Material
Laptops
Please bring a laptop to class if possible. We encourage collaborative work, and ideally we aim for at least one laptop for every two students. Both homework assignments and the group project involve coding and teamwork, so having access to a laptop will make your learning experience smoother.
If you are considering purchasing a laptop, students from Swiss universities can benefit from preferential pricing through the Neptun Projekt or EPFL’s Poseidon.
Note
You do not need to buy a new laptop if you do not have one — sharing with classmates is fine.
Operating Systems
The course can be followed on MacOS, Windows, or Linux. All three operating systems are supported, and we provide installation guides for each. Linux remains a strong option for data science, but any modern laptop will work.
Textbooks & Learning Resources
There is no single required textbook. Instead, we rely on a combination of online books, documentation, and tutorials. All resources are freely available online.
Core References
- R Programming
- R for Data Science by Garrett Grolemund & Hadley Wickham
- Advanced R by Hadley Wickham
- R Packages by Hadley Wickham & Jenny Bryan
- Python Programming
- Python Data Science Handbook by Jake VanderPlas
- Fluent Python (2nd Edition) by Luciano Ramalho
- SQL & Databases
- Reproducibility & Collaboration
- Happy Git and GitHub for the useR by Jenny Bryan
- Pro Git Book by Scott Chacon & Ben Straub
- Business Intelligence & Visualization
- Power BI Documentation
- Mastering Shiny by Hadley Wickham
- Modern AI Tools
- Prompt Engineering Guide
- LangChain Documentation (for Python + LLM integration)
Supplemental Resources
- ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham
- Seamless R and C++ Integration with Rcpp by Dirk Eddelbuettel
- Engineering Production-Grade Shiny Apps by Colin Fay et al.
- Effective Python by Brett Slatkin
We regroup additional references by topic in the resources page.
Software
All software used in this course is free and open source (or provided with academic licenses). We will work with:
- R (CRAN) with RStudio IDE
- Python (Anaconda Distribution or Miniconda)
- SQL (via SQLite, PostgreSQL, and connectors in R/Python)
- Git & GitHub for version control and collaboration
- Power BI Desktop (free academic license)
- Optional AI assistants (e.g. ChatGPT, Copilot) for coding and documentation support
Detailed installation guides will be provided before the first practical session.