I’m moving on from my current job so I wanted to capture some of the most important lessons I’ve learned during my time there. After nearly 7 years, there were a lot of lessons, so I challenged myself to narrow it down to a top 5-ish: Always Be Kind This is the top priority for me in any personal interaction or decision making. A key point is learning to distinguish between ‘nice’ actions and ‘kind’ ones.

So, after nearly 7 years, my time with Pivotal has come to an end. It’s been a great journey, and I’ve had the opportunity to work with some great people in all different parts of the business across the world. Thank you to everyone in and around Pivotal that made it so special! Pivotal has now become part of VMware Tanzu, and I wish everyone continuing the best of luck & success in the future, and I hope you can continue demonstrating the excellence, honesty, and above all kindness that was a key part of Pivotal.

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.]

I’m very happy to be giving a talk at the latest PyData conference in New York this weekend.

This is a long post but I wanted a place to collect all the code I am showing in my talk and to provide a few more resources for those interested in trying out Python on Cloud Foundry further.

Resources

• Simple Flask app
• Slides from the talk
• My new Cloud Foundry buildpack using conda
• Video of Flask app deployment
• Update: Video of PyData NY talk

What is Cloud Foundry?

My talk is about how to use Python and the PyData stack on Cloud Foundry the open source cloud platform. Cloud Foundry started life at VMware and development transferred to Pivotal when it was formed. Cloud Foundry has grown much bigger since then with over 30 companies joining together to form the Cloud Foundry Foundation which will guide the development of the open source project.

It looks like this is going to be a busy week. First up Noelle Sio, Alexander Kagoshima and I are presenting a webinar on Tuesday about our traffic analysis and prediction work. We talked about this topic at Strata Santa Clara but this webinar will take an extended look at the data, the challenges and the technology we used. You can sign up on the Pivotal website and the recording will be available afterwards.

I thought now would be a good time to reflect on 2013 and the changes that have happened in my life. Officially I have now been out of academia for a year. My postdoc contract ended at the end of 2012, and while I stayed on at QMUL as a visiting researcher, I took the opportunity to travel for a few months with my wife, before setting out to find a new job.