This paper brings together a cohesive picture of data science, a new interdisciplinary academic field, from many different perspectives.

In this 42-page paper, the author first reviews the evolution of data science from the early days of just having a huge amount of data mostly because of the Internet, to data analytics that try to make use of these data, to the big data or data science of today that is becoming a discipline of its own. The author then discusses the pervasiveness of data science; from business, social media, healthcare, and medical applications, to governments and academia, “data quantification” is taking place everywhere. That is, all branches of society are paying attention and making efforts to manage and utilize the huge amount of data we see every day. As a response to this phenomenon, the scientific community and society at large have been active in creating the applications and technologies that take advantage of big data, as well as the academic disciplines that can educate and produce data scientists who can be the leaders, the scientists, and the engineers in the new data economy. The core of data science is what is called data analytics. The author attempts to systematically describe the life cycle of data, from its generation to its use in decision making. In the last segments of the paper, the author discusses the challenges and predicts the future of data science and its practical impact on society, that is, the data economy: data industrialization and services.

This paper covers a wide collection of topics that present readers with resources to pursue further studies. It is written in such a way that readers with different backgrounds may pick up the pieces they need and gain an understanding about data science. For business leaders and government decision makers, this paper will help them appreciate the importance and understand the overall picture of data science, as well as its implications in their respective areas; scholars in various disciplines may find terms and structures of data science proposed in this paper useful, or at least a starting point for discussion. For all readers, the paper provides a rich collection of references that may help them pursue the study further.

Cao could have elaborated with more specifics in some areas. For example, it would be very helpful if the section on data education discussed more on the curriculum, as well as challenges in algorithms, computing infrastructures, or theories in statistics or data mining that would help advance data science.