Students participating in a lecture

Pre-Master's program Data Science and Entrepreneurship

In preparation of the Master's program Data Science and Entrepreneurship we offer a pre-Master of 30 ECTS. This half-year preparatory course is made for applicants who do have a sufficient background in statistics and mathematics, but are not directly eligible to the Master's program.

Pre-Master's program

Starting in February, this half a year pre-Master's program will prepare you for the start of the Master's program in September.

  • The total length of the pre-Master's program is one semester (six months).
  • After succesfully completing your exams, you will be eligible to enroll into the Master's program in Data Science and Entrepreneurship.
  • When you are still finishing your BSc studies at a Dutch Research University, it might be possible to join the pre-Master's program at the same time. If this applies to you, contact the International Admissions Coordinator (admissions-m-datascience@tilburguniversity.edu) for more information.
Courses pre-Master's program

The pre-Master's program includes the following courses:

  • Programming – 6 ECTS
    The course gives students who do not have experience with programming, a first introduction and basic skills in (mainly imperative) programming and scripting, using Python 3. You can solve simple programming problems independently, and structure these in the language Python. Most of the learned principles can be applied to other computer languages used in data science (e.g. R) as well.
  • Data-structures and Algorithms – 6 ECTS
    This course offers algorithmic techniques for solving problems with respect to data science applications. The main objective of this course is to learn basic skills and knowledge to design efficient algorithms and data structures and to analyze their complexity.
  • Introduction to Machine Learning – 6 ECTS
    The "introduction to Machine Learning" course will cover basic topics in Data Mining and Machine learning, leading from the design of a proper data-scientific study campaign which starts from data mining and preparation and proceeds to experimentation with ML algorithms. Known frameworks for Data Mining (i.e., CRISP-DM) will be considered and experimented upon practically. Furthermore, you will learn the basics of research design and hypothesis formulation/testing. Subsequently, you will get to grips with most commonly used techniques of machine-learning including decision-trees, instance-based learning, as well as artificial neural networks. Finally, you will learn the basics of model evaluation, model generalization as well as the bias-variance tradeoff.
  • Foundations of Databases – 6 ECTS
    This course will introduce the fundamentals of database systems. The main emphasis is on the relational algebra and model. Analysis, design and implementation of database systems are discussed in detail. Furthermore, you will learn to understand semi-structured data (e.g. XML and JSON).
  • Statistics for Data Scientists – 6 ECTS
    In this course, we systematically cover fundamentals of statistical inference and testing, and give an introduction to statistical modeling. The first half of the course will be focused on the fundamentals of statistical inference such as sampling procedures, probability theory, and random variables. In the beginning we also provide a gentle introduction to the R language for statistical computing, which will be used throughout the course to show how theoretical concepts can be applied in practice. In the second half of the course we will deal with the estimation and testing of population characteristics based on sample data. Furthermore, we provide an introduction to statistical modeling via introductory lectures on (generalized) linear regression models and briefly discuss the Bayesian approach to statistics. Throughout the course, real-data examples will be used in lecture discussion and homework problems. This course lays the foundation, preparing you for other courses in machine learning, data mining, and visualization.

You will find a detailed description of the courses and required literature in our course catalog.

Go to the course descriptions


Please note: programs are subject to change. We advise you to look up the current program in OSIRIS Student at the start of the year.


Do you want to know more about this pre-Master’s program?

Visit this pre-Master’s information session during the upcoming Open day

Open day Master's programs

Admission and application

An admission committee will determine your eligibility for the pre-Master’s program taking into account your eventual eligibility for the Master’s program.

To apply for the pre-Master's program, please follow the same steps as applying for the Master's program Data Science and Entrepreneurship. 

Are you an applied science / 'Hogeschool' student?

  • Students from AVANS and FONTYS ICT
    Excellent students studying at AVANS and FONTYS ICT can be offered the opportunity to follow a minor program during their studies. For more information about the minor, you can contact the study advisor of your program at AVANS or FONTYS ICT. Please note that application for the minor program goes directly through your hbo institution.
  • Students holding a degree in HBO ICT or equivalent
    If your HBO ICT program contains at least 15 ECTS in mathematics and statistics, which includes a minimum of 5 ECTS in mathematics and 5 ECTS in statistics, your English meets the language requirements and your average grade of the HBO ICT program is 7,5 or higher you are most likely to be eligible for a premaster at JADS.

    If your average grade of the HBO ICT program is 7,5 or higher you are a candidate for the 30 ECTS pre-Master. Students having a lower average but meeting the criteria on Mathematics, Statistics, and English will be considered for the 54 ECTS pre-Master. 

Interested in the MSc Data Science and Entrepreneurship?

Check your eligibility and the deadlines for application