Python For Science Python plays a pivotal role in the data science and academic communities. This is largely due to Python’s capabilities in terms of flexibility, simplicity, readability, and most importantly a strong basis of numerical libraries. The powerhouse libraries for numerical analysis are NumPy, SciPy, and Pandas and almost all higher level libraries, such Scikit-Learn, are built on top of them.
In this presentation, I’ll cover the strengths of Python’s numerical tools and how to best use them. We will cover basic statistics, optimization, data modeling, and basic machine learning practices. Industry and academic examples will be covered via IPython Notebook to demonstrate where the tools shine Speaker:
The speaker will be Eli Bressert. Eli is a data scientist at Stitch Fix, a board member and consultant for Authorea, and author of the O’Reilly book, “SciPy and NumPy”. He was a data scientist at Jawbone and a fellow at the Insight Data Science program in Silicon Valley. Previously, he received a post-doc fellowship in Australia investigating star-formation and young stellar clusters. Eli is one of the founding developers for two well-known astrophysics Python packages ATpy and APLpy.
*This is a Bay Area Python Interest Group (BayPIGgies) organized event. Please also see their web page: http://baypiggies.net/