Fundamentals of Data Science and Machine Learning (5 ECTS)

Code: IT00EW28-3004

General information

Enrollment: 05.05.2025 - 19.10.2025; Enrollment is ongoing
Enroll to the implementation in OMA

Timing: 20.10.2025 - 14.12.2025; The implementation has not yet started.

Number of ECTS credits allocated: 5 ECTS

Mode of delivery: On-campus

Unit: School of ICT and Industrial Management

Campus: Karaportti 2

Teaching languages: English

Seats: 0 - 40

Degree programmes: Master's Degree Programme in Information Technology

Teachers: Peter Hjort

Teacher in charge: Peter Hjort

Groups: T1625S6-N
Information Technology (MEng): Networking and Services

Course: IT00EW28

Implementation has 8 reservations. Total duration of reservations is 24 h 0 min.

Time	Topic	Location
Wed 22.10.2025 time 17:00 - 20:00 (3 h 0 min)	Fundamentals of Data Science and Machine Learning IT00EW28-3004	KMD550 Oppimistila
Wed 29.10.2025 time 17:00 - 20:00 (3 h 0 min)	Fundamentals of Data Science and Machine Learning IT00EW28-3004	KMD550 Oppimistila
Wed 05.11.2025 time 17:00 - 20:00 (3 h 0 min)	Fundamentals of Data Science and Machine Learning IT00EW28-3004	KMD550 Oppimistila
Wed 12.11.2025 time 17:00 - 20:00 (3 h 0 min)	Fundamentals of Data Science and Machine Learning IT00EW28-3004	KMD550 Oppimistila
Wed 19.11.2025 time 17:00 - 20:00 (3 h 0 min)	Fundamentals of Data Science and Machine Learning IT00EW28-3004	KMD550 Oppimistila
Wed 26.11.2025 time 17:00 - 20:00 (3 h 0 min)	Fundamentals of Data Science and Machine Learning IT00EW28-3004	KMD550 Oppimistila
Wed 03.12.2025 time 17:00 - 20:00 (3 h 0 min)	Fundamentals of Data Science and Machine Learning IT00EW28-3004	KMD550 Oppimistila
Wed 10.12.2025 time 17:00 - 20:00 (3 h 0 min)	Fundamentals of Data Science and Machine Learning IT00EW28-3004	KMD550 Oppimistila

Changes to reservations may be possible.

Objective

After completing the course the student has an understanding of the methods and Python packages for processing, visualising and analysing data for data science and machine learning applications. The student is able to use data coming from different sources in different formats, perform basic statistical analysis of data and visualise it. Basic tasks in creating models to make predictions based on data will also become familiar.

Content

• Python programming language and its use in processing data
• Tools for the analysis and visualisation of data, basic statistical methods
• Building models for making predictions

Evaluation scale

0-5

Assessment criteria, satisfactory (1)

The student understands the methods and tools for data science and machine learning and is able to apply them in most typical settings.

Assessment criteria, good (3)

In addition to satisfactory criteria, the student demonstrates ability to solve some more demanding problems in the field. The student has a fair understanding of the limitations of the methods and models.

Assessment criteria, excellent (5)

In addition to good criteria, the student is able to apply new knowledge to data science and machine learning tasks. They understand the limitations of the methods and are able to critically assess the outcomes.

Qualifications

No requirements