Background bachelor en

Curriculum Data Science

Year 1

You will develop a strong mathematical base in the first year that you will build on during the rest of your studies. You will also become familiar with the various perspectives on Data Science and you will complete your first Data Challenge.

We will explain some of the courses in more detail to give you an impression of what you can expect.

Data Statistics

In this course you will learn how to use modern statistical software to analyze data and get useful information. Statistics can be divided roughly into “descriptive statistics” and “inferential statistics”. Descriptive statistics summarizes and visualizes the observed data. It is usually not very difficult, but it forms an essential part of reporting (scientific) results. Inferential statistics tries to draw conclusions from the data that would hold true for part or the whole population from which the data is collected.

For instance, one group of patients may receive a control treatment and another group of patients may receive a new treatment. One specific question is whether the new treatment is better than the control treatment. Another question is whether the two treatments are (clinically) equivalent. The benefit of the new treatment may not be at the clinical outcome, but could lie somewhere else, e.g. it may be much cheaper or less invasive than the control treatment. In that case, we would like the new treatment not to be worse. These questions are mostly translated to statistical quantities like population means, medians, or proportions.

Another example is when a production process is being replaced or enhanced. The improved process should typically show more consistent quality in the products that are produced with the new process. Correct decisions in those cases are possible by making use of the theory of hypothesis testing.

The course includes an assignment in which you have to analyze data from a movie about a national disaster. You as a data scientist should draw the correct conclusions so that one can take the right decision to save many lives!

Data Ethics

What makes our joint Bachelor unique is that we combine the technical expertise needed to handle big data with perspectives from Law, Ethics, Economics, Humanities and the social sciences. To become a true all-round data scientist, a multifaceted understanding of ethics and law is crucial. A data scientist trained today can expect to work not only in the private sector but also potentially with government and NGOs, and to be involved in applications of data science from business analytics to humanitarian emergencies. Data scientists are involved in journalism and public policy, they help create smart urban environments and help to solve problems in fields as diverse as cancer research to space travel. Even when they work exclusively for private companies, data scientists’ work has far-reaching implications for society and for our collective wellbeing.

The relation between ethics and data science

For these reasons, understanding how ethics relate to data science is important not only in order to make informed choices about what data and methods to use, but also in order to build successful solutions to real-world problems. For example, data scientists have accidentally produced crime prediction applications that are heavily biased against ethnic minorities; have become involved in mass surveillance in ways that pose risks to democratic processes, and have developed applications such as facial recognition and biometric systems that tend to discriminate against the poorest and most vulnerable. Just as medical students study ethics as an important element of their training, data scientists also need to understand the impacts of their work on individuals and society.

Google as an example

To take one commercial example, Google’s search service has recently been found to prioritize what has been termed ‘fake news’ – fictional, misleading or biased search results that impact users’ ability to make informed choices. Reports show that it is possible to game the search algorithm so that it shows biased results: for example a search for ‘did the Holocaust happen?’ was recently found to produce a page of Holocaust denial sites managed by the political far Right. In a different machine-learning problem, a flaw in Google’s Photos app led to it tagging black people as gorillas.

Big data reflect the biases and prejudice of the crowd

In order to do good work in data science, it is essential to have an understanding of the ways in which that work can go wrong. Solutions that rely on big data – the inputs of the crowd – also tend to reflect the biases and prejudice of that crowd, and unless social and ethical understanding supports scientific knowledge, data science may end up reinforcing inequality and unfairness. 

The Data Science Ethics course

In the Data Science Ethics course, we investigate which ethical frameworks should guide data scientists as they aspire to produce innovative, profitable, and useful applications. We will ask what responsible data science consists of, using case studies from current practice that raise issues around values. The course has an emphasis on multidisciplinarity, responsibility to stakeholders and creating social value. Issues covered may include:

  • The implications of using personal data from individuals as a startup or an established company;
  • The rights and wrongs of using hacked or leaked data;
  • De-identification techniques and decision making frameworks for decreasing the risks of particular applications;
  • Ethical and legal challenges related to ‘living laboratories’ in smart urban environments, and their implications on the individual and group level;
  • The real-life difficulties of regulating data use in line with societal values.

Data Challenge

During this course you will put theory from courses like Data Mining, Data Statics and Data Science Research Methods into practice. The aim of this course is to teach you how to perform large-scale data-driven analyses yourself. You will use real-life data sets from cooperating companies and organizations. An important element in this course is handling large datasets stored in various formats (files, relational databases, object databases, etc.), pre-processing the data and storing the analysis results in a suitable data format.

The aim of the first data challenge is deceptively simple: you will have to answer a number of questions from a “client” using an existing dataset. We will try to make this as real as possible: there is real data, a real client (represented by two of their employees, each with different backgrounds and aims) and you will really have to convince them. This also means that you will have to solve real problems: how do you deal with the large dataset? How do you know the data is valid? How was it collected? What is the actual aim of the client? The data challenges will become more and more challenging as the course advances.

After taking the course, you will be able to:

  • Independently apply and follow established data science research methods for a given problem and dataset.
  • Access, process, and reason about large, complex datasets provided in various data formats.
  • Independently find and familiarize yourself with programming languages, libraries, programs and software.

Block 1

  • Calculus
  • Perspectives on Data Science
  • Programming

Total 15 ECTS

Block 2

  • Applied Physics / Understanding the Information Society
  • Foundations of Computing
  • Data Statistics

Total 15 ECTS

Block 3

  • Data Engineering    
  • Creative Thinking
  • Data Mining

Total 15 ECTS

Block 4

  • Data Science Ethics
  • Data Challenge
  • Statistical Computing

Total 15 ECTS


Year 2

In the second year, you will study the legal, economic, mathematical, and technical aspects of data research in more depth. You fill some of your program yourself with elective modules and apply all your acquired knowledge and skills in two Data Challenges.

Block 1

  • Engineering Design
  • Law and Data Science
  • Elective

Total 15 ECTS

Block 2

  • Data Science Research Methods
  • Data Challenge
  • Business Analytics

Total 15 ECTS

Block 3

  • Process Mining
  • Data Challenge
  • Elective

Total 15 ECTS

Block 4

  • Innovation and Regulation
  • Visualization
  • Elective

Total 15 ECTS

Year 3

Entrepreneurship plays a greater role in the third year, which offers courses such as Start-ups and Business Insights. There is a more extensive choice of electives, so that you can make sure the subjects you choose are best suited to the master you are looking to take. You will complete the program with a Bachelor's thesis.

Block 1

  • Business Insights
  • Data Challenge
  • Elective

Total 15 ECTS

Block 2

  • Cognitive Science
  • Start-ups
  • Elective

Total 15 ECTS


Block 3

  • Elective
  • Elective
  • Elective

Total 15 ECTS

Block 4

  • Final Bachelor Project
  • Elective

Total 15 ECTS

All Bachelor's programs

Financial matters

Admission and application