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 32 hours 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++.
Admission requirements
- Undergraduate program that includes calculus and linear algebra
- A course in probability
- A course in statistical methods
- Introduction to R software
- Strong background in an object oriented programming language such as Java or C++
Application
General application information for the University, as well as specific requirements for individual programs, are incorporated into the ApplyNow graduate application system.
Apply now
More information is provided in the applications FAQ page.
Required and Elective Courses
Semester 1 (Fall)
- STAT 6620 - Applied Linear Models (3 hours)
- STAT 5870 - Big Data Analysis Using Python (3 hours)
- CS 6100 - Advanced Storage, Retrieval and Processing of Big Data (3 hours)
Semester 2 (Spring)
- STAT 5860 - Computer Based Data Analysis (3 hours)
- CS 5610 - Advanced R Programming for Data Science (4 hours)
- CS 5821 - Machine Learning (3 hours)
Semester 3 (Fall)
- STAT 6800 - SAS Programming (3 hours)
- CS 5430 - Database Systems (3 hours)
- And MS Project 1
STAT 6970 - Data Science Masters Project (4 hours)
or
CS 6970 - Master's Project (2 to 6 hours)
Semester 4 (Spring)
- STAT/CS Elective chosen from List 1 or List 2 below (3 hours)
- And MS Project 2
STAT 6970 - Data Science Masters Project (4 hours)
or
CS 6970 - Master's Project (2 to 6 hours)
List 1 - STAT Electives
- STAT 6040 - Fundamentals of Epidemiology and Clinical Trials (3 hours)
- STAT 6500 - Statistical Theory I (4 hours)
- STAT 6600 - Statistical Theory II (4 hours)
- STAT 6640 - Applied Mixed Models (3 hours)
List 2 - CS Electives
- CS 6030 - Studies in Computer Science Credits: 3 hours
- CS 6260 - Advanced Parallel Computations Credits: 3 hours
- CS 6310 - Advanced Design and Analysis of Algorithms Credits: 3 hours
- CS 6430 - Database Management System Implementation Credits: 3 hours
- CS 6530 - Data Mining Credits: 3 hours
- CS 6820 - Advanced Artificial Intelligence Credits: 3 hours
- CS 6821 - Information Retrieval Credits: 3 hours