Code and material used in PyData DC 2016 talks

PyDataDC2016_SharedResources

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

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

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

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

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

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

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

8. Python useRs (http://pydata.org/dc2016/schedule/presentation/43/)
Github: 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/)
Github: https://github.com/cloudacademy/sentiment-analysis-aws-lambda

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