What is Data Science?

There is much debate among scholars and practitioners about what data science is, and what it isn’t. Does it deal only with big data? What constitutes big data? Is data science really that new? How is it different from statistics and analytics?

One way to consider data science is as an evolutionary step in interdisciplinary fields like business analysis that incorporate computer science, modeling, statistics, analytics, and mathematics.

At its core, data science involves using automated methods to analyze massive amounts of data and to extract knowledge from them. With such automated methods turning up everywhere from genomics to high-energy physics, data science is helping to create new branches of science, and influencing areas of social science and the humanities. The trend is expected to accelerate in the coming years as data from mobile sensors, sophisticated instruments, the web, and more, grows. In academic research, we will see an increasingly large number of traditional disciplines spawning new sub-disciplines with the adjective "computational" or “quantitative” in front of them. In industry, we will see data science transforming everything from healthcare to media.
 
50x

in 2020 the world will generate 50 times the amount of data than in 2011 Source: emc.com
 
DATA
SCIENCE
Applications
Computer Science
Mathematical Statistics

Data-Driven Discovery

WHAT DATA SCIENCE MEANS FOR RESEARCH

In virtually all areas of intellectual inquiry, data science offers a powerful new approach to making discoveries. By combining aspects of statistics, computer science, applied mathematics, and visualization, data science can turn the vast amounts of data the digital age generates into new insights and new knowledge.

Click on the icons to the left to see how social scientists, medical researchers, and many others are using data science to advance their fields.
 
 

Data + Context

Drawing insight from a piece of data involves understanding how it fits into the larger picture of an organization, explains IBM’s Jeff Jonas, distinguished engineer and chief scientist for IBM Entity Analytics. Business environments aren’t the only ones that require context; context is a necessity for any attempt to know more by examining data.