Data and their analysis are getting increasingly important in understanding and shaping our world. The demand for people who are able to put these data to productive use in many different contexts is rising constantly. Tech companies, the financial industry, central banks, statistical offices, consulting firms but also public administrations, and SMEs increasingly rely on specialists with strong quantitative skills.

The Major in Data Science and Computational Economics provides the tools from statistics, informatics, business, and economics needed to collect, understand and analyze complex data. Our focus is on answering real-world questions of businesses and economic policy based on empirical evidence and simulation.

We offer you a personalized and inspiring learning environment where students and lecturers closely interact, and which prepares you for a professional career in data science and computational economics.

This major is offered exclusively in English.

The Major in Data Science and Computational Economics provides you with knowledge of basic principles of programming and coding, numerical methods and optimization, probability, and statistics, econometric methods for prediction and causal inference, artificial intelligence and machine learning, as well as simulation and agent-based modeling.

You will learn how to collect and process data from a variety of sources, analyze cross-sectional, time series, and panel data, build computational and econometric models and run computational experiments, operationalize and apply quantitative models for insurance or finance, use Monte Carlo simulation for demanding stochastic problems, analyze complex adaptive (economic) systems with agent-based simulation, and work with AI inspired methods.

You will be able to analyze large and complex data sets to produce economically meaningful answers, assess the validity of econometric and computational evidence, and translate real-world questions into meaningful mathematical models and relevant answers.

A major in Data Science and Computational Economics includes the following elements:

Fundamentals in Business and Economics (12 ECTS).

This module consists of the three lectures "62650 - Behavioral Science", "12036 - Econometrics" and "62651 - Theory of the Firm", which must be attended and passed by all students of the Master's program in Business and Economics. If you are pursuing a major in Data Science and Computational Economics, it is recommended that you take the course "Econometrics" as early as possible - ideally in your first semester.

Core Courses in Data Science and Computational Economics (18 ECTS)

This module provides you with the necessary background in mathematics and statistics as well as the required practical skills in coding.

The mathematics course is offered in the two weeks prior to the start of classes in the fall semester so that you can acquire the necessary mathematical background for the other three core courses. We recommend that you take all Core Courses as early as possible. If you begin the course in the fall semester, the recommended plan of study is as follows:

  • 18545 - Advanced Mathematics for Economics | 1st semester - begins before classes officially start!
  • 41957 - Advanced Econometrics | 1st semester
  • 58989 - Computing for Business and Economics | 1st semester
  • 58920 - Causal Inference for Policy Evaluation | 1st or 3rd semester
  • 55753 - Elements of Applied Probability | 1st or 3rd semester
Research Design (6 ECTS)

Writing and presenting a paper in the seminar "65936 - Data Science" is mandatory for the major. This will familiarize you with the current state of research in one of the major areas of economics (you have the choice whether to write your seminar paper in Data Science or Computational Economics) and prepare you for your master thesis.

  • 65936 - Data Science and Computational Economics | recommended from the 2nd semester onwards
Specific Electives (12 ECTS)

This module provides you with a deeper insight into areas of economics and/or methodology. You choose the ECTS from the designated list of the medium-term curriculum (Master in Business and Economics > Major in Data Science and Computational Economics). Recommended from semester 2 (if starting in fall semester) or later.

General Electives (12 or 24 ECTS).

This module allows you to take additional courses in economics. Depending on your preferences, you can choose from those courses offered for the Master of Science in Business and Economics or listed in the General Electives module in the medium-term curriculum (Master in Business and Economics > Major in Data Science and Computational Economics > General Electives). You have to earn 12 ECTS (if you write a master thesis with 30 ETCS) or 24 ECTS (if you write a master thesis with 18 ECTS) in this module.

Master's Thesis (18 or 30 ECTS)

The master's thesis is the capstone of the Major in Data Science and Computational Economics, providing you with the opportunity to apply the skills you have acquired in the program to a specific economic problem. As a rule, you have 15 weeks to complete the master's thesis and will acquire 18 ECTS for the thesis. Alternatively, you can opt for spending 25 weeks writing a more ambitious master's thesis worth 30 ECTS. We recommend that students in the Major in Data Science and Computational Economics pursue this option. More information about the master's thesis is available here.

