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Data Science

Foundations of Data Science

An interactive course that exposes participants to state-of-the-art tools used in data science

17.5h (5 days)

€800 - €1,450

Face to Face

English

Program date: 30 June - July 4, 2025

Early bird deadline: April 15, 2025

Learn more

Foundations of Data Science

Applications for 2025 Summer School programs are now open!

Apply

Summer school

Data Science

Foundations of Data Science

This interactive course has three units, guiding participants from raw data to actionable insights:

Data Handling and Visualization.
Supervised Learning: Key tools overview.
Unsupervised Learning: Concepts like clustering and Principal Components Analysis.

Teaching Faculty

Want to get up to speed with the latest techniques in Data Science and more?

This course has been designed for:

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.
Researchers, academics, and PhD students seeking to incorporate machine learning methods into their research projects.

Extract valuable insights from real data

After successful completion of this course you will have:

Improved programming skills in two of the most used programming languages in data science.
Undood some of the key methods, as well as their limitations, used by data scientists.
Gained practical experience in applying these methods to large and heterogeneous data.
Learned to work with large datasets.

Program Syllabus for Foundations of Data Science

Here is a brief outline of what we will cover:

Data visualization

Elements of data visualization.
Exploration plots: scatter, lines, barplots, boxplots.
Advanced plots: correlation, regression, biplots.
Special plots.
Reporting using visualization.

Data Handling

Handling missing data: imputation methods.
Feature transformation and engineering: normalization, dimensionality reduction, category encoding.

Supervised learning

Linear models for regression

Linear models and non-linear feature maps.
Model evaluation.
Bias-Variance tradeoff.
Penalized regression.
Cross validation and model selection.

Linear models for classification

Logistic regression.
Misclassification, ROC, AUC.
Class imbalance.

Nonlinear models: decision trees

Decision trees.
Variable selection.
Random Forests.
Bagging and boosting.

Unsupervised learning

Clustering.
Principal components.

Data handling, Supervised and Unsupervised Learning with R.

List of References

To help you prepare for this course, we recommend the following texts: .

Books

The Elements of Statistical Learning. T. Hastie, R. Tibshirani, and J. Friedman. Springer Series in Statistics Springer New York Inc., New York, NY, USA, (2001).
An Introduction to Statistical Learning: with Applications in R, G. James, D. Witten, T. Hastie, R. Tibshirani, Springer Series in Statistics Springer New York Inc., New York, NY, USA, (2021).

Software / Hardware

The course is based on Jupyter Notebooks that can be run in Google Colab.
Participants must bring their own Laptop to participate fully in 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

Before submitting your application please check you are eligible to take the course.

Program date: 30 June - July 4, 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 Foundations of Data Science

Participants of this course are expected to be familiar with the fundamentals of linear algebra and programming with Python and R.
Students who do not have significant programming experience will be admitted provided that they attend the 8h “Computing Bootcamp”.
You can check your skill level by downloading the following script (this script is not part of the application process. It will not be reviewed by the BSE admissions team or the instructors).

Download the script here

Apply now

Schedule

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

Time

30

mon

1

tue

2

wed

3

thu

4

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.

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

Coding Bootcamp in Python and R (8h day)

Foundations of Data Science

Harnessing Language Models: Your Path to NLP Expert

Statistical Machine Learning for Large and Unstructured Data

Modality

Online

Face to Face

Total Hours

8

17.5

ECTS

0

1

Regular Fee

600€

1,450€

Reduced Fee*

350€

800€

FAQ’s

Here are some commonly asked questions by participants or contact our Admissions Team

Is accommodation included in the course fee?

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 or request a personalized discount quote by email.