This Git repository holds the material for the course Practical Data Analysis with Python by Dr. Andreas Hilboll, given at the University of Bremen during summer term 2018.

Time

Friday, 10:15 - 12:00

Dates

06 Apr 2018 - 06 Jul 2018

Location

NW1 / S3032

This course aims to be a very practical introduction to using a computer in scientific data analysis, using the Python_ programming language. The course will be most beneficial to third semester students, as they will be able to directly apply the newly learned techniques in their M.Sc. thesis work; of course, interested and motivated students can also join in their first semester.

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 exercises). There will be weekly homework excercises, plus two graded homework projects.