Provide a real-world data challenge and a dataset. During four weeks, a student team will apply econometric and data science methods to generate insights, answer research questions, and deliver recommendations.
Practical Case Study
Real-Life Modelling in Econometrics and Data Science
In this course, student teams work full-time for four weeks on a real-world data challenge provided by an external partner. Using econometric and data science methods, they analyse a supplied dataset, formulate and answer research questions, and communicate their findings through a professional report and presentation.
Challenges can originate from business, academia, government, or non-profit organisations. Throughout the project, students are supervised by experienced lecturers in Econometrics and Data Science at Vrije Universiteit Amsterdam.
✅ Key features of the challenge
Program: Minor in Applied Econometrics: A Big Data Experience for All at Vrije Universiteit Amsterdam (program study guide)
Course: Practical Case Study: Real-Life Modelling in Econometrics and Data Science (course study guide)
Students per team: 4 students (possible to get multiple teams if preferred)
No. of hours spent per team: Approximately 640 hours (4 students × 4 weeks full-time)
Duration: January 2027
Supervision: Student teams are supervised by lecturers from Econometrics and Data Science
Deliverables: Oral presentation and professional research report including code appendix
Company time investment: Approximately 2–6 hours (case development, kick-off, optional contact, optional (rehearsal) presentation)
Costs: None
📑 Challenge Examples
Previous projects have included:
Forecasting product demand
Explaining and forecasting housing prices
Analysing text
Studying trends in educational outcomes
Modelling climate and weather data
Analysing health-related outcomes
Investigating crime trends
Financial studies (e.g. derivatives, pension fund, risk, or volatility data)
Macroeconomic studies (e.g. selecting drivers of the economy, formulating policy recommendations, or forecasting economic variables)
🏢 Company requirement
Suitable partners include companies, government organisations, research institutes, NGOs, and university departments that:
Can provide a data-driven question or challenge.
Can supply a prepared or publicly available dataset.
Can appoint a contact person for the kick-off meeting.
The challenge should be feasible within four weeks and allow students to apply econometric and/or data science methods at an advanced undergraduate level.
💰 Costs
None
📨 How to apply
You can submit the challenge directly through the website. Provide a title that summarises the challenge. Subsequently, briefly elaborate on your challenge.
🗓 Planning
30 October 2026: Expression of interest
13 November 2026: Submission of case description
27 November 2026: Submission of dataset and kick-off details
Mid December 2026: Student matching
Early January 2027: Kick-off at the (online) office
January 2027: Project execution
Late January 2027: Final presentations and report submission
Further details on course organization, case guidelines, and important dates are available upon request.