Office: Donnelly Science Center, Room 127a
Director: Christopher H. Morrell, Professor of Mathematics and Statistics (Statistics)
Professors: David W. Binkley; Dawn J. Lawrie; Christopher H. Morrell; Gloria Phillips-Wren; Paul Tallon
Associate Professors: Sibren N. Isaacman; Megan M. Olsen; M S Raunak
Assistant Professors: Bu Hyoung Lee
Instructor: Christopher G. Wagner
The master of science in data science offers students a thorough data science educational experience through a 31-34 credit program, 10 or 11 three-credit courses and 1 one-credit course. Students are admitted to the program in either the fall or spring semester. This degree blends computer science and statistics courses to render students adaptable to any domain with rigorous statistical and computational skills. In addition to courses in business, computer science, and statistics, students complete a one-credit design course for the research project; and a capstone research project conducted with a partner in local industry/government/non-profit.
Students choose one of two specializations: Technical or Analytics. The Technical specialization is designed for students with computer programming experience who have the requisite background to develop machine learning algorithms and utilize advanced statistical insight. The introductory course in programming is waivable if the student has completed an Introduction to Programming or Introduction to Computer Science course that teaches problem solving (Python preferred, but not required).
The Analytics specialization is designed for students who have introductory statistics and computing skills, and who are interested in business applications of data science such as marketing or management.
The Technical specialization requires three courses in computer science, two courses in data science, and two courses in statistics followed by electives in computer science, statistics, and/or business; and a capstone research project conducted with a partner in local industry/government/non-profit.
The Analytics specialization requires two courses in computer science, two courses in data science, and one course in statistics followed by electives in computer science and business or statistics; and a capstone research project conducted with a partner in local industry/government/non-profit.
A strength of the program is the required two-semester capstone practicum, which could include a summer internship. The practicum is an independent or group project that uses the data science techniques acquired during the program in an applied manner to solve a practical problem with a local partner. In the first semester, students design the project and present their plan to the program's board; this could be part of a paid internship. The program director will work to develop opportunities by developing a strong advisory board, comprised of industry, government, and not-for-profit representatives. In the second semester, students implement their project and present the results of the project to the board for approval.
- Students will understand the underlying principles of data science and be able to keep up with this expanding field.
- Students will be proficient in analyzing complex data from diverse sources by discovering key relationships within the data.
- Students will be able to model data using machine learning techniques.
- Students will be able to model data using statistical models.
- Students will be able to predict future outcomes that can be used to advise decision makers on their course of action.
- Students will be knowledgeable of general ethical principles, how these principles apply to data science, and the social context of data science.
CoursesComputer ScienceData ScienceStatistics