This year I was fortunate enough to speak at PyCon Ireland 2016 in Dublin. This was a great event with lots of interesting Python based talks and a full PyData track over the two days. The topic of my talk was something I’ve been thinking about a lot over the last few years: how data scientists can work with other disciplines. Recently designers and product managers have begun working more closely with development teams, and in my opinion there are many lessons that data scientists can learn from this experience.

In the previous two posts in this series we have explored the values and principles outlined in Extreme Programming Explained by Kent Beck and Cynthia Anders (XP Explained). In this post we will discuss perhaps the most contentious of the three layers: practices.

What relevance do XP practices have for data scientists? What are the benefits and disadvantages of these practices? Are there any that we should modify for a data science environment?

The values described in Extreme Programming Explained are the foundation on which principles and practices are built. Last time we started to talk about those values through the lens of data science.

Today I’m going to describe some of the principles outlined by Beck and Anders, again asking whether they are applicable to data science, and whether data science could benefit from their application.

A new event in the Pivotal London office this week was our first lunchtime book club meeting. The first book suggested for discussion was Kent Beck and Cynthia Andres' Extreme Programming (XP) Explained (2nd Edition).

This is a classic of the agile programming community and Kent Beck’s shorter first edition (1999) can lay claim to being one of the first books about agile programming practices. In this long post I’m going to start discussing the message of the book, and how I think some of the ideas apply in the world of data science. [Update: The next parts of this post about principles and practices are now up.]