by Angela Guess
Steve Miller writes in Information Management, “I recently came across an outstanding article on data science, thanks to the always-informative R-bloggers website. Written by Stanford professor David Donoho, ‘50 years of Data Science’ views DS through an historical lens and as well provides a conceptual framework for the evolving discipline. The point of departure for 50 years is the current squabble in the data industry as to whether data science is really the same as traditional statistics. I’ve argued many times that the disciplines are quite different, but, alas, others disagree. Donoho, a statistician, believes the fields are different. His starting point is the simple but elegant depiction of data science as the science of learning from data. Most definitions today, mine included, focus on skills – the ‘industrial’ – rather than the basic academic or ‘intellectual’ foundations, which are independent of particular technologies and algorithms.”
Miller goes on. “50 Years pays homage to the long-standing debate on the perils of an overly-mathematicized statistical discipline, deftly detailed by John Tukey in his vanguard 1962 paper, ‘The Future of Data Analysis’. There, Tukey opined, ‘as I have watched mathematical statistics evolve, I have had cause to wonder and to doubt… All in all I have come to feel that my central interest is in data analysis, which I take to include, among other things: procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate.’ Tukey’s depiction of ‘data analysis’ as engaging ‘1. The formal theories of statistics 2. Accelerating developments in computers and display devices 3. The challenge, in many fields, of more and ever larger bodies of data 4. The emphasis on quantification in an ever wider variety of disciplines’ was remarkably prescient.”
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