Introduction Data science continues to generate excitement and yet real-world results can often disappoint business stakeholders. How can we mitigate risk and ensure results match expectations? Working as a technical data scientist at the interface between R&D and commercial operations has given me an insight into the traps

Debate hosted at the Royal Statistical Society, 19th May 2015. Zoubin Ghahramani (Professor of Machine Learning, University of Cambridge) Chris Wiggins (Chief Data Scientist, New York Times) David Hand (Emeritus Professor of Mathematics, Imperial College) Francine Bennett (Founder, Mastodon-C) Patrick Wolfe (Professor of Statistics, UCL / Executive Director, UCL Big Data

Whitepaper about errors in A/B testing, written for Qubit. Covered at qz.com [http://qz.com/181582/bad-ab-testing-is-worse-than-none-at-all/] and Hacker News [https://news.ycombinator.com/item?id=7287665] Introduction Marketers have begun to question the value of A/B testing, asking: ‘Where is my 20% uplift? Why doesn’t

Talk from PyData London 2014 Most Winning A/B Test Results are Illusory Talk Summary Many people have started to suspect that their A/B testing results are not what they seem. A/B test reports an uplift of 20% and yet this increase never seems to translate into increased

Reposted from Google+ (Mar 30, 2014) So, the inevitable backlash against 'Big Data' has begun. In an article in the Financial Times yesterday, journalist Tim Harford suggests that Big Data analyses are more dangerous than we thought [http://www.ft.com/cms/s/2/21a6e7d8-b479-11e3-a09a-00144feabdc0.html#axzz2xS1VXiUc] . He

It starts like a typical data science job interview - I summarise my resume and they describe their core products. They describe what their data looks like and we have a very interesting chat about the structure of their data. But the interviewer keeps dancing around the topic of what