Master's Programme in Data Science – Machine Learning and Statistics
Outline, TDA2M, MAST
- Code
- TDA2M
- Specialisation code
- MAST
- Finalised by
- The Faculty Board of Science and Technology, 22 October 2019
- Registration number
- TEKNAT 2019/267
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
Eligible courses:
- Computer Programming II, 5 credits (1TD722) Main field(s) of study and in-depth level: Computer Science G1F, Technology G1F
- Algorithms and Data Structures I, 5 credits (1DL210) Main field(s) of study and in-depth level: Computer Science G1F, Technology G1F
- Modelling for Combinatorial Optimisation, 5 credits (1DL451) Main field(s) of study and in-depth level: Computer Science A1N, Data Science A1N
- Scientific Computing II, 5 credits (1TD395) Main field(s) of study and in-depth level: Computer Science G1F, Technology G1F
- Linear Algebra II (for Master's Students), 5 credits (1MA323) Main field(s) of study and in-depth level: Mathematics G1F
- Probability Theory II, 5 credits (1MS036) Main field(s) of study and in-depth level: Mathematics G2F
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
Eligible courses:
- Database Design I, 5 credits (1DL301) Main field(s) of study and in-depth level: Computer Science G2F, Sociotechnical Systems G2F, Technology G2F
- Optimisation, 5 credits (1TD184) Main field(s) of study and in-depth level: Computational Science A1N, Computer Science A1N, Data Science A1N, Technology A1N
- Several Variable Calculus, Limited Version (for Master's Students), 5 credits (1MA324) Main field(s) of study and in-depth level: Mathematics G1F
- Foundations of Mathematical Analysis, 5 credits (1MA322) Main field(s) of study and in-depth level: Mathematics G2F
- Inference Theory II, 5 credits (1MS037) Main field(s) of study and in-depth level: Mathematics G2F
Semester 2
Period 3
- Data Engineering I, 7.5 credits (1TD069) Main field(s) of study and in-depth level: Computational Science A1N, Computer Science A1N, Data Science A1N, Technology A1N
- Theoretical Foundations for Data Science, 7.5 credits (1MS042) Main field(s) of study and in-depth level: Data Science A1N, Mathematics A1N
Eligible courses:
- Artificial Intelligence, 7.5 credits (1DL010) Main field(s) of study and in-depth level: Computer Science A1N
- Computer-Intensive Statistics and Data Mining DS, 7.5 credits (1MS043) Main field(s) of study and in-depth level: Computer Science A1N, Data Science A1N, Mathematics A1N
- Bayesian Statistics DS, 7.5 credits (1MS031) Main field(s) of study and in-depth level: Mathematics A1N
1MS043 is given even years
1MS031 is given odd years
Period 4
- Data Engineering II, 7.5 credits (1TD075) Main field(s) of study and in-depth level: Computational Science A1F, Computer Science A1F, Data Science A1F
- Reinforcement Learning, 7.5 credits (1RT747) Main field(s) of study and in-depth level: Computer Science A1N, Data Science A1N, Embedded Systems A1N, Image Analysis and Machine Learning A1N
Semester 3
YEAR 2, PRELIMINARY ACADEMIC YEAR 2021/2022
Project in Data Science, 15 credits
Advanced Probabilistic Machine Learning, 7.5 credits
Theoretical Statistics, 7.5 credits (given only in even years)
Scientific Visualisation, 7.5 credits
Semester 4
YEAR 2, PRELIMINARY ACADEMIC YEAR 2021/2022
Degree Project E in Data Science, 30 credits
Programme syllabus
- Programme syllabus valid from Autumn 2025
- 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