More information: www.tue.nl/bachelorprograms/ds
The bachelor Data Science is a 3-year Bachelor program, with a fixed part and a large elective part. The fixed part consists of major courses and the Basic Courses which are part of every major and are given university-wide. The Elective part will be filled with USE and Free Elective courses.
Since Data Science is a joint program, courses will also be given at Tilburg University. Each course marked with JOINT will be split between lectures in Eindhoven and Tilburg. The aim is to prevent traveling between two universities in one day, but please note that once you start taking electives this will be harder to plan.
Below you can see a schematic overview of the bachelor program.
In the second quarter you either take the courseApplied Physics orUnderstanding the Information Society. Which of these two courses you take depends on the university you enrolled at. If your main enrollment is at Eindhoven University of Technology you will take Applied Physics. If your main enrollment is at Tilburg University, you will take Understanding the Information Society. This is the only difference in content determined by your enrollment (if you send in a well-founded request to the Examining Board, switching from one course to the other is possible). Apart from this one difference, all students take the same joint program.
The future engineer will become more important as a link between technology and society. For this purpose the USE packages have been introduced. Several packages of 15 credits each are offered, each organized around a theme (e.g. Entrepreneurship, or Robotics). The preferred USE package for Data Science contains the three Data Challenges courses
You have to deﬁne part of your study program yourself by ﬁlling in the elective part. For Data Science, the following coherent packages, 15 credits each, are offered. Examples:
- Web Technology
- StatisticsTechnology Entrepreneurship
- Minor Business Analytics (30 ECTS)
- Minor Data Science and Entrepreneurship (30 ECTS)
- Data Modeling Foundations