Data Science is a complex and evolving field, but most agree that it can be defined as the intersection of computer science and technology, math and statistics, and domain knowledge, with the purpose of extracting knowledge and value from data.
We propose a new set of areas that builds on this definition but more accurately represents the field as it has grown and as it has come to be practiced. These are the areas of value, design, systems, and analytics. A fifth area, practice, connects the other four. Together, these areas define specific kinds of expertise that belong to every data science project but which are often unconnected and siloed in the academy. Unlike traditional disciplines, each new area is inherently interdisciplinary, bringing together diverse and sometimes contrary perspectives under a common theme. The inherently interdisciplinary and pluralist nature of these areas is a distinctive feature of data science and a key differentiator between it and traditional disciplines.
