PITTSBURGH, July 29, 2013 /PRNewswire-USNewswire/ -- Recent articles in the Harvard Business Review and New York Times have suggested that the onslaught of "Big Data" has created a new discipline they call data science and a need for specialized data scientists. They even claim that no universities are currently addressing this need with academic programs.
Carnegie Mellon University's Joel B. Greenhouse dismisses that data science is a new field by pointing out that statisticians have been conducting collaborative research, designing studies, analyzing data and developing new statistical theory for massive amounts of data for decades. For example, CMU's Department of Statistics, has been making contributions in Big Data applications, such as genomics, astronomy and finance.
Greenhouse, professor of statistics and director of the master's in statistical practice program, elaborates on how statistical thinking is the bedrock of data science in a new blog post for the American Statistical Association (ASA) in the Huffington Post.
Greenhouse writes, "Although the problems of generating and managing massive amounts of data are relatively new, methods for analyzing and making sense of such data, what some would now like to call 'data science,' are quite old and the domain of statistical science."He continues, "Good statistical thinking requires a nontrivial understanding of the real-world problem and the population for whom the research question is relevant. It involves judgments such as those about the relevance and representativeness of the data, about whether the underlying model assumptions are valid for the data at hand and about causality and the role of confounding variables as possible alternative explanations for observed results. Finally, an essential component of good statistical thinking is the ability to interpret and communicate the results of a statistical analysis so nonstatisticians can understand the findings."