Overview: Carnegie Mellon's Interdisciplinary Approach to Data Science
The extraordinary spread of computers and online data is changing forever the way decisions are made in many fields, from medicine to marketing to scientific research. Dramatic growth in the scale and complexity of data that can be collected and analyzed is affecting all aspects of work and society
including health care, business practices, public safety, scientific discoveries and public policy.
Understanding effective and ethical ways of using vast amounts of data is a significant challenge to science and to society as a whole, and developing scalable techniques for data analysis and decision making requires interdisciplinary research in many areas, including machine learning, algorithms, statistics, operations research, databases, complexity analysis, visualization, and privacy and security.
Carnegie Mellon University's programs in Data Science are designed to train students to become tomorrow's leaders in this rapidly growing area. Through a unique combination of interdisciplinary coursework and cutting-edge research, the programs will enable them to apply techniques and tools of data science to applications drawing on appropriate and relevant concepts and models from the engineering, natural or social sciences. Graduates will be uniquely positioned to pioneer new developments in this field, and to be leaders in industry, the public sector, and academia.
Carnegie Mellon’s educational and research activities in data science span a wide number of disciplines and departments. One reflection of this breadth is the number of different master’s-level data science programs, which vary as to the incoming students’ background, the focus of study, the intended outcomes, and detailed logistics. This document provides a brief summary of the different programs, along with links to their individual home pages, and is intended to help prospective students understand CMU’s varied offerings in data science.