The Master of Science in Data Science and Business Analytics program is designed to give graduates a core of computing, business, statistics, and operations research skills to identify, analyze, and solve analytics problems; to integrate those skills in an interdisciplinary way that other, single-discipline-oriented analytics degree might not; and to provide in-depth training in an analytics area of specialization. The Statistics major is designed to meet demand in industry for talent with solid statistical foundations.

Admission Requirements

Applicants must meet requirements for admission to the Graduate School. Additional admission requirements include:

• GPA of 3.0 or better
• English language proficiency passing test scores (for international applicants)
• Multivariate calculus course (equivalent to MAT 2030)
• One linear algebra course (equivalent to MAT 2250)
• One basic statistics course (equivalent to STA 2210, MAT 6150 or DSE 5070)
• One programming course (equivalent to CSC 1100 or higher or DSE 5070)

Students who have not completed the above courses can be admitted but have to complete corresponding courses before starting the program.

Program Requirements

Completion of the Master of Science in Data Science and Business Analytics with a major in Statistics requires a minimum of 30 credits. Courses cannot be double-counted even if listed in multiple modules. Coursework includes:

Course List

Code	Title	Credits
Module I: Core Courses
The following 3 courses (9 credits) are required.
DSB 6000	Data Science Strategy & Leadership
DSE 6000	Computing Platforms for Data Science
STA 5030	Statistical Computing and Data Analysis
Module II: Major Courses
Students have to finish following courses (11 credits) if they have not completed the courses before admission.
MAT 5700	Introduction to Probability Theory
STA 5800	Introduction to Mathematical Statistics
STA 5820	Introduction to Data Science
or CSC 5825	Introduction to Machine Learning and Applications
If any of the above courses were completed before admission to the program, students must complete three courses (9 credits) in Module II. Students can choose from any of the following courses.
STA 7810	Advanced Statistics Theory I
STA 7820	Advanced Statistics Theory II
DSA 6100	Statistical Learning for Data Science and Analytics
DSA 6200	Operations Research
or MAT 5770	Mathematical Models in Operations Research
DSE 6200	Modern Databases
CSC 7825	Machine Learning
Module III: Elective Courses
Students are required to select 4-6 credits from the following list. The number of credits will be based on coursework completed for Module II.
Statistics Courses
STA 5830	Applied Time Series
STA 6830	Design of Experiments
STA 6840	Applied Regression Analysis
STA 7810	Advanced Statistics Theory I
STA 7820	Advanced Statistics Theory II
STA 7870	Topics in Statistics
Probability Courses
MAT 5710	Introduction to Stochastic Processes
MAT 5750	Mathematics of Finance
MAT 7770	Special Topics in Probability
Mathematics Courses
MAT 5070	Elementary Analysis
MAT 5100	Numerical Methods I
MAT 5110	Numerical Methods II
MAT 5410	Applied Linear Algebra
MAT 5600	Introduction to Analysis I
MAT 5610	Introduction to Analysis II
MAT 5770	Mathematical Models in Operations Research
MAT 5870	Methods of Optimization
MAT 5890	Special Topics in Mathematics
MAT 6420	Advanced Linear Algebra
MAT 6990	Internship in Mathematical Sciences
Computer Science Courses
CSC 5800	Intelligent Systems: Algorithms and Tools
CSC 6220	Parallel Computing I: Programming
CSC 6710	Database Management Systems I
CSC 6800	Artificial Intelligence I
CSC 7220	Parallel Computing II: Algorithms and Applications
CSC 7300 & CSC 7301	Bioinformatics I: Biological Databases and Data Analysis and Bioinformatics I: Programming Lab
CSC 7410	Bioinformatics II
CSC 7710	Database Management Systems II
CSC 7800	Artificial Intelligence II
CSC 7810/IE 7811	Data Mining: Algorithms and Applications
CSC 7825	Machine Learning
Industrial Engineering Courses
IE 7220	Advanced Statistical Methods
IE 7710	Stochastic Processes
IE 7860	Intelligent Analytics
Economics Courses
ECO 7110	Econometrics II
ECO 7120	Econometrics III
Data Science Courses
DSA 6200	Operations Research
DSA 6300	Decision Analysis and Simulation
DSE 6200	Modern Databases
Technology and Information Systems Analysis Courses
TIS 7570	Advanced Business Analytics
Module IV: Practicum
Select 6 credits from the following:
STA 5830	Applied Time Series
STA 6830	Design of Experiments
STA 6840	Applied Regression Analysis
STA 7800	Data Science & Analytics Practicum
MAT 7999	Master's Essay Direction
IE 7860	Intelligent Analytics

Academic Scholarship: All coursework must be completed in accordance with the regulations of the Graduate School and the College of Liberal Arts and Sciences. Students may enroll on a full-time or part-time basis but must complete requirements within six years of admission.