2017-2018 Graduate Academic Catalogue 
    Oct 17, 2021  
2017-2018 Graduate Academic Catalogue [ARCHIVED CATALOG]

Data Science, M.S.

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.

Prerequisites for the Program

Students are expected to have taken university-level calculus and at least one other mathematics course, and to have had an introduction to computer science/programming course. Students are expected to have had an introductory statistics course. This requirement can be satisfied by taking the preparatory course, GB 715 . The programming prerequisite can be satisfied by taking an online programming course such as Code Academy's Python course and then passing a proficiency exam.

Degree Requirements

The degree consists of 31 graduate credit hours, as follows:

Preparatory Course

The preparatory must be taken, unless waived based on previous college experience. This course does not count toward the 31 required credit hours.


Choose one elective from computer science, one elective from statistics, and a third elective from computer science, statistics, or one of the approved graduate business courses (GB) offered by the Sellinger School of Business and Management:

Program of Study

The program is designed around a set of four core courses consisting of CS 703 ,  DS 730 DS 851  and ST 710 . Depth in computing and statistics is achieved in CS 737  and ST 765 . The program concludes with a year-long data science project where students practice the skills they have acquired through their course work in a real-world project, working with a client who has a data need.