Academic Catalog

Statistics (M.S. in Data Science and Business Analytics)

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:

Module I: Core Courses
The following 3 courses (9 credits) are required.
Data Science Strategy & Leadership
Computing Platforms for Data Science
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.
Introduction to Probability Theory
Introduction to Mathematical Statistics
Introduction to Data Science
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.
Advanced Statistics Theory I
Advanced Statistics Theory II
Statistical Learning for Data Science and Analytics
Operations Research
Mathematical Models in Operations Research
Modern Databases
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
Applied Time Series
Design of Experiments
Applied Regression Analysis
Advanced Statistics Theory I
Advanced Statistics Theory II
Topics in Statistics
Probability Courses
Introduction to Stochastic Processes
Mathematics of Finance
Special Topics in Probability
Mathematics Courses
Elementary Analysis
Numerical Methods I
Numerical Methods II
Applied Linear Algebra
Introduction to Analysis I
Introduction to Analysis II
Mathematical Models in Operations Research
Methods of Optimization
Special Topics in Mathematics
Advanced Linear Algebra
Internship in Mathematical Sciences
Computer Science Courses
Intelligent Systems: Algorithms and Tools
Parallel Computing I: Programming
Database Management Systems I
Artificial Intelligence I
Parallel Computing II: Algorithms and Applications
Bioinformatics I: Biological Databases and Data Analysis
and Bioinformatics I: Programming Lab
Bioinformatics II
Database Management Systems II
Artificial Intelligence II
Data Mining: Algorithms and Applications
Machine Learning
Industrial Engineering Courses
Advanced Statistical Methods
Stochastic Processes
Intelligent Analytics
Economics Courses
Econometrics II
Econometrics III
Data Science Courses
Operations Research
Decision Analysis and Simulation
Modern Databases
Technology and Information Systems Analysis Courses
Advanced Business Analytics
Module IV: Practicum
Select 6 credits from the following:
Applied Time Series
Design of Experiments
Applied Regression Analysis
Data Science and Analytics Practicum
Master's Essay Direction
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. 

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