The first rule of data science - The Berkeley Science Review

“'The first rule of data science is: don’t ask how to define data science.” So says Josh Bloom, a UC Berkeley professor of astronomy and a lead principal investigator (PI) at the Berkeley Institute for Data Science (BIDS). If this approach seems problematic, that’s because it is—data science is more of an emerging interdisciplinary philosophy, a wide-ranging modus operandi that entails a cultural shift in the academic community. The term means something different to every data scientist, and in a time when all researchers create, contribute to, and share information that describes how we live and interact with our surroundings in unprecedented detail, all researchers are data scientists ... In this rapidly changing world, universities are faced with the challenge of adapting to increasingly data-driven research agendas. At UC Berkeley and elsewhere, scientists and administration are working together to reshape how we do research and ultimately restructure the culture of academia ... More than ever, researchers in all disciplines find themselves wading through more and more kinds of data. Frequently, there is no standard system for storing, organizing, or analyzing this data. Data often never leaves the lab; the students graduate, the computers are upgraded, and records are simply lost. This makes research in the social, physical, and life sciences difficult to reproduce and develop further. To make matters worse, it’s no easy task to build tools for general scientific computing and data analysis. Doing so requires a set of skills researchers must largely learn independently, and a timeframe that extends beyond the length of the average PhD.  Historically, no single practice described the simultaneous use of so many different skill sets and bases of knowledge. However, in recent years data science has emerged as the field that exists at the intersection of math and statistics knowledge, expertise in a science discipline, and so-called 'hacking skills,' or computer programming ability. While these skills are changing the way that science is practiced, they’re also changing other aspects of society, such as business and technology startups. In a world where rapidly advancing technology is forcibly changing data science practices, universities are struggling to keep up, often losing good researchers to industries that place a high value on their computational skills ... Despite its increasing importance and relevance, it’s almost impossible to pin down what data science actually is ... What do researchers think?  ..."