Office: Donnelly Science Center, Room 127a
Telephone: 410-617-2464
Website: http://www.loyola.edu/academics/data-science

Director: Christopher H. Morrell, Professor of Mathematics and Statistics (Statistics)

Core Faculty

Professors: David W. Binkley; Roger D. Eastman; Dawn J. Lawrie; Christopher H. Morrell
Associate Professors: Megan M. Olsen; Mohammad S. Raunak
Assistant Professors: Sibren Isaacman; 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-credit program, 10 three-credit courses and 1 one-credit course. The program includes a data science core, which is at the nexus of business, computer science, and statistics. This degree blends computer science and statistics courses to render students adaptable to any domain with rigorous statistical and computational skills. Following the four-course core, there are seven additional courses: two statistics courses; two computer science courses; one elective from business, statistics, or computer science; a one-credit design course for the research project; and a capstone research project conducted with a partner in local industry/government/non-profit. The required statistics courses develop students' modeling skills, and the computer science course exposes students to machine learning and artificial intelligence.

A strength of the program is the required two-semester 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.

Learning Aims

• 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.