Data science is an interdisciplinary field containing processes and systems to extract knowledge and insights from data in various forms, either structured or unstructured. The field is showing itself transformative for a broad range of organizations in the way it delivers real business value based on enterprise data assets. 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 retail to genomics, data science is helping to create new branches of knowledge discovery and predictive analytics. The trend is expected to accelerate in the coming years as the volume of data grows from sensors, sophisticated instruments, the web, and more.
Although use of the term “data science” has exploded in business environments, many academics and journalists see no distinction between data science and statistics. Writing in Forbes, analyst Gil Press argues that data science is a buzzword without a clear definition and has simply replaced “business analytics” in various contexts. In the question-and-answer section of his keynote address at the Joint Statistical Meetings of American Statistical Association, noted applied statistician Nate Silver said, “I think data-scientist is a sexed up term for a statistician….Statistics is a branch of science. Data scientist is slightly redundant in some way and people shouldn’t berate the term statistician.”