MS and Certificate Program Requirements
Semester Course Offerings
Fall 2023
STA 503 - Introduction to R (Crossett)
STA 504 - Mathematical Statistics I with Calculus Review (Peng)
STA 505 - Mathematical Statistics I (Gallop)
STA 507 - Categorical Data Analysis (Rieger)
STA 511 - Introduction to Statistical Computing (Rieger)
STA 513 - Intermediate Linear Models (Gallop)
STA 546 - Foundations of Bioinformatics (Crossett)
Summer II 2023
STA 551 - Foundations of Data Science (Peng)
Summer I 2023
STA 532 - Survival Analysis (Rieger)
Spring 2023
STA 506 - Mathematical Statistics II (Crossett)
STA 512 - Principles of Experimental Analysis (McClintock)
STA 514 - Modern Experimental Design and Sampling Methods (Rieger)
STA 534 - Time Series (McClintock)
STA 541 - Categorical Data Analysis II (Gallop)
Winter 2022/23
STA538 - Statistical Programming Using R (Crossett)
Program Prerequisites
Prerequisite knowledge for the MS program will be a course in Multivariate Calculus. We have created a pathway that makes it convenient for students to fulfill all prerequisites. If a student has taken Calculus I or equivalent, they can take the new MAT243 over the summer, as an online asynchronous class. This class will cover all of the Calculus prerequisite topics, as well as an introduction to the topics needed from Linear Algebra. It will also be expected that a student will have completed an introduction to Statistics at the undergraduate level.
No background in calculus or other advanced math topics is required for the Applied Statistics Certificate. The only prerequisite for the Certificate Program is MAT121 or an equivalent introduction to statistics.
Extracurricular Events
For extracurricular events in the Applied Statistics department, and for new student orientation materials for Applied Statistics graduate students, please visit the Applied Statistics Events page.
Curriculum Information
Example Course Syllabi
STA 504 Mathematical Statistics I (with calculus review)
STA 505 Mathematical Statistics I
STA 506 Mathematical Statistics II
STA 507 Intro. Categorical Data Analysis
STA 511 Intro. Statistical Computing
STA 512 Principles of Experimental Analysis
STA 513 Intermediate Linear Models
STA 514 Modern Experimental Design and Sampling Methods
STA 533 Longitudinal Data Analysis
STA 535 Multivariate Data Analysis
STA 537 Advanced SAS Programming
STA 538 Statistical Programming Using R
STA 539 Applied Bayesian Methods
STA 540 Statistical Consulting
STA 541 Categorical Data Analysis II
STA 543 Statistical Methods in Business and Finance
STA 546 Foundations of Bioinformatics
STA 551 Foundations of Data Science