Fundamentals of Data Science and Machine Learning (5 cr)
Code: IT00EW28-3002
General information
- Enrollment
-
01.08.2023 - 20.10.2023
Registration for the implementation has ended.
- Timing
-
23.10.2023 - 17.12.2023
Implementation has ended.
- Number of ECTS credits allocated
- 5 cr
- Local portion
- 5 cr
- Mode of delivery
- On-campus
- Unit
- (2019-2024) School of ICT
- Campus
- Karaportti 2
- Teaching languages
- English
- Seats
- 0 - 30
- Degree programmes
- Master's Degree Programme in Information Technology
- Teachers
- Peter Hjort
- Groups
-
T1623S6-NInformation Technology (MEng): Networking and Services
- Course
- IT00EW28
Implementation has 7 reservations. Total duration of reservations is 21 h 0 min.
| Time | Topic | Location |
|---|---|---|
|
Wed 25.10.2023 time 17:00 - 20:00 (3 h 0 min) |
Fundamentals of Data Science and Machine Learning IT00EW28-3002 |
KMD550
Oppimistila
|
|
Wed 01.11.2023 time 17:00 - 20:00 (3 h 0 min) |
Fundamentals of Data Science and Machine Learning IT00EW28-3002 |
KMD550
Oppimistila
|
|
Wed 08.11.2023 time 17:00 - 20:00 (3 h 0 min) |
Fundamentals of Data Science and Machine Learning IT00EW28-3002 |
KMD550
Oppimistila
|
|
Wed 15.11.2023 time 17:00 - 20:00 (3 h 0 min) |
Fundamentals of Data Science and Machine Learning IT00EW28-3002 |
KMD550
Oppimistila
|
|
Wed 22.11.2023 time 17:00 - 20:00 (3 h 0 min) |
Fundamentals of Data Science and Machine Learning IT00EW28-3002 |
KME659
Oppimistila
|
|
Wed 29.11.2023 time 17:00 - 20:00 (3 h 0 min) |
Fundamentals of Data Science and Machine Learning IT00EW28-3002 |
KMD550
Oppimistila
|
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Wed 13.12.2023 time 17:00 - 20:00 (3 h 0 min) |
Fundamentals of Data Science and Machine Learning IT00EW28-3002 |
KMD550
Oppimistila
|
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