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Co-founder @ RMOTR

Functional Programming for Data Scientists

Last updated: November 2nd, 20182018-11-02Project preview

Functional Programming for Data Scientists

This tutorial is based on my talk at PyData NYC 2018. The slides of the talk are publicly available in google drive.

This tutorial contains executable code. You can run it interactively by "forking" this project with the button on the right 👉

This is a comprehensive tutorial of Functional Programming in Python. It is originally thought for Data Scientists but it can be also useful for people in other disciplines.

Why is Functional Programming important to Data Scientists?

Functional programming is usually thought as a hardcore topic exclusive for developers. The Data Scientists that I know (my students taking our Data Science Course) don't want to be "developers", that's not their final objective. They want to use programming and computers to get their data-related work done (analysis, explorations, reports, forecasting, predictions, etc).

They usually think that FP is difficult and "too technical" and that it won't make them a difference. That's not true; FP is simple and intuitive and provides huge advantages for people applying its concepts, as we'll explore throughout this tutorial.

The Theory of Everything

Once important advantage of Functional Programming is that we'll be able to use the same concepts for small pieces of code (a tiny function or script) or big data pipelines and ETL pipelines. By learning the FP abstractions, we'll be able to maintain a consistent state of mind, regardless of the "size" of the application.

Index and how this tutorial is structured

This tutorial is structured in a set of different notebooks. You can click on the index on the right 👉 or just fork the entire project and use the Jupyter Lab system to explore and execute them.

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