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Data-Analysis and Machine Learning Basics (4 cr)

Code: TTC8020-3010

General information


Enrollment
18.11.2024 - 09.01.2025
Registration for the implementation has ended.
Timing
24.03.2025 - 30.04.2025
Implementation is running.
Number of ECTS credits allocated
4 cr
Local portion
0 cr
Virtual portion
4 cr
Mode of delivery
Distance learning
Unit
School of Technology
Teaching languages
English
Seats
0 - 70
Degree programmes
Bachelor's Degree Programme in Information and Communications Technology
Bachelor's Degree Programme in Information and Communications Technology
Teachers
Juha Peltomäki
Groups
TTV22S5
Tieto- ja viestintätekniikka (AMK)
TTV22S2
Tieto- ja viestintätekniikka (AMK)
TTV22S3
Tieto- ja viestintätekniikka (AMK)
TIC22S1
Bachelor's Degree Programme in Information and Communications Technology
TTV22S1
Tieto- ja viestintätekniikka (AMK)
TTV22SM
Tieto- ja viestintätekniikka (AMK)
TTV22S4
Tieto- ja viestintätekniikka (AMK)
TTV22SM2
Tieto- ja viestintätekniikka (AMK)
ZJA25KTIDA1
Avoin amk, Data-analytiikka 1, Verkko
Course
TTC8020

Materials

Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana.

Evaluation scale

0-5

Completion alternatives

The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.

Further information

The course is evaluated on the basis of the assignments, which must be returned by the given timetables.
The assessment methods are reviewed at the beginning of the course.

Employer connections

The aim is to connect the content of the course to problems that occur in working life.

Virtual portion

4

Student workload

The workload of one credit corresponds to 27 hours of study. The total amount of study work (4 ECTS) in the course is 108 hours.

Assessment criteria, satisfactory (1)

Satisfactory 2: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project. Additionally, the student knows their implementation at a cursory level and is able to validate their conclusions.

Sufficient 1: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project at a cursory level. Additionally, the student is able to assess their implementation and conclusions.

Assessment criteria, good (3)

Very good 4: The student knows the various phases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of data analytics and machine learning project regardless of the problem to be solved. In addition, the student is able to assess their implementation and validate the conclusions.

Good 3: The student knows the variousphases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess their implementation in a versatile manner and to validate the conclusions.

Assessment criteria, excellent (5)

Excellent 5: The student knows the various phases of a data analytics and machine learning project and is able to systematically proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess critically their implementation and validate the conclusions.

Teaching language

en

Teaching methods

Virtual study including doing assignments and familiarizing yourself with related lecture and example materials. Assignments are mainly done as group work.

Location and time

The course will be implemented in the spring semester of 2025.

Number of ECTS credits allocated

4

Content

- Structure and implementation of a data-based project
- Data analytics and machine learning practices
- The concepts of the teaching and test data set and the most common ways of splitting them
- Documentation and visualization of the data-based project
- Introduction to data analytics and machine learning's most common tools and practical skills needed

Objective

You understand the practices of data analytics and machine learning and the structure and flow of the project. You understand how a data-based project is designed, built and implemented. You will also recognize the key terminology and most common practices of data-based projects. You understand the importance of data visualization. You know the concepts of the teaching and test dataset and the most common ways of splitting them. You will get basic information about the data analytics and machine learning tools used.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice

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