880502 :Master thesis/Data Science in Action

General info

Instruction language English
Type of Instruction Individual and group meetings with supervisors (Lecture schedule)
Type of exams Master thesis project proposal (pass/fail) + Master Thesis (graded) (No data available yet)
Course load:1 or 2 or 15 ECTS credits depending on the programme, see below.
Registration:Enrollment before start lectures
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dr. M. Postma-Nilsenova


The objective of this integrative project that will ultimately result in an individual Master’s thesis is to teach students how to perform large-scale data-driven analyses. Students combine the technical, legal and entrepreneurial skills acquired earlier in the program with insights provided by lecturers and companies involved in the different projects. Along the way, students acquire additional technical skills that are related to obtaining data (for instance extracting data from databases or data wrangling), performing the analysis (for instance data analysis based on mathematical modeling or machine learning), and creating (interactive) visualizations. With all this, the Data Science in Action project prepares students to work as all-round data scientists in a variety of fields.


  • The Data Science in Action projects are introduced during a plenary meeting organized in October 2016. Attendance of the meeting is obligatory.
  • In November 2016, students are matched to individual projects during a 'kick-off meeting' with selected internal and external project partners.
  • Following the November meeting, students write a project proposal.
  • The proposals are evaluated in the beginning of January 2017.
  • A project must be approved by the Data Science in Action coordinator to receive a 'GO'. Project proposals that do not receive a 'GO' can be resubmitted by the end of January. If the proposal fails the second time, the student will have a new attempt in the following academic year.


The Data Science in Action projects are specifically intended for students in the Data Science: Business and Governance track.

Compulsory Reading

  1. Articles relevant for the selected thesis topic

Recommended Prerequisites

Course RS Data Processing

Required Prerequisites

Course Social Data Mining

Compulsory for

  • Data Science: Business and Governance ( 2015: 1 ECTS, 2015: 15 ECTS, 2015: 2 ECTS, 2016: 15 ECTS, 2016: 1 ECTS, 2016: 2 ECTS )