These are the slides and notebooks used during the meetup #dsGhent: Python for Data Scientists (sessions one and two). The event took place at the University of Ghent in Ghent on Thu 10 Nov 2016 and Thu 17 Nov 2016. It's itself an updated version of the materials I had used for a previous meetup at the European Data Innovation Hub in Brussels on Thu 17 Sep 2015

Most of the material here is either directly from or closely adapted from other sources. In particular, the overview closely follows the chapter 1 of "Python: Essential Reference" (4th edition), by David Beazley and the Scikit.learn and Pandas notebooks owe a lot to Jake Vanderplas' tutorial notebooks on GitHub.

In the past few years, Python has emerged as a solid platform for data science. Couple a mature, clean and expressive language with powerful, fully-featured libraries for data wrangling and machine learning, and you're set up for maximum productivity. Easily ingest your data from practically anywhere using one of Python's thousands of free libraries. Effortlessly turn hundreds of convoluted lines of obscure model code into just a few lines of near-English prose. Add a few annotations and get maximum performance without drowning in pools of unnecessary boilerplate code. Present your results in beautiful living notebooks that seamlessly mix text, code and graphs. Whether you do all your modeling in R, you've written nothing but Matlab since university, or you swear by C# or (gasp!) Java, discovering Python will be a wonderful experience.

In detail, we plan to cover the following points:

• Quick history of Python and typical use cases

• Key advantages and disadvantages of Python for data science

• Ways to run python and write code

• Quick tour of language

• Showcase of useful language packages for data science:

• NumPy
• SciPy
• Matplotlib
• Seaborn
• Pandas
• Scikit-learn

• Writing efficient Python:

• Cython
• Numba

• Pointers for further learning: follow links in the notebooks