Degree structure

Program structure

ECTS points
Two years
Master of Science

This Data Science in Business and Entrepreneurship master focuses on the tools and skills you need in order to analyze data used by today’s businesses, and turn this data into meaningful insights.

You acquire the skills and gain the experience you need to evolve into a well-rounded data scientist. You learn by doing – using real business data and case studies based on data-driven issues. This joint degree consists of 120 credits (EC) divided among the courses and master’s thesis as follows:

  • Ten compulsory courses (60 EC)
  • Five elective courses (30 EC)
  • Master’s thesis (30 EC)

The first-year semesters contain five courses of each 6 EC. The second year contains five courses of 6 EC and the master’s thesis of 30 EC.

Compulsory courses

In the first year, you follow seven core courses (five in the first semester and two in the second):

Semester 1.1

  1. Data Entrepreneurship in Action
  2. Data Mining
  3. Data Engineering
  4. Strategy and Business Models
  5. Social Network Analysis for Data Scientists

Semester 1.2

  1. Data Consultancy in Action
  2. Interactive and Explainable AI Design

In the second year, you follow three core courses and write your master’s thesis:

Semester 2.1

  1. Data Entrepreneurship in Action
  2. Intellectual Property and Privacy
  3. Master’s thesis

Semester 2.2

  1. Data Ethics and Entrepreneurship
  2. Master’s thesis

In Action courses

An unparalleled aspect of the program is a series of ‘In Action’ courses. Working in a team, you learn by doing, applying data science methods to create business or societal value from data for companies and organizations. For example: you advise city municipalities on the impact of cultural events using the parking data of ParkNow, our partner organization. Or you predict the costs and duration of a case for DAS, the legal firm. You can help WWF prevent illegal deforestation in developing countries and even improve credit provision to SMEs carried out by Floryn, the fintech scale-up. The significant involvement of these organizations and others in the curriculum is a valued and valuable feature of the program.

Elective courses

On top of the mandatory courses and master’s thesis, you have to pass five elective courses worth 30 EC in total. You are expected to choose one of the specializations listed below. To qualify for a certain specialization, you should pass at least three of the relevant courses, including the core course (listed first and marked in bold below). You are strongly recommended to write your thesis in line with your specialization.

  • Data EngineerAdvanced Data Architectures, Cybersecurity, Real-Time Process Mining, Data-Driven Food Value Chain, and Data Forensics. 
  • Data ScientistDeep Learning, Prescriptive Algorithms, Real-Time Process Mining, Causal Interference for Business Development, and Natural Language Processing. 
  • Data Entrepreneur/ConsultantData-Driven Service Innovation, Data Visualization, Decision Support Systems, Entrepreneurial Finance, and Natural Language Processing.
  • Data-Driven ResearcherResearch in Action/Research Internship, Prescriptive Algorithms, Decision Support Systems, Causal Interference for Business Development, and Natural Language Processing. 

A detailed description of the courses and required literature can be found in our course catalog: Go to the course descriptions. 

Knowledge and skills

Alongside the knowledge gained in data engineering, data analytics, decision-making, business development, and legal and ethical disciplines, you are explicitly trained in a set of essential professional skills, such as:

  • Creative thinking, communicating, presenting, negotiating, debating, interviewing and pitching, practicing these in the In Action courses with real-life data.
  • By engaging with a company’s stakeholders, you learn to shape the problem definition and select and apply the methods suited to the problem – not vice versa. 
  • You learn to create value for business and better understand the practicalities of doing data science in a company. 
  • You start programming, learning and applying data science methods from day 1 of the master’s program. More than a year is spent on gaining practical experience, which has proven to be well appreciated by future employers. This enables you to launch your career in a positive direction – potentially more difficult to achieve with only a one-year master’s program. 
  • In addition to the professional competences you develop, your academic capabilities are honed. These skills enable you to retrieve and understand scientific articles and do your own research into the core scientific disciplines covered by the master’s program.

Master's thesis

You write your master’s thesis during the second year (30 EC) in the course of your thesis project at an external organization (just as 95% of your peers are doing). The commitment of our partner organizations is a compelling and inspiring feature of the Data Science in Business and Entrepreneurship program. Your thesis must advance one of the four scientific disciplines referred to above, while generating a business or societal impact at the same time. Examples of theses written by our students in the past include the following:

  • Predicting the occurrence of a change in a data stream (‘concept drift’) for Mobiquity and adapting Machine Learning models accordingly and dynamically (Data Engineering).
  • Calculating the expected possession value in football using a componential approach, together with the Dutch national football federation KNVB (Data Analytics).
  • Supporting breast cancer treatment using deep learning algorithms with our partner hospital, the Catharina Ziekenhuis (Data Analytics)
  • Earmarking targets for potential acquisitions for clients in the private equity market, together with the Bain Management Consultancy group (Data-Driven Business Development).

Follow in the footsteps of these first-year master’s students: