Practical Data Analysis with Python

Lecture content:

This course aims to be a very practical introduction to using a computer in scientific data analysis, using the Python programming language. The course is strongly recommended to all first semester students, as it will enable participants to successfully use a computer during the data analysis and homework assignments during the rest of their study.

The course will be accessible to all students, as it will not make any assumptions on the students' computer environment, i.e., all topics will be explained for all relevant operating systems (Windows, MacOS, Linux). Participants are expected to bring their own laptops, as the course contains large parts of practical work.

The first part of the course will touch on the following subjects:

- "But this worked yesterday, before I made some changes ...", or: an
introduction to version control.
- Getting started: How to setup your own computer for data analysis in Python.
- Hands-on introduction to the Python scientific ecosystem: Arrays and
mathematical operations.
- Labeled arrays, or how to intuitively work with data.
- Reading and writing data in common file formats.
- Making both beautiful and meaningful plots from data.
- An overview of the most common special-topic libraries for all research areas
covered by the /pep/program.

In its last sessions, the course will focus on a practical introduction to the most common data analysis tasks, like, among others, curve fitting, parameter estimation, and correlation analysis.

Every week, there will be 2 hours of course (approx. 1 hour lecture + 1 hour practical excercises). There will be homework excercises, plus two graded homework projects.

Reading material will be announced in the first session.