Why Data Science Isn't an Exact Science - InformationWeek

Business professionals have traditionally viewed the world in concrete terms and sometimes even round numbers. That legacy perspective is black and white compared to the shades of gray that data science produces. Instead of producing a single number result such as 40%, the result is probabilistic, combining a level of confidence with a margin of error. In fact, there are several reasons why data science isn't an exact science, some of which are described below. "When we're doing data science effectively, we're using statistics to model the real world, and it's not clear that the statistical models we develop accurately describe what's going on in the real world," said Ben Moseley, associate professor of operations research at Carnegie Mellon University's Tepper School of Business.