Data Science and Statistics: different worlds?
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 Institute)
Zoubin Ghahramani (Professor of Machine Learning, University of Cambridge)
Chair: Martin Goodson (Vice-President Data Science, Skimlinks)
Discussant: John Pullinger (UK National Statistician)
In the last few years data science has become an increasingly popular discipline. Often linked to the use and analysis of ‘big data’, data scientists are seen as the new professionals who can unlock the potential of an increasingly data-rich world, and to generate economic and social benefits from the data revolution.
However within the world of statistics, the ‘big data’ and ‘data scientist’ developments are sometimes labelled as hypes, and ‘data science’ is seen as a rebranding of what should be statistics. One of the often heard criticisms of big data analytics is that there’s a lack of statistical rigour which can lead to the wrong decisions.
As with any new discipline there are questions about exactly what data science is. Has the relevance of statistics been diminished because of new types of data or technologies which need a radical new approach? Is data science about ‘getting the job done’, and statistics about the deeper scientific understanding? Are our universities offering students the right skill sets to meet the high demand for data scientists?