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Summer school

Data Science

The BSE Data Science Summer School explores cutting-edge machine learning tools. “Foundations of Data Science” provides a broad overview of key data analysis methods.

Advanced courses include “Harnessing Language Models for NLP” and “Statistical Machine Learning for Large Data”.

€350 - €1,450

Face to Face or Online

English

June 2025 - July 2025

Early bird deadline: April 15, 2025

Learn more

Data Science

Applications for 2025 Summer School programs are now open!

Apply

Discover all of our Data Science Summer School Courses!

Summer School

Data Science

Harnessing Language Models

July 7 - July 11, 2025

Summer School

Data Science

Foundations of Data Science

30 June - July 4, 2025

Summer School

Data Science

Coding Bootcamp in Python and R

June 29, 2025

Summer School

Data Science

Statistical Machine Learning for Large and Unstructured Data

July 14 - July 18, 2025

Who attends BSE Data Science courses?

This course is specifically aimed at:

Graduate students looking to build a strong foundation in machine learning.
Professionals and consultants who want to transition into data science and machine learning roles and are interested in expanding their machine learning skills for predictive modeling and data-driven decision-making.
Researchers & academics seeking to incorporate machine learning methods into their research projects.

Teaching Faculty

Director

Christian Brownlees

Why study at BSE?

Top 3 Economic Research Department

Study with professors who produce top-ranking research in their fields.

World-renowned Faculty

Receive the highest quality training, with courses developed with students in mind.

Barcelona

One of Europe’s most popular cities, combine learning with culture for the best learning experience.

All BSE Summer courses are taught to the same high standard as our Master’s programs. Why should you attend BSE Summer School?

1

Network with like-minded peers from around the world.

2

Short courses allow you to learn without a big time commitment.

3

Try something new, expand your knowledge and career prospects, and advance your thesis.

Admissions Process

What do you need to be able to apply? See the admissions requirements below.

Course begin dates: June - July 2025

Early bird deadline: April 15, 2025

Requirements

Successful candidates will usually demonstrate one or more of the following in an application to our Summer School programs:

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 Foundations course

Participants should be familiar with the fundamentals of linear algebra and programming with Python and R.

Requirements for the other Data Science courses

Participants must be familiar with statistical machine learning and Python and R.

Apply now

FAQ's

Do you still have questions? Check out our FAQ section below! Or contact our Admissions Team.

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 courses. See more information about available discounts or request a personalized discount quote by email.

Can I take more than one course?

Yes! you can combine banking Summer School courses with other BSE Summer School programs (schedule permitting). See the full schedule here.

Credit transfers (ECTS)

To be eligible for credit transfers, 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.

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

Do I get a certificate of attendance?

Participants not seeking credit transfer will receive a Certificate of Attendance free of charge. These participants will be neither evaluated nor graded.