• Business Data Science Courses in 2020
  • Summer School 2020
    • Introduction in Genome-Wide Data Analysis
    • Behavioral Decision Making
    • Econometric Methods for Forecasting and Data Science with Applications in Finance, Economics and Business
    • Deep Learning
  • Research
  • Events

Course Topics and Teachers


Course Topics and Teachers

Course Topics

The availability of big data from a growing range of interconnected, interactive, and interoperable devices and the concurrent development of powerful quantitative techniques are giving rise to new perspectives and paradigms in scientific practice. This is particularly true in the field of business. As data collection has transformed from a tedious, expensive, and time-consuming practice into a continuous and, often, unobtrusive side-effect of day-to-day practices, behaviors and actions of people within and across organizations can be studied far more closely. To leverage these opportunities, there is an increasing demand for highly-trained specialists who can extract insights out of big data to solve business-related problems.

The Business Data Science courses have a strong focus on data science, presented to students at a higher theoretical level than the traditional master’s level. The courses tie the foundations of data science directly to application areas in business research and in businesses, varying from traditional quantitative areas, such as finance, marketing and operations management to new fields, such as HR analytics, accountancy analytics and relevant technological developments around distributed ledgers/Blockchain. 

The Business Data Science courses are advanced courses, targeting students with quantitative skills and a solid background in mathematics, statistics and econometrics. For each course, entrance requirements have been stipulated (see the individual course pages). The program director will only admint students who meet the entrance requirements.

Courses are taught in a small-scale setting, where students work in close collaboration with faculty. The class-size limit of 30 students guarantees a high level of interaction in the classroom, detailed feedback from faculty, and the support of fellow students. The courses are embedded in the vibrant research culture of three leading universities, benefitting from the expertise and research network of top-notch faculty of three schools of economics and business. 

Teaching faculty in 2020 (in alphabetical order):

Prof. dr. Bas Donkers (EUR)
Short bio: Bas Donkers is a professor of marketing research at the Erasmus School of Economics. His research examines consumer decision-making from a behavioural perspective and relies on the use of advanced quantitative analyses as well as various advanced market research techniques to establish new and ground breaking insights in the field. He has published articles in the leading journals in the field including the Journal of Marketing Research and Marketing Science.

Prof. dr. Patrick Groenen (EUR)
Short bio: Patrick Groenen is a professor of Statistics at the Erasmus School of Economics. His work focuses on development of data science methods and their numerical algorithms. He is the co-author of a textbook on multidimensional scaling published by Springer and has published articles in the top peer-reviewed journals including, among others, the Journal of Machine Learning Research, the Journal of Marketing Research, Computational Statistics and Data Analysis, Psychological Methods, Psychometrika, the Journal of Classification, the British Journal of Mathematical and Statistical Psychology, and the Journal of Empirical Finance.

Dr. Meike Morren (Vrije Universiteit)
Short bio: Meike Morren is an Assistant Professor in Marketing at VU since 2012. She holds a PhD in Method and Statistics from Tilburg University. Her interests are sustainability, survey data quality, and text analysis. Her current projects involve NLP on restaurant reviews.

Prof. Gui Liberali (EUR)
Short bio: Gui Liberali is the Professor of Digital Marketing at the Erasmus University. His research has been published in Marketing Science, Management Science, IJRM, EJOR, and Sloan Management Review. He is the Vice-President for Membership at INFORMS Society of Marketing Science (ISMS), elected for the 2018/2019 term. He is currently co-editing a special issue of Management Science on Data-Driven Prescriptive Analytics. Gui is an ERIM Fellow(erim.nl) and twice was a finalist of the John Little award. His research focuses on multi-armed bandits, morphing theory and applications, and online experimentation. Gui was a visiting scholar at the MIT Sloan School of Management for several years. He holds a Doctorate in Marketing, and a B.Sc. in Computer Science

Dr Eran Raviv (APG) 
Short bio: Eran Raviv holds a PhD in econometrics from Tinbergen Institute, a master’s degree in applied statistics from Tel Aviv University and a second master’s degree in quantitative finance from Rotterdam University. His research has been published in high impact peer-reviewed journals. In 2013 he joined APG-AM as a senior quantitative investment strategist. Since spending two years in that role, he has served as a data scientist working on various innovative projects across APG-AM organization, focusing on deep learning methodologies and NLP applications.

Dr. Pieter Schoonees (EUR)
Short bio: Pieter Schoonees is an assistant professor in the Department of Marketing Management at RSM, Erasmus University. His expertise lies in the fields of computational statistics, machine learning and psychometrics. Pieter's research focuses on developing statistical and machine learning algorithms and applying these to secondary data. A special interest is the use of such techniques for the analysis of data gathered from neuroscientific studies.