Data Analytics for Economists

URL study guide

https://studiegids.vu.nl/en/courses/2025-2026/E_EC_DAEC

Course Objective

The main goal of this course is to make students familiar with methods that are often used in economics for analyzing large amounts of data. Therefore, students learn the key concepts and methods in big data and machine learning. After the course students are able to work with large data sets and apply the key methods. (Academic and research skills) Furthermore, they can interpret empirical results in economics produced by machine learning methods. (Bridging theory and practice
- knowledge)

Course Content

More and more data are becoming available for economic research in large amounts and often these data are structured differently than traditional data sets. New methods have recently been developed to analyze such data. These methods are often referred to as big data techniques or machine learning methods. During this course various techniques are discussed. The main attention is devoted to introducing the idea of data structures and key concepts and algorithms of machine learning methods, including regularization and cross-validation as well as both supervised/unsupervised methods and techniques for natural language processing. During the course students work with the R programming language when appling different methods.

Teaching Methods

Regular classes and tutorials

Method of Assessment

25% in-class quiz 20% group assignments (data exercise) 55% exam (written closed-book)

Literature

The Elements of Statistical Learning Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, Jerome Friedman Foundations of Statistics for Data Scientists: With R and Python by by Alan Agresti, Maria Kateri Introduction To Algorithms (3rd edition) by Thomas H. Cormen, ‎Charles E. Leiserson, ‎Ronald L. Rivest

Entry Requirements

Students are expected to have passed the prior econometrics course confidently and are familiar with R at the level at which it is used in these courses. Learning machine learning / big data analytics inevitably requires a stronger mathematical background, good programming skills, and a problem-solving mindset.

Recommended background knowledge

Mathematics Basic programming skills Statistics Econometrics