Minor Data Analytics

The rapid rise of artificial intelligence, machine learning, data science and digitalization in organizations has led to a range of new opportunities and challenges for managers, executives, and (public) decision-makers. These methods are being used to improve both operational and strategic decisions, but to fully leverage their potential, it is essential to understand the inputs and be able to read how they work. You will learn how to interpret machine-learning techniques, understand their inputs, and be able to read their outputs. Furthermore, ethical challenges, such as whether algorithms could be biased towards gender and race, are becoming increasingly important. Leaders should understand the limits of what can be learned from the methods. 

In light of all these challenges, there is a need for managers, executives, and (public) decision-makers who have knowledge about artificial intelligence and digitalization. In a world where technology and data are shaping every industry, this minor bridges the gap between business analytics, economics, artificial intelligence, machine learning and digitalization.

The Minor Data Analytics is designed for students who want to understand how artificial intelligence, data science, and digital technologies are transforming organizations and decision-making. Whether you’re interested in using data to drive business insights, exploring the ethical challenges of AI, or learning how to interpret machine-learning models, this minor is the right choice for you!

If you’re a future manager, executive, or decision-maker looking to navigate the world of AI and digitalization, this minor will provide you with the essential knowledge and skills to stay ahead.

This faculty minor programme in English is for 3rd-year Economics & Business Economics and Bedrijfskunde students of the Faculty of Economics and Business. However, 3rd-year International Business and Econometrics and Operations Research are welcome as well. There are no restrictions on the number of students.

In Block 1.1, the fundamental principles for the minor are established, covering essential concepts and tools for data management, data analysis, and programming. 

In Block 1.2, the knowledge and tools acquired in Block 1.1 are applied and expanded through field-specific courses and a practical data project. Students can select 2 out of 3 elective courses based on their field of study and interests. 

Some examples of courses you can follow are Data Engineering, Data Analytics in Management and Strategy, and Programming in Python.