Pre Master

Do you have a relevant background and sufficient knowledge of statistics and mathematics, but are you not directly eligible for the Data Science in Business and Entrepreneurship master’s program? You could then consider the 30 EC premaster’s program, which is available to both research university students and graduates from a Dutch university of Applied Sciences (‘HBO’). The premaster's program includes the following courses:

Programming – 6 ECTS

Provides students unfamiliar with programming an introduction to and basic skills in programming and scripting, using Python 3. You can solve simple programming problems independently, and structure these in Python. Most of the acquired principles can also be applied to other computer languages used in data science (such as ‘R’).

Data-structures and Algorithms – 6 ECTS

Presents algorithmic techniques for solving data science application problems. You learn the basic skills required to design efficient algorithms and data structures and to analyze their complexity.

Introduction to Machine Learning – 6 ECTS

Covers basic topics in Data Mining and Machine learning, leading from the design of a proper data-scientific study campaign and proceeding to experimentation with ML algorithms. You learn the basics of research design and hypothesis formulation and testing.

Foundations of Databases – 6 ECTS

Introduces the fundamentals of database systems, focusing on the relational algebra model. Analysis, design and implementation of database systems are discussed in detail and you learn to understand semi-structured data.

Statistics for Data Scientists – 6 ECTS

Addresses in systematic fashion the fundamentals of statistical inference and testing, providing an introduction to statistical modeling. The first half of the course focuses on the fundamentals of statistical inference such as sampling procedures, probability theory and random variables. In the beginning, a gentle introduction to ‘R’ for statistical computing is provided, which is used throughout the course to show how theoretical concepts can be applied in practice. In the second half of the course, we address the estimation and testing of population characteristics based on sample data. We then provide an introduction to statistical modeling through lectures on generalized linear regression models, briefly outlining the Bayesian approach to statistics. Real data examples are used throughout the course during the lectures and then in challenging homework. This course is a stepping stone for your future, preparing you for other electives in machine learning, data mining, and visualization.

Link to premaster: Go to the course descriptions