Machine learning can feel pretty mysterious at times, but as python developers you have so many of the tools you need to be a part of it! With basic python experience you can use libraries like pandas and tools like Jupyter Notebooks to analyze and manipulate data sets. By apply Test-Driven Development practices to you analysis you can feel confident about what your building.

You can build well developed and well tested cleaning scripts and functions using pytest and use these functions in your notebooks and scripts.

You can even build simple recommendation engines using libraries such as Scikit Learn! 

As a part of this talk we will walk through the process of data analysis, data cleaning, feature preparation, and building a simple movie recommendation engine.  As we move through those steps, my main focus is to teach engineers how they can incorporate Test-Driven Development into the data cleaning process and the building of our engine.  I will also walk through strategies for data analysis and explain at a high level a couple ML concepts that we can use.

As participants get the chance to see live examples of how to use Test Driven Development in data analysis and machine learning they can get a handle on some core concepts and learn how to ensure quality in the code that they produce.