Master's Degree in Economics and Finance

Master's Degree in Specialized Economic Analysis

Master's Degree in Data Science

Admissions

Career Services

Alumni Community

Economics Program

Financial Economics Program

PhD Economics Track Program

Go to Master's degrees

Close

Data Science in Finance

Data Science for Empirical Finance

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

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!

Apply

Summer school

Data Science in Finance

Data Science for Empirical Finance

This is an introductory course where students will revisit several classic applications in empirical finance (including Asset Allocation, Portfolio Sorts, Fama-MacBeth Regressions) using the state-of-the-art tools used in data science.

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 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, Python, or Julia.
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, R or Julia 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 a basic understanding of both R and Python programming and finance.

Apply now

Schedule

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

Time

14

mon

15

tue

16

wed

17

thu

18

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 not interested in credit transfer will instead receive a Certificate of Attendance free of charge. These Participants will not be graded or assessed during the course.

Fees for 2025

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€

FAQ’s

Need more information? Check out our most commonly asked questions

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 be recorded and videos will be available for a month once 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.