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PyData DC 2016

  1. [Using Dask for Parallel Computing in Python] (http://pydata.org/dc2016/schedule/presentation/59/) | [[Code]] (https://github.com/jseabold/dask-pydata-dc-2016)

  2. [Building Your First Data Pipelines] (http://pydata.org/dc2016/schedule/presentation/10/) | [[Code]] (https://github.com/hunterowens/data-pipelines)

  3. [Doing frequentist statistics in Python] (http://pydata.org/dc2016/schedule/presentation/9/) | [[Code]] (https://github.com/gapatino/Doing-frequentist-statistics-with-Scipy)

  4. [Machine Learning with Text in scikit-learn] (http://pydata.org/dc2016/schedule/presentation/12/) | [[Code]] (https://github.com/justmarkham/pydata-dc-2016-tutorial)

  5. [Julia Tutorial] (http://pydata.org/dc2016/schedule/presentation/72/) | [[Code]] (https://github.com/cc7768/PyDataDC_julia)

  6. [Parallel Python - Analyzing Large Datasets] (http://pydata.org/dc2016/schedule/presentation/8/) | [[Code]] (https://github.com/mrocklin/scipy-2016-parallel)

  7. [Modern NLP in Python] (http://pydata.org/dc2016/schedule/presentation/11/) | [[Code]] (https://github.com/skipgram/modern-nlp-in-python)

  8. [Python useRs] (http://pydata.org/dc2016/schedule/presentation/43/) | [[Code]] (https://github.com/chendaniely/2016-pydata-dc-python_useRs)

  9. [Building Serverless Machine Learning Models in the Cloud] (http://pydata.org/dc2016/schedule/presentation/33/) | [[Code]] (https://github.com/cloudacademy/sentiment-analysis-aws-lambda)

  10. [Learn How To Make Life Easier With Anaconda] (http://pydata.org/dc2016/schedule/presentation/76/) | [[Code]] (https://github.com/dhavide/PyData-DC-2016-Anaconda)

  11. [The Five Kinds of Python Functions] (http://pydata.org/dc2016/schedule/presentation/14/) | [[Slides]] (https://slott56.github.io/five-kinds-of-python-functions/assets/player/KeynoteDHTMLPlayer.html)

  12. [Sustainable Scrapers] (http://pydata.org/dc2016/schedule/presentation/38/) | [[Slides]] (https://docs.google.com/presentation/d/1jCFVPffHs8bVynMPctd0PmtbJAdFhHvpcC3w2ypQjpA/edit#slide=id.p)

  13. [Open Data Dashboards & Python Web Scraping] (http://pydata.org/dc2016/schedule/presentation/34/) | [[Slides]] (https://github.com/mseew/Presentation-Slides/blob/master/pyData_MCW.pdf) | [[Code]] (https://github.com/mseew/DM-Dashboard)

  14. [Agent-based Modeling in Python] (http://pydata.org/dc2016/schedule/presentation/28/) | [[Code]] (https://github.com/projectmesa/Mesa)

  15. [Variational Inference in Python] (http://pydata.org/dc2016/schedule/presentation/47/) | [[Slides]] (http://austinrochford.com/resources/talks/dydata-dc-2016-variational-python.slides.html#/) | [[Jupyter NB]] (https://nbviewer.jupyter.org/gist/AustinRochford/91cabfd2e1eecf9049774ce529ba4c16) | [[Code]] (https://gist.github.com/AustinRochford/910c506cebbec530328d4aa5c5c79cef)

  16. [Clustering: A Guide for the Perplexed] (http://pydata.org/dc2016/schedule/presentation/19/) | [[Slides]] (https://github.com/jc-healy/Presentations/blob/gh-pages/PyDataDC2016%20Clustering.pdf) | [[Code]] (https://github.com/scikit-learn-contrib/hdbscan)

  17. [Logistic Regression: Behind The Scenes] (http://pydata.org/dc2016/schedule/presentation/37/) | [[Slides]] (http://www.slideshare.net/ChrisWhite249/logistic-regression-behind-the-scenes)

  18. [Visual diagnostics for more informed machine learning] (http://pydata.org/dc2016/schedule/presentation/39/) | [[Slides]] (https://rebeccabilbro.github.io/pydata/#/) | [[Code]] (https://github.com/DistrictDataLabs/yellowbrick)

  19. [Creating Python Data Pipelines in the Cloud] (http://pydata.org/dc2016/schedule/presentation/16/) | [[Slides]] (https://github.com/femibyte/data-eng/blob/master/PyData2016-DataPipelinesCloud.pdf)

  20. [Data Transformation: A Framework for Exploratory Data Analysis] (http://pydata.org/dc2016/schedule/presentation/32/) | [[Jupyter NB]] (https://github.com/ojedatony1616/exploratory_transformation/blob/master/Transforming%20Data%20to%20Unlock%20Its%20Latent%20Value.ipynb)

  21. [Interactive multi-scale time series exploration with matplotlib] (http://pydata.org/dc2016/schedule/presentation/79/) | [[Code]] (https://github.com/tacaswell/interactive_mpl_tutorial)

  22. [Forecasting critical food violations at restaurants using open data] (http://pydata.org/dc2016/schedule/presentation/35/) | [[Slides]] (http://www.slideshare.net/NicoleDonnelly6/pydatadc-forecasting-critical-food-violations-at-restaurants-using-open-data) | [[Code]] (https://github.com/nd1/DC_RestaurantViolationForecasting)

  23. [GraphGen: Conducting Graph Analytics over Relational Databases] (http://pydata.org/dc2016/schedule/presentation/57/) | [[Project]] (http://konstantinosx.github.io/graphgen-project/)

  24. [NoSQL doesn't mean No Schema] (http://pydata.org/dc2016/schedule/presentation/40/) | [[Slides]] (https://slott56.github.io/no-sql-doesnt-mean-no-schema/assets/player/KeynoteDHTMLPlayer.html#0)

  25. [Dask for ad-hoc distributed computing] (http://pydata.org/dc2016/schedule/presentation/48/) | [[Slides]] (http://matthewrocklin.com/slides/pydata-dc-2016#/)

  26. [Eat Your Vegetables - Data Security for Data Scientists] (http://pydata.org/dc2016/schedule/presentation/50/) | [[Slides]] (http://www.slideshare.net/WilliamVoorhees1/eat-your-vegetables-data-security-for-data-scientists)

  27. [Becoming a Data Scientist:Advice From My Podcast Guests] (http://pydata.org/dc2016/schedule/presentation/30/) | [[Slides]] (http://www.becomingadatascientist.com/wp-content/uploads/2016/10/Becoming-a-Data-Scientist-Advice-PyDataDC-shared.pdf)

YouTube

Playlist with all 61 talks [here] (https://www.youtube.com/playlist?list=PLGVZCDnMOq0qLoYpkeySVtfdbQg1A_GiB).