial phase: Data preprocessing and visualization (assignment 1)
Week 41: Machine Learning I
• Students' presentations showcasing outputs and implementation of the first assignment
• Commencement of phase two: Supervised and unsupervised learning (assignment 2)
Week 45: Machine Learning II
• Students' presentations showcasing outputs and implementation of the second assignment
• Tuning and optimizing your Machine Learning algorithms (assignment 3)

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 points for each phase of the project (assignments 1-3). You can therefore get a maximum of 120 points from all phases of the project.

Hylätty (0)

Less than 60 points in assignments not passed.

Arviointikriteerit, tyydyttävä (1-2)

60 - 79 points from the total points of the assignments

Arviointikriteerit, hyvä (3-4)

80 - 99 points from the total points of the assignments

Arviointikriteerit, kiitettävä (5)

100 - 120 points from the total points of the assignments

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

Arviointiasteikko

H-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

Material will be available via the learning environment (ITS) and our institution's eBook Central database.

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:
Week 37: Introduction to the Course and Data Preprocessing:
• Project overview and requisites
• Initiation of project's initial phase: Data preprocessing and visualization (assignment 1)
Week 41: Machine Learning I
• Students' presentations showcasing outputs and implementation of the first assignment
• Commencement of phase two: Supervised and unsupervised learning (assignment 2)
Week 45: Machine Learning II
• Students' presentations showcasing outputs and implementation of the second assignment
• Tuning and optimizing your Machine Learning algorithms (assignment 3)

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 points for each phase of the project (assignments 1-3). You can therefore get a maximum of 120 points from all phases of the project.

Hylätty (0)

Less than 60 points in assignments not passed.

Arviointikriteerit, tyydyttävä (1-2)

60 - 79 points from the total points of the assignments

Arviointikriteerit, hyvä (3-4)

80 - 99 points from the total points of the assignments

Arviointikriteerit, kiitettävä (5)

100 - 120 points from the total points of the assignments