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 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.
The degree consists of 34 graduate credit hours (three credits may be waived) as follows:
- CS701 - Introduction to Programming (may be waived)
- CS703 - Programming for Data Science
- CS737 - Machine Learning (only Technical Specialization) (CS703)
- DS730 - Introduction to Data Science
- DS795 - Data Science Project Design (CS703, DS851, ST710)
- DS796 - Data Science Project (DS795)
- DS851 - Business Intelligence and Data Mining (DS730 or written permission of the department chair)
- ST710 - Statistical Computing
- ST765 - Linear Statistical Models (ST710)
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)
- 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
- GB712 - Law, Ethics, and Social Responsibility
- GB735 - Project Management (GB704 or GB705)
- DS736 - Data Visualization for Decision Making (DS730 or written permission of the instructor)
- DS739 - Data Management and Database Systems
- GB747 - Special Topics in Marketing: Digital Marketing and Analytics (only Business Analytics Specialization)
- GB759 - Special Topics in Management Information Systems: Location Analytics (only Business Analytics Specialization) (DS730 or CS703)
For course descriptions, please see the Graduate Academic Catalog.
Program of Study
The program of study is designed around the required courses and electives. 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.
||CS701 - Introduction to Programming
CS703 - Programming for Data Science
DS730 - Introduction to Data Science
ST765 - Linear Statistical Models
DS795 - Data Science Project Design
|Computer Science Elective
CS737 - Machine Learning (only Technical Specialization)
ST710 - Statistical Computing
DS796 - Data Science Project
|Computer Science Elective or
Statistics Elective or
||DS851 - Business Intelligence and Data Mining