plipp/informatica-pfr-2017

Python for Research Workshop at the Informatica 2017

Workshop: Python in Science and Research

Dive into the following topics

• Numeric Computation
• Data Visualization
  • basic plotting
• Machine Learning
• Social Network Analysis

Up Front Preparations

As this is a notebook class, you should try to setup Python and Git on your machine before we start with the actual workshop.

Git Installation

The course will be available in github. The most convenient way to fetch it and updates of it from there is installing a Git Client.

Please refer to Basic Git for further information, how to work with git.

Python Distribution Installation

The easiest way to do so is by installing the Anaconda Distribution. Just download and install the current distribution with Python 3.6 or higher as described in the Anaconda Installation Instructions.

Then test the Anaconda installation by running your first notebook: Therefore

• git clone https://github.com/plipp/informatica-pfr-2017.git and
• run in a terminal:

$ jupyter-notebook

, open in your browser The Jupyter Notebook for the initial Installation Test and run it ... If all runs through and you see as output in the last cell All looks good! you are done!

Additional Python Package Installation

Further on in the workshop the following additional packages are required. If you don't succeed to install them upfront, don't worry: If help is needed, we will have enough time to install them together in the course.

    conda install seaborn

    conda install -c scitools cartopy=0.15.0
    # or
    conda install -c conda-forge cartopy=0.15.1
    
    conda install networkx
    pip install python-louvain

    pip install graphviz
    
    pip install xlrd

On OS-Level you also will need graphviz. Please check the Graphviz Homepage about how to install on your Machine.

You can check, whether the additional packages also work fine, with The Jupyter Notebook for the Test of the additional Components.

Refresher - Python(3) Basics

If you need a refresher of your Python knowledge the Interactive Python Tutorial is a good starting point.

The Lectures

• Learn the Basics: All and
• Advanced Tutorials
  • Generators
  • List Comprehensions
  • Multiple Function Arguments
  • Sets
  • Partial functions

should be sufficient for attending the Workshop IF PRO 02 Python für Wissenschaft und Forschung.

Also A whirlwind tour of Python is a good starting point.

Further Reading/Resources

This Workshop is inspired by

MOOCs

In Python

• edX - Python for Research (free)
• Coursera - Applied Data Science with Python Specialization (free audit)
• Udacity - Intro to Data Analysis (free)

In R

• The Analytics Edge

Primers

• Python Data Science Primer (free)

Books

• Packt - Practical Datascience Cookbook

Free Books/Juypter-Notebooks

• Python Data Science Handbook
• Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney(jupyter-nbs free)

Library Documentation

• Numpy
• Pandas
• Matplotlib
• NetworkX