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
• Linear Algebra for Data Science, 5 credits (1MA330) Main field(s) of study and in-depth level: Data Science A1N, Mathematics A1N

Bridging courses:

Course for students with a background in engineering:

• Computer Programming II, 5 credits (1TD722) Main field(s) of study and in-depth level: Computer Science G1F, Technology G1F

Course for students with a background in computer science:

• Industrial Project Management I, 5 credits (1TS327) Main field(s) of study and in-depth level: Industrial Engineering and Management A1N, Technology A1N

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
• Industrial Production Philosophies, 5 credits (1TS306) Main field(s) of study and in-depth level: Industrial Engineering and Management A1N

• Database Design I, 5 credits (1DL301) Main field(s) of study and in-depth level: Computer Science G2F, Sociotechnical Systems G2F, Technology G2F

• Database Design II, 5 credits (1DL400) Main field(s) of study and in-depth level: Computer Science 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
• 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
• Statistical Inference for Technological Applications, 5 credits (1TS325) Main field(s) of study and in-depth level: Bioinformatics A1N, Industrial Engineering and Management A1N, Technology A1N

Eligible course:

• 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

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

Period 1

Recommended course

• 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

Eliglible course in industrial analytics, depending on previous studies: 

• Data Mining I, 5 credits (1DL360) Main field(s) of study and in-depth level: Computer Science A1N, Technology A1N

option

• Data Mining II, 5 credits (1DL460) Main field(s) of study and in-depth level: Computer Science A1F, Technology A1F

Course for students who follow the Industrial Engineering and Management-track:

• Industrial Project with Extended Reality, 5 of 10 credits (1TS334) Main field(s) of study and in-depth level: Industrial Engineering and Management A1F, Technology A1F

Course for students who follow the Computer Science-track:

• 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 in Industrial Engineering and Management I, 5 credits (1TS338)

Course for students who follow the Industrial Engineering and Management-track:

• Industrial Project with Extended Reality, 5 of 10 credits (1TS334) Main field(s) of study and in-depth level: Industrial Engineering and Management A1F, Technology A1F

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

Semester 3-4, or semester 4: A degree project within Industrial Analytics depending on prior knowledge.

Semester 4

• Degree Project in Industrial Analytics: Industrial Engineering and Management, 30 credits (1TS339) Main field(s) of study and in-depth level: Industrial Engineering and Management A2E
• Degree Project in Industrial Analytics: Computer Science, 30 credits (1DT112) Main field(s) of study and in-depth level: Computer Science A2E