DATA SCIENCE : THE NERDS STRIKE AGAIN
Be nice to nerds. You may end up working for them. We all could.
By Charles J. SykesThe era when all the data a business gathered on itself could be accommodated by a single spreadsheet is over. Over the past year, interest in data science has soared. This data phenomen means one thing : “Business Needs Mathematicians nerds”.
Data scientists are highly educated – 88% have at least a Master’s degree and 46% have PhDs – and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist. Their most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). They caracterized by an exceptional Intellectual curiosity – Business acumen ( In terms of data science, being able to discern which problems are important to solve for the business is critical, in addition to identifying new ways the business should be leveraging its data.) – Communication skills – Companies searching for a strong data scientist are looking for someone who can clearly and fluently translate their technical findings to a non-technical team, such as the Marketing or Sales departments.
The companies, such as Amazon (AMZN) and Google (GOOG), that emerged and thrived after the first Web Wave did so not just because they were Web companies, but because they had solid businesses that offered business value to their customers and knew how to use or create the latest technologies. The current wave of business-tech companies also has business models targeting companies using the latest technologies. These business-tech firms need the people — the nerds — who not only know how to use these technologies, but also how to make them useful to their customers.
We are in an era where nerd-power may mean the difference between an overnight flame-out (business with “eyeballs” but not profit) and long-term business-tech success. Maybe some of these firms will emerge to be the next generation of high tech titans.
As proof of this shift, when General Motors was looking for someone to lead its global talent and organizational capability group, the $152 billion carmaker clearly wasn’t looking for a paper-pushing administrator. Michael Arena, who took the position in 2010, is an engineer by training. He was a visiting scientist at MIT Media Lab. He’s a Six Sigma black belt. He’s got a Ph.D. This is not your father’s human resources executive.
O’Neill’s description of the skills required of a data scientist are precisely those of a suitably well-educated statistician, even if he or she has only an undergraduate degree in the subject. Granted, Shaliz teaches at Carnegie Mellon, which is among the best engineering schools on the planet, so that’s not to say that everyone with a B.S. in statistics has mastered modern regression, advanced data analysis, data mining and statistical visualization.
This re-branding of statistical literacy as “data science” points out a larger trend – disciplines that were formerly the domain of the specialist, such as statistics, are now more important to a larger segment of the business world than ever. The fact that so few students view even a fraction of this level of mastery as necessary – and that schools often do not offer even a basic statistical education to non-math majors until the post-graduate level – suggests that in this area, perhaps even more than other areas associated with engineering, there is a yawning gap between the skills our workforce possesses and the skills employers require.
Physicists have long been drifting into Wall Street, which can use their mathematical abilities to manage hedge funds and the like. We are experimenting a similar drift of mathematicians into startups where business decision-making was formerly the sole domain of sales and C-level executives. This explain the success of the scientific method for management as it allows for organizational decisions—whether by business or government—to be formulated under more rigorous considerations. The quantitative approach to risk and decision making, with tools such as Palisade’s DecisionTools Suite, is one method for making management decisions with the aid of data and science.