Master's Programme in Data Science – Data Engineering
120 credits
Outline, TDA2M, DAEN
- Code
- TDA2M
- Specialisation code
- DAEN
- Finalised by
- The Faculty Board of Science and Technology, 6 November 2024
- Registration number
- TEKNAT 2024/125
The following designations are used:
G1N - First cycle, has only upper-secondary level entry requirements
G1F - First cycle, has less than 60 credits in first-cycle courses as entry requirements
G2F - First cycle, has at least 60 credits in first-cycle courses as entry requirements
A1N - Second cycle, has only first-cycle courses as entry requirements, at least 120 credits
A1F - Second cycle, has second-cycle courses as entry requirements
A2E - Second cycle, degree project for Master of Arts/Master of Science (120 credits)
Semester 1
Period 1
- Introduction to Data Science, 5 of 10 credits (1MS041) Main field(s) of study and in-depth level: Computer Science A1N, Data Science A1N, Mathematics A1N
- Data, Ethics and Law, 5 credits (1DL002) Main field(s) of study and in-depth level: Computer Science A1N, Data Science A1N, Human-Computer Interaction A1N, Image Analysis and Machine Learning A1N
- Linear Algebra for Data Science, 5 credits (1MA330) Main field(s) of study and in-depth level: Data Science A1N, Mathematics A1N
Eligible course:
- Database Design I, 5 credits (1DL301) Main field(s) of study and in-depth level: Computer Science G2F, Sociotechnical Systems G2F, Technology G2F
- Software Engineering and Project Management, 5 credits (1DL251) Main field(s) of study and in-depth level: Computer Science A1N, Data Science A1N, Technology A1N
Period 2
- Introduction to Data Science, 5 of 10 credits (1MS041) Main field(s) of study and in-depth level: Computer Science A1N, Data Science A1N, Mathematics A1N
- Statistical Machine Learning, 5 credits (1RT700) Main field(s) of study and in-depth level: Computer Science A1N, Data Science A1N, Image Analysis and Machine Learning A1N, Mathematics A1N, Technology A1N
- Database Design I, 5 credits (1DL301) Main field(s) of study and in-depth level: Computer Science G2F, Sociotechnical Systems G2F, Technology G2F
Eligable course:
- Database Design II, 5 credits (1DL400) Main field(s) of study and in-depth level: Computer Science A1N, Data Science A1N, Technology A1N
Semester 2
Period 3
- Data Engineering I, 5 credits (1TD169) Main field(s) of study and in-depth level: Computational Science A1N, Computer Science A1N, Data Science A1N, Technology A1N
- High Performance Programming, 10 credits (1TD062) Main field(s) of study and in-depth level: Computational Science A1N, Computer Science A1N, Data Science A1N, Technology A1N
- Natural Computation Methods for Machine Learning, 5 of 10 credits (1DL073) Main field(s) of study and in-depth level: Computer Science A1N, Data Science A1N
Period 4
- Data Engineering II, 10 credits (1TD076) Main field(s) of study and in-depth level: Computational Science A1F, Computer Science A1F, Data Science A1F, Technology A1F
- Data Security and Privacy, 5 credits (1DT114) Main field(s) of study and in-depth level: Computer Science A1N, Data Science A1N, Technology A1N
- Natural Computation Methods for Machine Learning, 5 of 10 credits (1DL073) Main field(s) of study and in-depth level: Computer Science A1N, Data Science A1N
- Degree Project D in Data Science, 15 credits (1DL390) Main field(s) of study and in-depth level: Data Science A1E
Semester 3
Period 1
- Project in Data Science, 5 of 10 credits (1DL508) Main field(s) of study and in-depth level: Data Science A1F
- Data Mining I, 5 credits (1DL360) Main field(s) of study and in-depth level: Computer Science A1N, Data Science A1N, Technology A1N
- Accelerator-Based Programming, 5 credits (1TD054) Main field(s) of study and in-depth level: Computational Science A1F, Computer Science A1F, Data Science A1F
Period 2
- Project in Data Science, 5 of 10 credits (1DL508) Main field(s) of study and in-depth level: Data Science A1F
- Mining of Social Data, 10 credits (1DL465) Main field(s) of study and in-depth level: Computer Science A1F, Data Science A1F
Semester 4
- Degree Project E in Data Science, 30 credits (1DL510) Main field(s) of study and in-depth level: Data Science A2E
Programme syllabus
- Programme syllabus valid from Autumn 2025, version 2
- Programme syllabus valid from Autumn 2025, version 1
- Programme syllabus valid from Autumn 2024
- Programme syllabus valid from Autumn 2023, version 2
- Programme syllabus valid from Autumn 2023, version 1
- Programme syllabus valid from Autumn 2022, version 2
- Programme syllabus valid from Autumn 2022, version 1
- Programme syllabus valid from Autumn 2021, version 2
- Programme syllabus valid from Autumn 2021, version 1
- Programme syllabus valid from Autumn 2020