Master's Programme in Industrial Analytics
120 credits
Outline, TIA2M
A revised version of the outline is available.
- Code
- TIA2M
- Finalised by
- The Faculty Board of Science and Technology, 9 November 2020
- Registration number
- TEKNAT 2020/258
Semester 1
Period 1
Recommended courses
- Introduction to Industrial Analytics, 5 credits (1TS304) Main field(s) of study and in-depth level: Industrial Engineering and Management A1N
- Industrial Production Philosophies, 5 credits (1TS306) Main field(s) of study and in-depth level: Industrial Engineering and Management A1N
Course for students who follow the Industrial Engineering and Management-track:
- Computer Programming II, 5 credits (1TD722) Main field(s) of study and in-depth level: Computer Science G1F, Technology G1F
Course for students who follow the Computer Science-track:
- Industrial Management, 5 credits (1TE743) Main field(s) of study and in-depth level: Industrial Engineering and Management G1F
Period 2
Recommended courses
- Systems Thinking and Modelling, 5 credits (1TS305) Main field(s) of study and in-depth level: Industrial Engineering and Management A1N
Course for students who follow the Industrial Engineering and Management-track:
- Database Design I, 5 credits (1DL301) Main field(s) of study and in-depth level: Computer Science G2F, Sociotechnical Systems G2F, Technology G2F
Course for students who follow the computer science-track:
- Database Design II, 5 credits (1DL400) Main field(s) of study and in-depth level: Computer Science A1N, Technology A1N
Course for students who follow the Industrial Engineering and Management-track:
- Scientific Computing I, 5 credits (1TD393) Main field(s) of study and in-depth level: Computer Science G1F, Mathematics G1F, Technology G1F
Course for students who follow the Computer Science-track:
- Innovation Management, 5 credits (1TE736) Main field(s) of study and in-depth level: Industrial Engineering and Management A1N
- Technology-Based Entrepreneurship, 5 credits (1TE777) Main field(s) of study and in-depth level: Industrial Engineering and Management A1N, Technology A1N
Semester 2
Period 3
Recommended courses
- Operations and Supply Chain Management, 5 credits (1TE764) Main field(s) of study and in-depth level: Industrial Engineering and Management A1N
- 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
- Statistical Inference for Industrial Analytics, 5 credits (1TS322) Main field(s) of study and in-depth level: Industrial Engineering and Management 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
Period 4
Recommended courses
- Logistics Systems Modelling and Optimisation, 5 credits (1TE776) Main field(s) of study and in-depth level: Industrial Engineering and Management A1N, Technology A1N
- Internet of Things, 5 credits (1DT094) Main field(s) of study and in-depth level: Computer Science G1F, Technology G1F
- Industrial Optimisation Methods, 5 credits (1TS307) Main field(s) of study and in-depth level: Industrial Engineering and Management A1N
Semester 3
YEAR 2 ACADEMIC YEAR 2021/2022
Recommended courses
Period 1
- Organising Knowledge-Intensive Work, 5 credits (1TE779) Main field(s) of study and in-depth level: Industrial Engineering and Management A1N
- Data Mining II, 5 credits (1DL460) Main field(s) of study and in-depth level: Computer Science A1F, Technology A1F
- Artificial Intelligence for Industrial Analytics, 5 credits (1TS321) Main field(s) of study and in-depth level: Computer Science A1N, Industrial Engineering and Management A1N, Technology A1N
- Advanced Probabilistic Machine Learning, 5 credits (1RT705) Main field(s) of study and in-depth level: Computer Science A1F, Mathematics A1F, Technology A1F
Period 2
Recommended courses
- Advanced Simulation and Prescriptive Analytics, 5 credits (1TS316) Main field(s) of study and in-depth level: Computer Science A1F, Industrial Engineering and Management A1F, Technology A1F
- Qualitative and Quantitative Methods for Industrial Analytics, 5 credits (1TS317) Main field(s) of study and in-depth level: Industrial Engineering and Management A1F
- Industrial Project with Mixed Reality, 5 credits (1TS315) Main field(s) of study and in-depth level: Industrial Engineering and Management A1F
- 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 II, 5 credits (1DL400) Main field(s) of study and in-depth level: Computer Science A1N, Technology A1N
Semester 3-4
A degree project within Industrial Analytics depending on prior knowledge:
- Degree Project in Industrial Engineering and Management, 30 credits (1TE962) Main field(s) of study and in-depth level: Industrial Engineering and Management A2E
- Degree Project E in Computer Science, 30 credits (1DT540) Main field(s) of study and in-depth level: Computer Science A2E