Loyola University Maryland

Data Science

Master of Science in Data Science Curriculum and Course Planning

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 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 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.

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.

For students beginning in Fall 2021 and thereafter, all courses will be offered online. Depending on the instructor, the course may be offered either synchronously or asynchronously.

Degree Requirements

The degree consists of 31 (or 34) graduate credit hours as follows:

Required Courses

  • CS701 - Introduction to Programming (may be waived)
  • CS703 - Programming for Data Science (taught by CS faculty) (placement or CS701)
  • DS730 - Introduction to Data Science (taught by IS faculty)
  • DS851 - Business Intelligence and Data Mining (taught by IS faculty) (DS730 or written permission of the instructor)
  • ST710 - Statistical Computing (taught by ST faculty)
  • ST765 - Linear Statistical Models (taught by ST faculty) (ST710)

Additional Required Courses

  • CS737 - Machine Learning (for technical specialization) (CS703)
  • DS795 - Data Science Project Design (CS 703, DS 851, ST 710)
  • DS796 - Data Science Project (DS795)

Computer Science Electives

  • CS745 - Multimedia Data Analysis and Mining (CS737, ST710)
  • CS746 - Data Visualization (CS703, ST710)
  • CS753 - Big Data (CS703)
  • CS765 - Database Retrieval (CS703)
  • CS766 - Information Retrieval and Natural Language Processing (CS737)

Statistics Electives

  • ST767 - Multivariate Analysis (ST710)
  • ST775 - Generalized Linear Models and Multilevel Models (ST765)
  • ST776 - Bayesian Inference (ST765)
  • ST778 - Time Series Analysis (ST710)
  • ST791 - Special Topics in Statistics
  • ST792 - Independent Study

Business Electives

  • GB700 - Business Ethics and Corporate Social Responsibility
  • GB735 - Project Management (GB704 or GB705)
  • GB/DS736 - Data Visualization for Decision Making (DS730 or written permission of the instructor)
  • GB/DS739 - Data Management and Database Systems

For course descriptions, please see the Graduate Academic Catalog.

* Course numbers in (parentheses) are prerequisite coursework

Program of Study

  Required Courses Technical Specialization & Required Courses Electives
Fall CS701 - Introduction to Programming
CS703 - Programming for Data Science (CS701)
DS730 - Introduction to Data Science
ST765 - Linear Statistical Models
DS795 - Data Science Project Design (CS703, DS851, ST710)
Computer Science Elective
Business Elective
 
 Spring CS701 - Introduction to Programming
ST710 - Statistical Computing
CS737 - Machine Learning (CS703)
DS796 - Data Science Project (DS795)
Statistics Elective (ST710 or ST765)
Business Elective
 
 Summer DS851 - Business Intelligence and Data Mining (DS730 or written permission of the instructor)   Statistics Elective 

* Course numbers in (parentheses) are prerequisite coursework


Osei Amoh
Students

Osei

Osei aspires to apply what he learns in Loyola’s graduate program in data science to grow his startup

Data Science