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Data Science in Finance

Data Science for Empirical Finance

Master key tools for reproducible empirical finance research with hands-on coding in R or Python.

17.5h (5 days)

€800 - €1,400

Face-to-face

English

Program date: July 7 - 11, 2025

Early bird deadline: April 15, 2025

Learn more

Data Science for Empirical Finance

Applications for 2025 Summer School programs are now open!

Summer school

Data Science in Finance

Data Science for Empirical Finance

Course Overview

This summer school provides participants with the essential tools and techniques for reproducible empirical research in finance, clear communication, and efficient collaboration on code. Whether students plan to pursue academic careers or transition into industry after their PhD, the content is highly relevant, equipping them with skills valued in both settings. While prior programming experience is helpful, it is not necessary, as the course covers both foundational and advanced techniques that can be applied with any programming language.

The course is designed to accommodate participants with varying levels of experience. It covers both foundational and advanced techniques that are transferable across programming languages. Key concepts such as project-oriented workflows, version control, tidy data principles, tidy coding, and reproducible communication form the core of the course. These concepts are introduced and applied through popular empirical finance applications, providing participants with hands-on experience as they iteratively develop their own research projects.

Participants will have the flexibility to work in their preferred open-source language, whether it be R, Python, or Julia. Morning lectures will focus on core theoretical concepts and key applications in empirical finance, complete with illustrative code. In the afternoon, practical sessions will allow participants to apply their newly acquired skills to real-world financial data and research challenges. The emphasis will be on universal best practices, ensuring the skills are applicable to both academic research and industry roles.

Teaching Faculty

Gain hands-on experience using key data science languages to execute advanced financial applications

The course is designed to accommodate participants with varying levels of experience.

Students who are currently pursuing a MSc/PhD in Finance or Data Science
Students who plan to get into academia or the industry after completing their studies

Get essential tools and techniques for reproducible empirical research in finance

Participants of this course will:

Learn essential tools and techniques for reproducible empirical research in finance
Develop clear communication skills for presenting research findings and collaborating on code
Learn efficient collaboration techniques, emphasizing best practices for version control and tidy coding
Gain hands-on experience through empirical finance applications, allowing participants to iteratively develop their own research projects
Cover foundational and advanced techniques that are transferable across programming languages like R or Python
Learn key concepts such as tidy data principles and reproducible communication to support robust research practices
Be able to apply their skills to real-world financial data and research challenges through practical sessions

Program Syllabus for Data Science for Empirical Finance

Here is an outline of the topics that will be discussed during the course.

Foundations of Reproducible Research

Overview of code sharing policies in economics & finance journals and industry coding practices
Project-oriented workflows demonstrated through an application of Modern Portfolio Theory
Introduction to version control for code management

Accessing and Managing Financial Data

Introduction to tidy data principles illustrated through historical firm fundamental and return data
Overview of popular open-source and proprietary data sources for financial research
Efficient technologies for local storage of financial data

Estimating Betas

Introduction to tidy coding principles based on popular examples in empirical asset pricing
Application of the Capital Asset Pricing Model to real-world data
Estimation of time-varying risk factors based on daily and monthly data

Portfolio Sorts

Application of univariate and bivariate portfolio sorts
Replication of Fama-French risk factors
Scaling portfolio sorts to compute non-standard errors

Regression Analyses

Application of Fama-MacBeth regressions for empirical asset pricing
Application of panel regressions and clustered standard errors for empirical corporate finance
Application of causal inference methods (e.g. difference-in-difference, regression discontinuity)

List of References

The course slides and code will be distributed with the course pack. The main references are:

Books

‘Tidy Finance with R’ by Christoph Scheuch, Stefan Voigt, and Patrick Weiss, Released: April 2023, Publisher: Chapman & Hall/CRC Press, ISBN: 9781032389349.
‘Tidy Finance with Python’ by Christoph Scheuch, Stefan Voigt, Patrick Weiss, and Christoph Frey, Release: July 2024, Publisher: Chapman & Hall/CRC Press, ISBN: 9781032676418.

Software / Hardware

Students will utilize coding programs Python or R throughout the course

Why join our Summer School?

All BSE Summer courses are taught to the same high standard as our Master’s programs. Join us to:

1

Network with like-minded peers

2

Study in vibrant Barcelona

3

Learn from world-renowned faculty

Admissions and Requirements

It is the participant’s responsibility to ensure they meet the Admissions Criteria.

Program date: July 7 - 11, 2025

Early bird deadline: April 15, 2025

Requirements

Summer School applicants normally demonstrate one or more of the following:

A strong background in Economics or a field closely related to the course topic (Statistics, Law, etc.)
Postgraduate degree or current Master’s/PhD studies related to the course topic
Relevant professional experience

Requirements for Data Science for Empirical Finance

Participants should have undergraduate-level finance knowledge and be familiar with R or Python

Schedule

Here is your schedule for this edition of BSE Data Science for Finance Summer School, Data Science for Empirical Finance course.

Time

7

mon

8

tue

9

wed

10

thu

11

fri

09:00 - 11:00

Lecture

14:30 - 16:00

Practical

Credit Transfers (ECTS)

To be eligible for credit transfer, students must complete a final project.

Students will deliver a short final project one week after the summer school finishes. It will consist in solving a final problem that will include the practical and empirical issues worked on in class.

Please consult the Summer School Admissions page for more information about this option.

Certificate of Attendance

Participants who attend more than 80% of the course will receive a Certificate of Attendance, free of charge.

Fees

Multiple course discounts are available, see more information about available discounts. Fees for courses in other Summer School programs may vary.

Course

Data Science for Empirical Finance

Large Language Models in Finance

Modality

Face-to-face

Total Hours

17.5

ECTS

1

Regular Fee

1,400€

Reduced Fee*

800€

* Reduced Fee applies for PhD or Master’s students, Alumni of BSE Master’s programs, and participants who are unemployed.

FAQ

Need more information? Check out our most commonly asked questions.

Can I see the full Summer School calendar?

You can view the full Summer School calendar here.

Is accommodation included in the course fee?

Unfortunately, accommodation is not included in the course fee. Participants are responsible for finding accommodation.

Are the sessions recorded?

Sessions will NOT be recorded; however, the materials provided by the professor will be available for a month after the course has finished.

How much does each Summer School course cost?

Fees for each course may vary. Please consult each course page for accurate information.

Are there any discounts available?

Yes, BSE offers a variety of discounts on its Summer School courses. See more information about available discounts oor request a personalized discount quote by email.