Why do we need Data Science when we’ve had Statistics for centuries?

Published September 12, 2014   |   

Data Science is emerging as as one of the hottest new professions and academic disciplines in these early years of the 21st century.  A number of articles have noted that the demand for data scientists is racing ahead of supply.  People with the necessary skills are scarce, primarily because the discipline is so new.  But, the situation is rapidly changing, as universities around the world have started to offer different kinds of graduate programs in data science.  This year, for example, NYU is offering two new degrees, – a general Master in Data Science, and a more domain-specific Master in Applied Urban Science and Informatics.

It’s very exciting to contemplate the emergence of a major new discipline.  It reminds me of the advent of computer science in the 1960s and 1970s.  Like data science, computer science had its roots in a number of related areas, including math, engineering and management.  In its early years, the field attracted people from a variety of other disciplines who started out using computers in their work or studies, and eventually switched to computer science from their original field.

This was the case with me.  I used computers extensively while a student at the University of Chicago, where I worked closely with Professor Clemens Roothaan, – one of the pioneers in the use of computers in physics and chemistry.  As an undergraduate, I worked part-time at the university’s supercomputing center which he founded, and later he was my thesis advisor as a graduate student in physics.  When the time came to look for a job, I realized that I enjoyed the computing side of my work more than the physics.  I decided to switch fields and in 1970 joined the computer science department at IBM’s Watson Research Center.

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