Degree structure of the Master's program

The EIT Digital technical programs involve a 2-year master program (120 ECTS) that includes a common technical competence base, which constitutes the curriculum for the first study year, and a specialization that will be the starting point for the thesis work during the second year. In all, this compiles 90 ECTS. In addition, a minor in Innovation & Entrepreneurship (I&E) will provide you with valuable knowledge on how to drive your innovations to the market. Note that it is compulsory for students that the first year program at the entry point university is followed by a second year program at a different university (exit point university). The students will obtain a degree of both the entry and exit university.

Entry Point Program

TU/e, UPM,POLIMI, and UNS offer an entry-point program for the Data Science master. It consists of a set of Common Core Competences, a bases in Entrepreneurship and Electives. The following summarizes the TU/e entry-point program:

Technical Common Base
2IMI35 Introduction to process mining
2IMW15 Web information retrieval and data mining
2IMA10 Advanced Algorithms
2IMV20 Visualization
2DMT00 Applied Statistics

Core Electives (2 out of 4)
2IMV10 Visual computing project
2IMA20 Algorithms for geographic data
2IMM20 Statistical learning theory
2IMI20 Advanced process mining

Suggested Electives (on Top of Program)
2MMS10 Probability and stochastics 1
2IMV25 Interactive virtual environments
2IMS25 Principles of Data Protection
2MMS30 Probability and stochastics 2
2IMI30 Business process simulation
2IMW30 Foundations of data mining
2IMW10 Data engineering
2IMV15 Simulation in Computer Graphics
2IMW20 Database technology
2DD23 Time-series analysis & forecasting

Innovation and Entrepreneurship Module

1ZM20 Technology entrepreneurship
1ZSM0 CTEM project
2IEIT0 Winter school
2IEIT5 Summer school
0LM150 Entrepreneurship and corporate social responsibility


Exit Point Program

The TU/e offers an exit-point program with a specialization in Business Process Intelligence. The courses listed below offer business analytics and predictive modelling techniques enable to detect structures and relationships in such large data sets and to build predictive models. Common techniques for this from fields such as applied statistics, data mining and artificial intelligence are discussed, but also multi-objective optimization of operational processes through nature-inspired meta-heuristics. This includes evolutionary computation techniques such as genetic/memetic algorithms, particle swarm optimization, and ant-colony optimization. Process mining techniques will not be limited to control-flow and will also include other perspectives in bottleneck analysis, social network analysis, and decision mining. The courses enable students to acquire greater depth in high tech systems, healthcare applications, spatial data handling, visual analytics, or software evolution.

Besides learning theoretical concepts, students will be exposed to event data from a variety of domains, including hospitals, insurance companies, governments, high-tech systems, etc. Assignments will focus on the analysis of such data sets and on focusing on a particular process mining problem. Application areas include but are not limited to hospital logistics optimization, software repository mining, predictive maintenance of healthcare equipment, visualisation of genomics data, visual analytics for epidemiologists, etc. Upon completion of this program, graduates will possess a sound foundation to begin a career as a data scientist with a specialization in business process intelligence.

The following summarizes the program:

  • 2IMI35 Introduction to process mining (if needed)
  • 2MMS10 Probability and stochastics 1 (if needed)
  • 2IMS25 Principles of Data Protection
  • 2IMI20 Advanced process mining
  • 2IMI00 Seminar architecture of information systems, OR
  • 2IMW00 Seminar web engineering
  • 1ZS30 Innovation and entrepreneurship thesis
  • 2IMC00 Master project

Alternatives for “if needed” courses

  • 2IMA10 Advanced algorithms
  • 2IMW15 Web information retrieval and data mining