Data Analysis in Python

Content

Python is a high-level dynamic object-oriented programming language. It is easy to learn, intuitive, well documented, very readable and extremely powerful. Python is packaged with an impressive standard library following the so called "batteries included" philosophy. Together with the large number of additionally available scientific packages like NumPy, SciPy, pandas, matplotlib, etc., Python becomes a very well suited programming language for data analysis.

One more thing to mention is the possibility to easily integrate C, C++ or even FORTRAN code into Python, which can be used to optimize computational bottlenecks by moving the code to a lower-level compiled language. Cython, a compiler for Python code, is one of the standard ways to transform Python code into fast compiled low-level extensions and to interface already existing C/C++ code.

This hands-on session introduces the pythonic way of programming, demonstrates the power of Python in data analysis and gives a brief glimpse of developing performant code in Python using Cython.

Required Programming Skills

• Knowledge of basic concepts of a programming language. (i.e. for-loops, while-loops, if-else-statements, ...)

Technical Requirements for the Course

You can either use the pre-installed nodes at GridKa or your private notebook, which will be probably more comfortable for you.

The pre-installed nodes at GridKa

You need:

• a ssh client installed on your laptop
• a web browser installed on your laptop

Your private notebook

You can download the anaconda package from http://continuum.io/downloads and follow the installation instructions. In addition, you need to have a git client installed.