Data science is an amalgam of tools, techniques, and processes from statistics, computer science, signal processing, machine learning, …, chosen to form a powerful toolbox and a set of best practices for modern data analysis. Success stories of data science range from molecular biology where it is used to understand single cell RNA sequencing datasets, over physics where it is used to detect new elementary particles, to governance and policymaking where it is used to visualize, understand, and predict global migration flows. A Practical Introduction to Data Science is a first data science course for a varied audience, which emphasizes concrete examples in Python. It assumes some familiarity with programming, ideally in Python. The course covers exploratory data analysis, causal reasoning, data visualization principles, fundamentals of statistics and probability, and machine learning, with many computational examples.
Timetable
|
Week |
Topic |
Resources |
Assignments |
|---|---|---|---|
| 1 (19/02) |
Introduction(slides, video) NB: videos and slides are from previous year so ignore the admin parts; |
Breiman: Statistical Modeling: The Two Cultures Lecture notebooks:
|
|
Communication
For discussions about the lectures and excercises you can use the Piazza forum. This allows you to ask questions and help each other in the precise context where issues arise. It's really great!
Invitations to the Piazza forum will be sent out after the first lecture on 19.02.2025
Class Time & Location
Lectures — Wednesday @ 08:15am in Alte Universität, Hörsaal -101
Recitations — Thursday @ 08.15am in Biozentrum, Hörsaal U1.131
Exam ---- 20.06.2025 @ 2pm to 4pm in Biozentrum, Maurice E. Müller Saal U1.111
Reading and Online Resources
The following books are pure gold:
- Spiegelhalter, David. The Art of Statistics: Learning from Data. Penguin UK, 2019.
- Pearl, Judea & MacKenzie, Dana: The Book fo Why: The New Science of Cause and Effect. Basic Books New York, 2018.
- Rafael A. Irizarry: Introduction to Data Science, Data Analysis and Prediction Algorithms with R (book, course).
There are many great data science courses offered around the world, and the best ones are free:
- Data 8 at UC Berkeley
- Stefanie Molin's workshops
- Marcel Lüthi's Amazing Python Course
Contact
Lecturer
Prof. Dr. Ivan Dokmanić: ivan.dokmanic[at]unibas.ch
Teaching assistants
Fabian Kruse: fabian.kruse[at]unibas.ch
Till Muser: till.muser[at]unibas.ch
Mark Starzynski: mark.starzynski[at]unibas.ch
Giovanni Utzeri: giovanni.utzeri[at]unibas.ch