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.
|2||1/3||Lecture notebook: tidy-data-in-pandas (files)|
Exercise session 2, video
|Lecture notebooks: billboard (files), matplotlib-intro (files)|
Exercise session 3 video
|Lecture notebook: seaborn-and-more (files)|
Exercise session 4 video
|Lecture notebook: Lecture5 (files)|
Exercise session 5 video
|Lecture notebook: monty-hall (files)|
Exercise session 6 video
|Lecture notebook: towards-clt (files)|
Exercise session 7 video
|Lecture notebook: walk-bowel (files), bootstrap (files)|
Exercise session 8 video
|Lecture notebook: confidence intervals (files)|
|11||10/5||Brief p-values and beginning regression (video 1, video 2, slides)|
|12||17/5||Regression, confounding (video 1, video 2, slides)||Lecture notebook: correlation-regression (files)|
|13||24/5||Intro to ML (video 1, video 2, slides)||Lecture notebook: ML (files)|
|14||31/5||Practice exam, correct answers||Lecture notebook: ML (files)|
|24/6||Final exam @ Biozentrum||
Communication and Collaborative Reading
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!
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.
There are many great data science courses offered around the world, and the best ones are free:
Prof. Dr. Ivan Dokmanić: ivan.dokmanic[at]unibas.ch
Phaina Koncebovski: phaina.koncebovski[at]stud.unibas.ch
AmirEhsan Khorashadizadeh: amir.kh[at]unibas.ch
Valentin Debarnot: valentin.debarnot[at]unibas.ch