MS Concentration in Analytics

The Master of Science Concentration in Analytics combines the mathematical and statistical training of the traditional MS in Statistics with enhanced computational and data analytic training for those planning careers in information intensive industries or research. The program includes fundamental training in mathematical and applied statistics as well as specialized training in data management, analysis, and model building with large datasets and databases. The specialized courses have an emphasis on statistical computing, data management, and statistical learning, which encompasses the more statistical topics that fall under the broader title of data mining. Students are encouraged to gain experience in a business or consulting environment as part of the program.


The prerequisites for the program include calculus through multivariable calculus, linear algebra equivalent to MATH 257, and an introduction to mathematical statistics and probability equivalent to STAT 400. Students in this program should also have prior exposure to computing using business software, statistical software such as SAS or SPSS, and an interactive programming environment such as C,  R or Matlab.

Course Requirements

The concentration requires completing 10 courses, organized around five broad areas of expertise. The first course in probability and statistics, STAT 410, may be waived for students entering with credit for the same or an equivalent course. The 10 required courses by area are described below, and a flat list of courses can be found in the Academic Catalog.

For course descriptions and availability per semester, please see the Academic Catalog.

Mathematical Statistics

  • STAT 410 - Statistics and Probability II - (4 hours)
  • STAT 510 - Mathematical Statistics (4 hours)

STAT 410 is a course in probability and mathematical statistics and prepares students for STAT 510, a practical advanced graduate level course mathematical statistics course. These courses form the foundation for statistical inference that is encountered throughout the remainder of the curriculum. STAT 410 may be waived for students entering with credit for the same or an equivalent course.

Foundational Applied Statistics

  • STAT 425 - Statistical Modeling I (4 hours) or
  • STAT 527 - Advanced Regression Analysis (4 hours)

Select one of the following - 4 hours:

  • STAT 424 - Analysis of Variance
  • STAT 426 - Statistical Modeling II
  • STAT 429 - Time Series Analysis
  • STAT 431 - Applied Bayesian Analysis
  • STAT 433 - Stochastic Processes
  • STAT 528 - Advanced Regression Analysis II
  • STAT 533 - Advanced Stochastic Processes
  • STAT 556 - Advanced Time Series Analysis
    *Note: For students who entered the program prior to Fall 2021, the listed options for this item are STAT 424, STAT 426, STAT 429, STAT 430, and 578.

The choice of STAT 425 or STAT 527 provides thorough coverage of linear regression and data analysis that is fundamental for further study in analytics. STAT 527 is the more advanced course required for PhD students. The second course is a selection of one of several traditional courses in foundational areas of statistics.

Statistical Computing

  • STAT 440 - Statistical Data Management (4 hours)
  • STAT 448 - Advanced Data Analysis (4 hours)

Select one of the following - 4 hours:

  • STAT 428 - Statistical Computing (4 hours)
  • STAT 432 - Basics of Statistical Learning (4 hours)
  • STAT 437 - Unsupervised Learning (4 hours)
  • STAT 447 - Data Science Programming Methods (4 hours)
  • STAT 480 - Big Data Analytics (4 hours)
  • CS 412 - Introduction to Data Mining (4 hours)*
    *This course is not controlled by the Statistics Department, please see CS on how to register for this course. 

STAT 440 focuses on data management and sampling skills vital for success in information intensive careers. STAT 448 is a survey of common advanced statistical methods with an emphasis on application in a computational setting. The third course is a choice of computational courses focused on foundations of statistical computation, data science, or data mining.

Advanced Analytics

  • STAT 542 - Statistical Learning (4 hours)

Select one of the following - 4 hours: 

  • STAT 525 - Computational Statistics (4 hours)
  • STAT 546 - Machine Learning in Data Science (4 hours)
  • STAT 571 - Multivariate Analysis (4 hours)
  • CS 512 - Data Mining Principles (4 hours)*
    *This course is not controlled by the Statistics Department, please see CS on how to register for this course

STAT 542 is an advanced course in statistical learning that covers stat-of-the-art and proven methods for classification, clustering, model selection, and predictive modeling in the context of large data sets. The second advanced analytics course is a choice of advanced statistical computing theory, multivariate analysis, data mining, and machine learning courses.

Experiential Learning

Select one of the following - 4 hours: 

  • STAT 427 – Statistical Consulting (4 hours)
  • STAT 443 – Professional Statistics (4 hours)
  • STAT 593 – STAT Internship (0 or 4 hours)

Team problem solving and communication of statistical results is critical for career success. The experiential learning requirement focuses on these skills and is a choice of consulting, professional preparation or approved internship.


Other Requirements: 

2.75 Minimum GPA