Master of Science in Data Science (DSCM)

Advisors:
Dr. Kevin Lee (Statistics)
Dr. Alvis Fong (Computer Science)

This is an interdisciplinary Master of Science program offered jointly by the Department of Statistics (COAS) and the Department of Computer Science (CEAS), housed in the Department of Statistics. Data Science is today one of the most rapidly developing disciplines and data scientists are in high demand in the job market. Students in this program would develop a skill set that will allow them to take on current and future complex data challenges. Graduates will be able to store and access data from a variety of sources (also heterogeneous), process Big Data architecture, apply analytic techniques and algorithms (including statistical and data mining) to large, complex data sets, apply relevant environments for data processing and visualization, work in collaborative teams and communicate effectively.

A minimum of 35 credit is required and the resulting degree is a Master of Science in Data Science.

For admission to the program, candidates must have completed an undergraduate program including linear algebra, calculus, a course in statistical methods, a course in probability, introduction to R software and a strong background in an object oriented programming language such as Java or C++.

Program Requirements

The program requires at least 35 credit hours of courses:

Core Courses (25 credit hours)

STAT 5680 - Regression Analysis Credits: 3 hours
OR
STAT 6620 - Applied Linear Models Credits: 3 hours
STAT 5860 - Computer Based Data Analysis Credits: 3 hours
STAT 5870 - Big Data Analysis Using Python Credits: 3 hours
STAT 6800 - SAS Programming Credits: 3 hours
CS 5430 - Database Systems Credits: 3 hours
CS 5610 - Advanced R Programming for Data Science Credits: 4 hours
CS 5821 - Machine Learning Credits: 3 hours
CS 6100 - Advanced Storage, Retrieval and Processing of Big Data Credits: 3 hours

Elective Courses (3 credit hours from STAT and 3 credit hours from CS)

List 1 - STAT Electives

STAT 5610 - Applied Multivariate Statistical Methods Credits: 3 hours
STAT 5630 - Sample Survey Methods Credits: 3 hours
STAT 5660 - Nonparametric Statistical Methods Credits: 3 hours
STAT 5820 - Time Series Analysis Credits: 3 hours
STAT 5850 - Applied Data Mining Credits: 3 hours
STAT 6500 - Statistical Theory I Credits: 4 hours
STAT 6600 - Statistical Theory II Credits: 4 hours
STAT 6640 - Applied Mixed Models Credits: 3 hours
STAT 6650 - Advanced Statistical Inference Credits: 3 hours

List 2 - CS Electives

CS 5260 - Parallel Computations Credits: 3 hours
CS 5300 - Artificial Neural Systems Credits: 3 hours
CS 5310 - Algorithms Credits: 3 hours
CS 5560 - Network Programming Credits: 3 hours
CS 6260 - Advanced Parallel Computations Credits: 3 hours
CS 6430 - Database Management System Implementation Credits: 3 hours
CS 6530 - Data Mining Credits: 3 hours

Master Project Course (4 credit hours from STAT or 2+2 credit hours from CS)

STAT 6970 - Data Science Masters Project Credits: 4 hours
CS 6970 - Master’s Project Credits: 2 to 6 hours
(2 hours needed for two semesters)