Übung P 10.2 Practical Course Introduction to Scientific Programming in Python

Intended Audience: Science students of all education levels (Bachelor, Masters, PhD, and Post-doc) who are relatively new to programming or only have experience with Matlab or R. Description: In this intensive course, we will cover the basics of the Python programming language and some of its scientific library in order to enable students to conduct data analysis: - reading various file types - manipulating the data using common data structures - calculating statistics - making figures for publication. Libraries covered will be NumPy, MatPlotLib, Pandas, and Scipy Stats. Required Homework assignments between each session will be used to integrate new techniques with real-life problems that the practicing scientist encounters in his/her work, and course sessions will be fully interactive to increase the retention level of each student. Required Materials: Laptop Computer, brought to each class day. Course Overview: Day 1: Installation of Scientific Python Software Stack and Introduction to Python Built-in Collection Types Day 2: Scripts, Functions, and Modules. Day 3: Programming Flow Control Day 4: Package Imports, Documentation, and Namespaces Day 5: Jupyter Notebook and Matrix Manipulation with NumPy Day 6: Figure Creation with MatPlotLib Day 7: More Powerful Data Processing with DataFrames: Intro. to Pandas Day 8: Statistical Analysis with Scipy Stats and Course Review Reccomended Preparation: Online Python Tutorial at codeacademy.com Reccomended Literature: -Python for Data Analysis- by Wes McKinney Sirota, Anton , Univ.Prof.Dr. LMU München SoSe 2016 Del Grosso Nicholas