Fundamental Data Science

Over the last three decades, a new interdisciplinary field centered around the ability to access and analyze data has emerged. The field employs advanced mathematics, statistics, and computer science, in concert with the remarkable growth in the capacity of computers, to achieve insights, to make predictions, and to make decisions in ways that would have been science fiction in 1985. Although many of the most important tools have arisen in academia or in research groups of major corporations, the new field is driven by an enormous number of application settings each with its own distinctive features. Taken together this synthesis of tools and domain-applications form the field of data science, and the topic of DataX.

This pillar of DataX is comprised of the fundamental data sciences: statistics, applied mathematics, machine learning, computer science and engineering, as well as ideas from information sciences and other fields. Fundamental data science is the interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data. It addresses the question: “How do we learn from data?”. As such, fundamental data science has direct involvement in almost all disciplines in the modern university. This pillar will work in concert with the other pillars toward the goals of DataX.