Data Science and Technology (DST)

The Data Science and Technology (DST) master specialization connects the important fields of data science and smart services via information systems. In this master specialization you will get acquainted with and work on the following topics: big data, data analytics, information inference, context-aware applications, smart services. With data science, one learns how to dig for value in data by analyzing various data sources. With service engineering, one learns how to design services that effectively use system capabilities to satisfy user needs and requirements. Information systems that can use the results of data science to get more value out of data and become context-aware may turn traditional services into smart services. We already see applications of this in various domains such as pervasive health, well-being, intelligent transportation, logistics, and business intelligence. Within the Data Science and Technology track we also offer students the option to specialise in Sports Data Science combining data science with courses human movement science and sports.

specialization courses

In addition to the programme below, you must also comply to the overall programme requirements of Computer Science by completing the following mandatory components:

Data Science and technology (DST) and sports Data Science (SDS)

Within Data Science and Technology you can either opt for the regular track or the Sports Data Science track. There is a difference in advanced courses. Furthermore students in the Sports Data Science track use the profiling space to further specialise in Human movement science, by completing at least 27 credits of the mandatory sports data science courses. 

Core courses (DST and SDS)

The following 4 courses are mandatory:

Advanced courses DST

At least 4 courses should be chosen out of the following:

Advanced courses SDS

At least 4 courses should be chosen out of the following:

profiling space

Requirements

DST: no additional requirements
SDS: students need to complete the mandatory SDS profiling courses 

Data Science profiles DST

a) specialist in specific kinds of data, such as natural language text, image data, geographic data, sensor data, networked data
b) designer of smart services
c) designer of data science algorithms
d) multi-disciplinary researcher
e) specialist in sports and human movement data, devices and measurement techniques

The following are suggested courses for the profiling space:
(a) 201600074 Natural Language Processing (1A) (if not part of the advanced courses)
(a) 201600076 Foundations of Information Retrieval (1A if not part of the advanced courses)
(a) 201600083 Advanced Information Retrieval (1B)
a) 201600081 Advanced Natural Language Processing (1B)
(a) 201600075 Speech Processing(1B) 
(a) 201600082 Advanced Speech processing (2A)
(a,b) 201700075 Internet of Things (1A)
a,b) 202100263 Linked Data and Semantic Web
(a,b) 201500042 Privacy-Enhancing Technologies (2B) 
(a,b,c,d,e) 201300074 Research Experiments in Databases and Information Retrieval (2B)
(a,b,c,d,e) 202000029 Empirical and Design Science Research in Information Systems (2A, 5EC)
(a,b,c,d,e) 201500527 Capita Selecta DS
(a,c) 201800222 Complex Networks (1A)
a,c) 201500040 Introduction to Biometrics
(a,c) 201700364 Spatial Statistics (2B)
(a,d) 201800063 Traffic Forecasting and Analysis (1B)
(a,d) 201500363 Data Science Additional Topics (1B, 2A)
(a,d) 202100107 Deep Learning for 3D Medical Image Analysis (2B)
(a,d,e) 193810020 Advanced Techniques for Signal Analysis (2A)
(a,e) 201100254 Advanced Computer Vision and Pattern Recognition (2B)
(b) 192376500 Business Process Integration Lab (1B)
(b) 192320501 Electronic Commerce (1B)
(b) 202000027 Enterprise Security (1B, 5EC)
(b) 201400277 Enterprise Architecture (1A, 5EC)
(b) 191820210 Simulation (1A, 5EC)
(b) 201100051 Information Services (2A, 5EC)
(b) 192652150 Service-oriented Architecture with Web services (2A)
(b,d) 202100258 FAIR Principles and the FAIRification process (2B)
(b,d) 202000028 Smart Industry (2B)
(b,d) 201600028 Telemedicine and Data Analysis for Monitoring (1B)
(c) 192135310 Modeling and Analysis of Concurrent Systems 1 (1A) 
(c) 191506103 Statistics and Probability (1A, 5EC)
(c) 201400353 Signals with Information (1B) 
(c) 201900115 Statistical Learning (1A, 5EC)
(c) 191520751 Graph Theory (2A)
(c) 192111092 Advanced Logic (2B) 
(c,e) 191571090 Time Series Analysis (2B) 
(c) other courses on fundamentals and algorithms of signal processing, stochastic processing, etc.
(d) 201700196 Advanced Simulation for Health Economic Analysis (2A, 5EC)
(d,e) 202001583 Sports Interaction Technology: Designing Interactive Systems for Sports (2B, 5EC)
(d) other courses on data analysis from fields like health/medicine, social sciences, business sciences, bio-informatics, engineering.

profiling space SDS

The following are mandatory profiling courses on Human Movement Sciences for the SDS sub-specialization. They are provided by teachers of the VU Amsterdam. These courses are specifically aimed at students sports Data Science as part of their programme and not advised to be taken as individual courses.

Mandatory:

Optional: 202100145 Electromyography (3EC)

EIT DS

The requirements for the specialization in Data Science and Technology can also be fulfilled by completing the DST programme at the EIT Digital Masterschool, one year of which takes place at the University of Twente.

EIT Entry year

For the EIT Entry year, the same requirements exist in core and advanced courses as for regular DST track above (combining SDS with EIT is not possible). Additionally, there are some extra EIT requirements on Innovation and Entrepreneurship.

Mandatory core and advanced courses: 191612680 computer ethics, DST core and DST advanced courses

Mandatory I&E courses EIT:

Additional electives: See DST profiling courses or
the following I&E elective courses:
201700019 Brand Management
201800077 Bioresource Business Development & Management
201800079 Bioresource Supply Chain Management
201600155 Global Strategy and Business Development
194105070 Information Systems for the Financial Services Industry
201500008 Empirical Methods for Designers

EIT Exit YEAR

Exit year students have completed a programme in the entry year at one of our partner universities. Nevertheless, students need to comply with our requirements for a core and advanced programme (see below). Students are expected to show how the courses in their programme at the entry university cover at least most of the core and advanced courses with at least the same amount of EC as the core and advanced courses at the UT. This has to be approved by the Programme mentor. 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. A grade transcript of the entry university needs to be provided in the end to prove that the covering courses have at least the same amount of ECs and have been passed.

Mandatory
The exit year counts at least 60 EC. It consists of the following mandotory parts:
Final Project: 192199978 Final Project (30 EC)
Computer Ethics:191612680 Computer Ethics
Research Topics: Instead of the regular CS course “Research Topics”, EIT exit year students combine: 201800524 Research Topics EIT (4EC) and 201800525 I&E Study EIT (6EC)

Courses to be largely covered by the entry year at another university (see explanation above), but if not the remaining courses need to be covered as part of the exit year:

PLus 3 out of:

Profiling/elective courses:
The above 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. Though any other course suggested for the profiling space of the Data Science & Technology programme is also allowed.

Programme mentors

dr.ir. M. van Keulen (Maurice)
Programme mentor DST and EIT Digital
dr. N. Strisciuglio (Nicola)
Programme mentor SDS

Study advisors