├── README.md └── pydatadc.md /README.md: -------------------------------------------------------------------------------- 1 | # conf 2 | A collection of talks, workshop material and tutorials from conferences I attend 3 | 4 | 5 | ### [PyData DC 2016] (https://github.com/bhavikat/conf/blob/master/pydatadc.md) 6 | -------------------------------------------------------------------------------- /pydatadc.md: -------------------------------------------------------------------------------- 1 | # PyData DC 2016 2 | 3 | 1. [Using Dask for Parallel Computing in Python] (http://pydata.org/dc2016/schedule/presentation/59/) | [[Code]] (https://github.com/jseabold/dask-pydata-dc-2016) 4 | 5 | 2. [Building Your First Data Pipelines] (http://pydata.org/dc2016/schedule/presentation/10/) | [[Code]] (https://github.com/hunterowens/data-pipelines) 6 | 7 | 3. [Doing frequentist statistics in Python] (http://pydata.org/dc2016/schedule/presentation/9/) | [[Code]] (https://github.com/gapatino/Doing-frequentist-statistics-with-Scipy) 8 | 9 | 4. [Machine Learning with Text in scikit-learn] (http://pydata.org/dc2016/schedule/presentation/12/) | [[Code]] (https://github.com/justmarkham/pydata-dc-2016-tutorial) 10 | 11 | 5. [Julia Tutorial] (http://pydata.org/dc2016/schedule/presentation/72/) | [[Code]] (https://github.com/cc7768/PyDataDC_julia) 12 | 13 | 6. [Parallel Python - Analyzing Large Datasets] (http://pydata.org/dc2016/schedule/presentation/8/) | [[Code]] (https://github.com/mrocklin/scipy-2016-parallel) 14 | 15 | 7. [Modern NLP in Python] (http://pydata.org/dc2016/schedule/presentation/11/) | [[Code]] (https://github.com/skipgram/modern-nlp-in-python) 16 | 17 | 8. [Python useRs] (http://pydata.org/dc2016/schedule/presentation/43/) | [[Code]] (https://github.com/chendaniely/2016-pydata-dc-python_useRs) 18 | 19 | 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) 20 | 21 | 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) 22 | 23 | 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) 24 | 25 | 12. [Sustainable Scrapers] (http://pydata.org/dc2016/schedule/presentation/38/) | [[Slides]] (https://docs.google.com/presentation/d/1jCFVPffHs8bVynMPctd0PmtbJAdFhHvpcC3w2ypQjpA/edit#slide=id.p) 26 | 27 | 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) 28 | 29 | 14. [Agent-based Modeling in Python] (http://pydata.org/dc2016/schedule/presentation/28/) | [[Code]] (https://github.com/projectmesa/Mesa) 30 | 31 | 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) 32 | 33 | 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) 34 | 35 | 17. [Logistic Regression: Behind The Scenes] (http://pydata.org/dc2016/schedule/presentation/37/) | [[Slides]] (http://www.slideshare.net/ChrisWhite249/logistic-regression-behind-the-scenes) 36 | 37 | 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) 38 | 39 | 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) 40 | 41 | 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) 42 | 43 | 21. [Interactive multi-scale time series exploration with matplotlib] (http://pydata.org/dc2016/schedule/presentation/79/) | [[Code]] (https://github.com/tacaswell/interactive_mpl_tutorial) 44 | 45 | 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) 46 | 47 | 23. [GraphGen: Conducting Graph Analytics over Relational Databases] (http://pydata.org/dc2016/schedule/presentation/57/) | [[Project]] (http://konstantinosx.github.io/graphgen-project/) 48 | 49 | 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) 50 | 51 | 25. [Dask for ad-hoc distributed computing] (http://pydata.org/dc2016/schedule/presentation/48/) | [[Slides]] (http://matthewrocklin.com/slides/pydata-dc-2016#/) 52 | 53 | 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) 54 | 55 | 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) 56 | --------------------------------------------------------------------------------