![]() Among faculty colleagues, he reports, “Half the members of the government department are doing some type of data analysis, along with much of the sociology department and a good fraction of economics, more than half of the School of Public Health, and a lot in the Medical School.” Even law has been seized by the movement to empirical research-“which is social science,” he says. “There is a movement of quantification rumbling across fields in academia and science, industry and government and nonprofits,” says King, who directs Harvard’s Institute for Quantitative Social Science (IQSS), a hub of expertise for interdisciplinary projects aimed at solving problems in human society. In SEAS, there is talk of organizing a master’s in data science. Faculty members have taken note: the Harvard School of Public Health (HSPH) will introduce a new master’s program in computational biology and quantitative genetics next year, likely a precursor to a Ph.D. A Harvard course in data science last fall attracted 400 students, from the schools of law, business, government, design, and medicine, as well from the College, the School of Engineering and Applied Sciences (SEAS), and even MIT. Among students, there is a huge appetite for the new field. Many of the tools now being developed can be used across disciplines as seemingly disparate as astronomy and medicine. And creative approaches to visualizing data-humans are far better than computers at seeing patterns-frequently prove integral to the process of creating knowledge. New ways of linking datasets have played a large role in generating new insights. Instead, King and his graduate students came up with an algorithm within two hours that would do the same thing in 20 minutes-on a laptop: a simple example, but illustrative. One colleague, faced with a mountain of data, figured out that he would need a $2-million computer to analyze it. The doubling of computing power every 18 months (Moore’s Law) “is nothing compared to a big algorithm”-a set of rules that can be used to solve a problem a thousand times faster than conventional computational methods could. ![]() The revolution lies in improved statistical and computational methods, not in the exponential growth of storage or even computational capacity, King explains. “The big data revolution is that now we can do something with the data.” But it is not the quantity of data that is revolutionary. “There is a big data revolution,” says Weatherhead University Professor Gary King. The data flow so fast that the total accumulation of the past two years-a zettabyte-dwarfs the prior record of human civilization. Data now stream from daily life: from phones and credit cards and televisions and computers from the infrastructure of cities from sensor-equipped buildings, trains, buses, planes, bridges, and factories.
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