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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)

€850 - €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!

Summer school

Data Science

Foundations of Data Science

Course Overview

Foundations of Data Science is a hands-on course that exposes participants to state-of-the-art tools employed in data science. The course is composed of three units that guide participants through the process of converting raw data into actionable insights:

Data handling and visualization
Supervised learning: the course covers some of the most relevant supervised learning tools, ranging from linear models such as LASSO, Ridge and Elastic Net to nonlinear models, such as Decision Trees, Random Forests and Boosting.
Unsupervised learning: participants are introduced to the main concepts and tools for dealing with unsupervised learning problems, such as clustering algorithms and Principal Components Analysis.

Classes will consist of rigorous training of the topics together with practical sessions to learn how to deploy these techniques to real data sets. Built around Jupyter Notebooks, this course offers participants the opportunity to improve their programming skills in both Python and R, the most widely used programming languages in data science.
After successful completion, you will have:

Worked with and extracted valuable insights from real data
Improved programming skills in two of the most used programming languages in data science
Skills to understand 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
Learnt how to work with large data sets

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

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 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

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€

850€

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

FAQ

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

Can I see the full Summer School calendar?

You can view the full Summer School calendar here.

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 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 or request a personalized discount quote by email.