Master's Programme in Industrial Analytics
Outline, TIA2M
- Code
- TIA2M
- Finalised by
- The Faculty Board of Science and Technology, 22 October 2019
- Registration number
- TEKNAT 2019/267
Semester 1
Period 1
- 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
- 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:
- Production Management I, 5 credits (1MI087) Main field(s) of study and in-depth level: Technology G2F
Semester 2
Period 3
- 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
- Data Mining I, 5 credits (1DL360) Main field(s) of study and in-depth level: Computer Science A1N, Technology A1N
Period 4
- 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, PRELIMINARY ACADEMIC YEAR 2021/2022
Semester 3
Organising Knowledge-Intensive Work, 5 credits
Engineering Ethics, 5 credits
Data Mining II, 5 credits
Advanced Simulation and Prescriptive Analytics, 5 credits
Scientific Method, 5 credits
Eligible courses within the field of Industrial Analytics (depending on prior knowledge):
Courses for students who follow the Industrial Engineering and Management-track:
Industrial Project with Mixed Reality, 5 credits
Database Design II, 5 credits
Courses for students who follow the Computer Science-track:
Statistical Machine Learning, 5 credits
Data Engineering II, 5 credits
Semester 3-4
Eligible courses within the field of Industrial Analytics (depending on prior knowledge):
Degree Project in Industrial Engineering and Management, 30 hp
Degree Project in Computer Science, 30 hp