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Office: The Graduate Center - Columbia Campus
Telephone: 410-617-2171
Website: http://www.loyola.edu/academics/data-science

Director, Program Operations: Hizbullah Baryal

Core Faculty

Professors: Christopher H. Morrell (emeritus); Gloria Phillips-Wren; Paul Tallon
Associate Professors: Megan Olsen
Assistant Professors: Bu Hyoung Lee; Dobin Yim
Instructor: Kevin Drummey; Erhan Guyen, Robert Kenyon

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 may be admitted to the program in the summer, 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 Business Analytics. The Technical specialization is designed for students with some mathematics and statistics background and computer programming experience who have the requisite background to develop machine learning algorithms and utilize advanced statistical insight. The introductory course in programming may be waived 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 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 Business Analytics specialization is designed for students who have introductory statistics, and who are interested in business applications of data science such as marketing or management. The specialization requires two courses in computer science, two courses in data science, and two courses in statistics followed by electives in business, computer science, 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 director of program operations works 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 who graduate with an MS in Data Science will be able to:

• Perform an appropriate analysis of data in the context of a business/organizational problem.
• Apply programming languages to solve data science problems. 
• Apply machine learning to solve data science problems.
• Effectively communicate key elements of the data science process.
• Recognize professional responsibilities and make informed and ethical judgments in practicing data science.

Programs

• Data Science, Business Analytics Specialization, MS
• Data Science, Technical Specialization, MS

Courses

• CS 701 - Introduction to Programming
• CS 703 - Programming for Data Science
• CS 737 - Machine Learning
• CS 745 - Multimedia Data Analysis and Mining
• CS 746 - Data Visualization
• CS 753 - Big Data
• CS 765 - Database Retrieval
• CS 766 - Information Retrieval and Natural Language Processing

• DS 510 - Applied Business Statistics
• DS 730 - Introduction to Data Science
• DS 735 - Project Management
• DS 736 - Data Visualization for Decision Making
• DS 739 - Data Management and Database Systems
• DS 759 - Special Topics in Data Science and Analytics
• DS 795 - Data Science Project Design
• DS 796 - Data Science Project
• DS 851 - Business Intelligence and Data Mining

• ST 510 - Introduction to Statistics
• ST 565 - Biostatistics
• ST 681 - Probability and Statistics
• ST 710 - Statistical Computing
• ST 765 - Linear Statistical Models
• ST 767 - Multivariate Analysis
• ST 775 - Generalized Linear Models and Multilevel Models
• ST 776 - Bayesian Inference
• ST 778 - Time Series Analysis
• ST 791 - Special Topics in Statistics
• ST 792 - Independent Study

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