**PyDataDC2016 was held from October 7th-9th 2016.**

Check out the schedule: http://pydata.org/dc2016/schedule/

Here are the links to some of the talks.

The talks have been grouped by the following categories:

**Directly Python Related | Machine Learning | Data Analysis | Database Related |**

**NLP | Scheduling | Security | Other Topics**

**Talks**

**Directly Python Related**

**The 5 Kinds of Python Functions: Steven Lott**

Slides: https://slott56.github.io/five-kinds-of-python-functions/assets/player/KeynoteDHTMLPlayer.html

**Learn How to Make Life Easier with Anaconda: Dhavide Arulia**

Twitter: @dhavidearuliah

**Slides (pdf)**

Jupyter Notebooks & Data: https://github.com/dhavide/PyData-DC-2016-Anaconda

**Sustainable Scrapers:**David Eads / @eads

Google Docs

**Open Data Dashboards & Python Web Scraping: Marie Whittaker**

Twitter: @MarieCWhittaker

Presentation: https://github.com/mseew/Presentation-Slides/blob/master/pyData_MCW.pdf

Github: https://github.com/mseew/DM-Dashboard

**Agent-based Modeling in Python: Jackie Kazil**

**(Mesa Framework)**

Twitter: @JackieKazil

Github: https://github.com/projectmesa/Mesa

**Machine Learning Related**

**Variational Inference in Python: Austin Rochford**

Twitter: @AustinRochford

Slides: http://austinrochford.com/resources/talks/dydata-dc-2016-variational-python.slides.html#/ … Jupyter notebook 1:

https://nbviewer.jupyter.org/gist/AustinRochford/91cabfd2e1eecf9049774ce529ba4c16

Jupyter Notebook 2: Dependent Dirichlet Process Regression

**Clustering talk (McInnes & Healy)**

HDBScan

Twitter: @dvbalen

Jupyter Notebooks: https://github.com/scikit-learn-contrib/hdbscan

**Logistic Regression: Behind The Scenes: Chris White**

Twitter: @markov_gainz

Slides: http://www.slideshare.net/ChrisWhite249/logistic-regression-behind-the-scenes

**Visual Diagnostics for more informed Machine Learning: Rebecca Bilbro**

Yellowbrick

Twitter: @RebeccaBilbro

Slides: https://rebeccabilbro.github.io/pydata/#/

Github: https://github.com/DistrictDataLabs/yellowbrick

**Building Serveless ML Models in the Cloud: Alex Casalboni**

Github: https://github.com/cloudacademy/sentiment-analysis-aws-lambda

**Data Exploration & Analysis**

**Building Your First Data Pipelines: Hunter Owens**

Twitter: @hunter_owens

Presentation: http://hunterowens.net/data-pipelines/presentation/#/

Github: https://github.com/hunterowens/data-pipelines

**Creating Python Data Pipelines in the Cloud: Femi Anthony**

Twitter: @DataPhanatik

Slides: https://github.com/femibyte/data-eng/blob/master/PyData2016-DataPipelinesCloud.pdf

Github: See the references (last slide) in the presentation above

**Parallel Python - Analyzing Large Data Sets: Aron Ahmadia, Matthew Rocklin**

Github: https://github.com/mrocklin/scipy-2016-parallel

**Transforming Data to Unlock Its Latent Value: Tony Ojeda**

EDA Framework

Twitter: @tonyojeda3

Jupyter Notebook

**Time series exploration with matplotlib: Thomas Caswell**

Twitter: @tacaswell

**Forecasting Critical Food Violations at Restaurants using Open Data: Nicole Donnelly**

Twitter: @NicoleADonnelly

Github:https://github.com/nd1/DC_RestaurantViolationForecasting

**Doing Frequentist Statistics in Python: Gustavo A. Patino**

Github: https://github.com/gapatino/Doing-frequentist-statistics-with-Scipy

**Database Related**

**NoSQL doesn't mean No Schema: Steven Lott**

Twitter: @s_lott

**Presentation**

**GraphGen**: Conducting Graph Analytics Over Relational Databases

Twitter:

**Benjamin Bengfort**@bbengfort

http://konstantinosx.github.io/graphgen-project/

**Natural Language Processing**

**What you can learn about food by analyzing a million Yelp reviews: Patrick Harrison**

**Machine Learning with Text in scikit-learn: Kevin Markham**

Github: https://github.com/justmarkham/pydata-dc-2016-tutorial

**Scheduling Related**

**Dask: Fine Grained Task Parallelism: Matthew Rocklin**

**Matthew Rocklin**@mrocklin

**Security**

**Eat Your Vegetables: Data Security for Data Scientists: William Vorhees**

**Other Topics**

**Keynote: A Dept of Commerce Conundrum: Star Ying**

Slides: http://www.slideshare.net/StarYing1/pydata-dc-2016-a-doc-conundrum

**Becoming a Data Scientist: Advice From My Podcast Guests: Renee Teate**

Twitter: @BecomingDataSci

**Python Users: Daniel Chen**

Github: https://github.com/chendaniely/2016-pydata-dc-python_useRs

**Semi-autonomous Drone: YHat**

https://github.com/yhat/semi-autonomous-drone

**Data Sciencing while Female: Amanda Traud**

Slides: Google Doc

Shiny App: https://netmandi.shinyapps.io/DSMeetups/

**Julia Tutorial: Chase Coleman**

Github:https://github.com/cc7768/PyDataDC_julia

Thanks to Bhavika Tekwani & Renee Teate for help with a number of these links.