Tuesday, October 11, 2016

PyData Carolinas 2016 Tutorial: Datascience on the web

PyData Carolinas 2016

Don Jennings and I presented a tutorial at PyData Carolinas 2016: Datascience on the web.

The plan was as follow:


Learn to deploy your research as a web application. You have been using Jupyter and Python to do some interesting research, build models, visualize results. In this tutorial, you’ll learn how to easily go from a notebook to a Flask web application which you can share.


Jupyter is a great notebook environment for Python based data science and exploratory data analysis. You can share the notebooks via a github repository, as html or even on the web using something like JupyterHub. How can we turn the work we have done in the notebook into a real web application?
In this tutorial, you will learn to structure your notebook for web deployment, how to create a skeleton Flask application, add a model and add a visualization. While you have some experience with Jupyter and Python, you do not have any previous web application experience.
Bring your laptop and you will be able to do all of these hands-on things:
  1. get to the virtual environment
  2. review the Jupyter notebook
  3. refactor for reuse
  4. create a basic Flask application
  5. bring in the model
  6. add the visualization
  7. profit!
Now that is has been presented, the artifacts are a github repo and a youtube video.

Github Repo


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


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

Francois Dion

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