What lectures are part of the program? Have a look!
All you need to know about the master's thesis.

Students with a university degree that is quantitatively and qualitatively equivalent to the Bachelor of Arts in Business and Economics of the University of Basel are admitted to the master's program without additional requirements. Other university degrees are assessed for equivalence and are admitted with additional requirements if necessary.

Prospective students with a Bachelor of Arts in Business and Economics from the University of Applied Sciences Northwestern Switzerland (FHNW) can find detailed information on admission and the optimal course of study in this information sheet.

The detailed admission requirements are regulated in the study plan. If you have any questions about possible requirements, you can contact the Dean of Studies' Office of the Faculty of Business and Economics. However, without an existing official application, only a legally non-binding information can be given.

Please send your application to the Student Administraiton Office. From there you will also receive the legally binding admission decision.

Prof. Dr. Christian Kleiber

Econometrics and Statistics

Christian Kleiber is Professor of Econometrics and Statistics at the University of Basel. Trained in statistics, he obtained his Ph.D. from the Technical University of Dortmund. His research interests include microeconometrics, applied probability in business and economics, statistical distributions, income distribution, and inequality measurement, and the probabilistic and statistical foundations of econometrics and data science. The co-author of two books, on economic size distributions and econometric computing, he has published in journals such as the Journal of Applied Econometrics, the Journal of Multivariate Analysis, the Journal of Public Economics, the Journal of Theoretical Probability, and The American Statistician. He has also co-authored several R packages.


Prof. Dr. Dietmar Maringer

Computational Economics and Finance

Dietmar Maringer is Professor of Computational Economics and Finance at the University of Basel, Switzerland. He studied computer science, business, and finance in Vienna, Austria, and Cambridge, UK, and earned his habilitation while at the University of Erfurt’s econometrics department, Germany. His research interests combine finance and computational methods from artificial intelligence and data analysis. These include risk management, portfolio optimization, algorithmic trading, high-frequency markets, financial networks, complex adaptive systems, machine learning, computational intelligence, and simulation. He has co-authored and edited several books and has published in journals like Mathematics of Computation, Journal of Global Optimization, Computational Statistics and Data Analysis, Quantitative Finance, Journal of Banking and Finance, Computational Economics, and Computational Management Science.

Prof. Dr. Kurt Schmidheiny

Economics and Applied Econometrics

Kurt Schmidheiny is Professor of Economics and Applied Econometrics at the University of Basel. He is also a Research Fellow at the Centre for Economic Policy Research (CEPR, London) and at CESifo (Munich), and a member of the editorial boards of the Journal of Urban Economics and the Journal of Economic Geography. He published in the Journal of Political Economy, American Economic Journal, Economic Journal, Journal of Public Economics, Journal of Urban Economics, Regional Science and Urban Economics, and Journal of Economic Geography.

His research focuses on tax competition, fiscal federalism, and urban economics. Kurt Schmidheiny often works with detailed administrative data such as individual-level tax data on individuals and firms. He also develops the necessary applied microeconometrics methods used to estimate causal effects from such data.

Prof. Dr. Conny Wunsch

Labor Economics

Conny Wunsch is Professor of Labor Economics at the University of Basel and head of the Graduate School of Business and Economics. Her research focuses on labor economics and econometric methods. She is co-editor of Labour Economics and the Swiss Journal of Economics and Statistics, and a member of the scientific advisory boards of the KOF Swiss Economic Institute, the Institute for Employment Research (IAB), and the RWI Essen. She is also a research fellow of the Centre for Economic Policy Research (CEPR), CESifo, DIW, and IZA. She published in Experimental Economics, the Industrial and Labor Relations Review, the Journal of Econometrics, the Journal of the European Economic Association, the Journal of Health Economics, the Journal of Labor Economics, and the Review of Economics and Statistics.



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