Bachelor 2025/2026

Data Science for Economics

Type: Elective course (International Programme in Economics and Finance)

Delivered by: International College of Economics and Finance

When: 3 year, 3, 4 module

Open to: students of one campus

Language: English

Contact hours: 64

Course Syllabus

Abstract

The course consists of three parts: 1. Introduction to programming; 2. Overview of the most commonly used machine learning algorithms; 3. Time permitting, an introduction to causal inference and applications of machine learning algorithms to causal inference. In the first part of the course students will learn basic programming using computing language R. Obtained skills will allow to implement all methods taught subsequently. Additionally, students will learn how to explore and analyse structured and un-structured data sets. Finally, provided introduction to programming will also be useful in subsequent courses in econometrics and economics. At first, to gain intuition, we will study how to solve the problem in a brute force manner and then explore R packages and built in functions to deal with a problem in the most efficient manner. In the second part of the course we will focus on most commonly used machine learning algorithms. We will cover regression techniques (parametric, nonparametric and high-dimensional), classification methods, resampling methods, model selection, unsupervised learning and text analysis (time permitting). Finally, in the last part of the course we will cover research papers which have recently applied machine learning methods to causal inference in economics. Course Pre-requisites: Statistics; Mathematics for Economists. In the second part of the course I will present and derive statistical properties of various estimators. To be able to follow this part of the course students should have a certain level of mathematical maturity. In practice, this means that students should have done some simple mathematical proofs before taking this class (for example, understand “epsilon-delta” arguments in the context of limits of sequences).