Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Graph
Search

Course

MGT-499: Data Science & Causal Inference for Sustainability

Summary

This class explores key climate questions through data. Students will learn to collect, clean, and analyze data, apply causal methods using Python, and communicate insights clearly. With a focus on sustainability, the course builds skills to avoid pitfalls and draw meaningful conclusions.

Lectures

Instructors (2)

Quentin Gallea

Edoardo Chiarotti

Official source

https://edu.epfl.ch/coursebook/en/data-science-causal-inference-for-sustainability-MGT-499

Moodle Page

http://go.epfl.ch/MGT-499

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Summary

Lectures

Instructors (2)

Quentin Gallea

Edoardo Chiarotti

Official source

https://edu.epfl.ch/coursebook/en/data-science-causal-inference-for-sustainability-MGT-499

Moodle Page

http://go.epfl.ch/MGT-499

About this result

Ontological neighbourhood

Mathematics

Algebra: Linear algebra

Related courses (30)

CS-433: Machine learning

Machine learning methods are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented. ...

PHYS-467: Machine learning for physicists

Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practised. ...

EE-556: Mathematics of data: from theory to computation

This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. We review recent learning formulations and models as well as their guarantees

CS-401: Applied data analysis

This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.) ...

DH-406: Machine learning for DH

This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and implement methods to analyze diverse data types, such as imag ...