Python for Data Scientists

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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, Matplotlib, SciPy, Pandas, Scikit-learn, PySpark, PyHive. Accessing RDBMSs
• Writing efficient Python: Cython, Numba, SWIG
• Pointers for further learning

The course will be taught by Patrick Varilly of Data Minded. Patrick fell in love with Python four years ago as a theoretical chemistry post-doc at Cambridge and has never looked back. He has contributed to SciPy and used Python in wide-ranging settings, from scientific libraries to model proof-of-concepts to data backend pipelines.

Pizza will be provided courtesy of Data Minded.