Course programme 2021-2022 | EIT Digital Data Science exit 2021-2022 | Master Computer Science (CS): for students and staff

Academic year 2021-2022

Programme mentors: dr.ir. Maurice van Keulen

Name:

Student number:

Email address

At which university did you complete your entry year?

Is this your first time filling out a course form for this specialization?

Yes
Proceed by filling out the courses below.

No
If you want to change a previously submitted form, please briefly explain your changes in the field below, before proceeding by filling out the course form.

Brief explanation of the changes you're about to submit:

The DS programme contains a number of core (4) and advanced courses (4 out of 6). The intention is that students minimize the number of core and advanced courses they still have to do in their exit year, so that sufficient room for electives remain. Students are expected to show in the field below how the courses in their programme at the entry university cover the all or at least most of the core and advanced courses (preferably including a link to the course description). This has to be approved by the Programme mentor. If their are any uncovered core/advanced courses remaining, please do check them or discuss with your programme mentor first.

Example:
Machine learning techniques KHT (link: www.......) covers 201600070 Machine Learning 1 UT.......

How I covered the core and advanced course at my entry university:

Remaining DS Core courses:

201600070 Machine Learning 1 (1A, 5EC)

201200044 Managing Big Data (1B, 5EC)

201400174 Data Science (1B or 2A, 5EC)

201700080 Information Theory and Statistics (2A, 5EC)

Credits remaining core DS

Remaining DS Advanced courses (4 out of 6)

201600076 Foundations of Information Retrieval (1A, 5EC)

You cannot pick both NLP and Foundations of IR as advanced course. You can take both, but one will need to be part of the electives in your profiling space.

201600074 Natural Language Processing (1A, 5EC)

You cannot pick both NLP and Foundations of IR as advanced course. You can take both, but one will need to be part of the electives in your profiling space.

201600071 Machine Learning 2 (1B, 5EC)

201800177 Deep Learning – theory to practice (1B, 5EC)

191210910 Image Processing and Computer Vision (2A, 5EC)

201700081 Probabilistic programming (2A, 5EC)

192320111 Architecture of Information Systems (2B, 5EC)

Credits remaining Advanced Courses:

General Computer Science (mandatory)

191612680 Computer Ethics (1B, 5EC)

201800525 I&E minor EIT (6EC)

201800524 Research Topics EIT (4EC)

192199978 Final Project 30EC

Credits General Computer Science

Profiling space: For the remainder of the 60EC, the student needs to pick at least 15 EC from the following courses (if not spend on remaining core/advanced courses). These courses are specifically suggested for the EIT specialisation “Data Science for Persona Information”. They are course related to topics such as health and sports, wellbeing, biometrics and privacy. Any other course suggested for the profiling space of the Data Science & Technology programme is also allowed.

Profiling space: suggested electives

201600028 Telemedicine and Data Analysis for Monitoring

201500222 Technology for Health

201400353 Signals with Information

201500040 Introduction to Biometrics

201100254 Advanced Computer Vision & Pattern Recognition

201700075 Internet of Things

Profiling space: Other courses (course code, course name):

If you proposed "other courses" above, please explain how these form a coherent DS&T programme targeting your specific individual goals

Credits Profiling space

Total credits (EC):