Civis at PyData Chicago 2016

October 18, 2016 Michael Heilman

Data science is an interdisciplinary endeavor. It requires a variety of technical knowledge, from computer science to statistics to data visualization. While conferences are a great way to gain such knowledge, many conferences focus only on particular aspects of data science (e.g., NIPS for machine learning, or PyCon for Python programming), and so one would have to go to several different conferences to keep up with the latest data science.

That’s one reason why we at Civis were excited that PyData recently hosted its first conference in Chicago, PyData Chicago 2016. PyData is great because it brings together people who are developing and using data science tools to talk about both theoretical and practical challenges. Also, the scope is fairly broad (e.g., with talks on data engineering, distributed computing, machine learning, time series analysis, etc.), so it’s a bit like going to several conferences all at once.

Civis played a big role in the conference: Skipper Seabold and Stephen Hoover helped organize it; several other people from Civis attended; we co-sponsored it with some other great organizations; and we had four presentations (listed below).

We had a great time, learned a lot, and are looking forward to the next PyData Chicago conference. Hopefully, we’ll see you there!

PyData Chicago 2016 Talks by Civis

“Using Dask for Parallel Computing in Python” by Skipper Seabold [slides]

“Deconstructing Feather” by Bill Lattner [slides]

“Implementing distributed grid search for deep learning using TensorFlow, scikit-learn, and joblib” by Mike Heilman [slides]

“Evolutionary Algorithms: Perfecting the Art of ‘Good Enough’” by Liz Sander (who was doing an internship at Civis at the time) [slides]

Notes

 

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