Skip to content

fdion/pydata

 
 

Repository files navigation

PyData Carolinas 2016 Tutorial: Datascience on the web

Welcome to Datascience on the web, with Don and Francois.

You have been given a paper with a URL in the form: https://xxxxx-xxx.dionresearch.com:8088

Point your browser to it and type the accompanying password.

Problems? Raise your hand and somebody will help you, even perhaps your neighbor. Also, feel free to tweet about this session. I am @f_dion and this is #datascience and #flask at #pydatacarolinas.

After the fact

The unrefactored notebook is here while the refactored one is here.

Once you run through the whole refactored notebook, you will have train and test sets saved in data/ and a trained model in trained_models/. To make these available in the tutorial directory, you will have to run the publish.sh script. On a unix like environment (mac, linux etc):

chmod a+x publish.sh
./publish.sh

Video

The whole session is now on youtube: Francois Dion & Don Jennings Datascience on the web

Updates

This repository will get a few more files after the tutorial, including some PDFs. Make sure you watch the repo if you want the latest information.

The basics

The Machine Learning

The visualization

Other options for jupyter and flask compatible visualizations:

Further reading

Check out these awesome lists:

And this video for building APIs with flask-restplus: you-tube video

Automating the basics

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 96.6%
  • HTML 1.7%
  • Python 1.7%