Program Data Science and Entrepreneurship

The MSc Joint Master Data Science and Entrepreneurship entails a two-year program requiring students to attain 120 credit points (ECTS).

  • The curriculum is divided into four semesters of 30 credits each.
  • Each semester has five parallel courses of six credits each, except for the second semester in the second year, in which you write your master’s thesis.

Curriculum Joint Master Data Science and Entrepreneurship

This is what the curriculum of the program will look like. Note: the program is still in development and may undergo some small changes.

Year 1

Semester 1

    • Data Entrepreneurship in Action I 
    • Data Mining
    • Data & Law (IP&Privacy)
    • Data Architecture & Integration
    • Strategy and Business Models 

    Semester 2

      • Data Entrepreneurship in Action II 
      • Deep Learning
      • Cognition & Creativity

      2 of the following electives:

      • Innovation Services
      • Entrepreneurial Marketing
      • Entrepreneurial Finance
      • Business Analytics

      Year 2

      Semester 1

      • Data Entrepreneurship in Action III 
      • Process Mining
      • Ethics & Entrepreneurship

       2 of the following electives:

      • Data Visualisation
      • Data Entrepreneurship
      • Corporate Entrepreneurship
      • Super Crunchers

       Semester 2

      • Master’s thesis

      Elective courses

      In addition to eleven compulsory courses and writing a dissertation, students are required to follow a minimum of four elective courses. Depending on your choice of course, this could require you to travel to either the Tilburg or Eindhoven campus.

      Data Entrepreneurship in Action courses

      The backbone of the program comprises a series of Data Entrepreneurship in Action courses. Working in a team, you learn to apply data-driven methods to test the feasibility of an idea or innovation, build a data-intensive solution, propose sales channels and customers and develop entrepreneurial skills in building a technology startup.

      Collaboration

      Teams are typically built up of students with different skills and expertise. Based on real data and issues provided by industry partners and the Data Science Academy Eindhoven, students learn by tackling real problems. Teams are actively coached by industry partners and university professors.

      How we teach

      You'll learn from teachers who are experts in their respective fields. You'll develop skills and gain experience that will make you a well-rounded data scientist. We use a wide variety of teaching techniques from lectures and tutorials to online teaching methods. These include virtual lectures, chat sessions, online assignments and digital learning platforms. But, most importantly, you learn by using real data and case studies to practice skills. At post-graduate level we place emphasis on self-directed study.

      Studying abroad

      You are encouraged to study abroad during a quarter- or a full semester.

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