Home → Magazine Archive → December 2013 (Vol. 56, No. 12) → Data Science and Prediction → Abstract

Data Science and Prediction

By Vasant Dhar

Communications of the ACM, Vol. 56 No. 12, Pages 64-73
10.1145/2500499

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Use of the term "data science" is increasingly common, as is "big data." But what does it mean? Is there something unique about it? What skills do "data scientists" need to be productive in a world deluged by data? What are the implications for scientific inquiry? Here, I address these questions from the perspective of predictive modeling.

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The term "science" implies knowledge gained through systematic study. In one definition, it is a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions.11 Data science might therefore imply a focus involving data and, by extension, statistics, or the systematic study of the organization, properties, and analysis of data and its role in inference, including our confidence in the inference. Why then do we need a new term like data science when we have had statistics for centuries? The fact that we now have huge amounts of data should not in and of itself justify the need for a new term.

2 Comments

Ben Shneiderman

A strong and interesting article on data science, but how is it possible that information visualization is never mentioned? The focus on enabling "the system to understand" undermines the value of this thoughtful analysis, when the author could easily have emphasized human control, insight, and responsibility, even for high-speed trading and other so-called "autonomous systems".

David Levine

I believe this article confounds information with knowledge. The progression should be from data -> information -> knowledge. Both man and machine can have all 3, but it's important to distinguish between these different words when discussing these topics.

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