Requirement 1 — Complete 2 Courses 

course - Principles of Statistics 3.0

course - Intro to Statistics Department 0.5

Requirement 2 — Complete 5 Courses 

Statistics core courses:

course - Analysis of Variance 3.0

course - Probability and Inference 1 3.0

course - Applied R Programming 3.0

course - Introduction to Regression 3.0

course - Probability and Inference 2 3.0

Requirement 3 — Complete 4 Courses 

Mathematical foundation courses:

course - Calculus 1 4.0

course - Calculus 2 4.0

course - Elementary Linear Algebra 2.0

course - Computational Linear Algebra 1.0

Requirement 4 — Complete 3 hours

course - How to Program 3.0

course - Intro to Computer Science 3.0

course - Statistical Computing in Epi 3.0

course - Spreadsheet Automation 3.0

course - Data Science Ecosystems 3.0

Requirement 5 — Complete 1 Course 

course - Calculus of Several Variables 3.0

Requirement 6 — Complete 3 hours

course - Applications in Biostatistics 3.0

course - Survival Analysis 3.0

Requirement 7 — Complete 6 hours

Note: If taken above, STAT 437 and 538 will not double count here.

course - Ecology 3.0

course - Science of Biology 3.0

course - Human Physiology 4.0

course - Gen College Chem 1+Lab Integr 4.0

course - Principles of Chemistry 1 4.0

course - Principles of Epidemiology 3.0

course - Molecular Biology 3.0

course - Genetics 3.0

course - Applications in Biostatistics 3.0

course - Survival Analysis 3.0

Requirement 8 — Complete 3 hours

Note: Courses used anywhere above will not double count here.

course - Methods of Survey Sampling 3.0

course - Intro to Bayesian Statistics 3.0

course - Theory of Interest 3.0

course - Data Visualization 3.0

course - Data Science Ecosystems 3.0

course - Predictive Analysis 3.0

course - Statistical Computing 3.0

course - Data Science Process 3.0

course - Nonparametric Stat Methods 3.0

course - Applications in Biostatistics 3.0

course - Applied Bayesian Statistics 3.0

course - Intro to Reliability 3.0

course - Analysis of Correlated Data 3.0

course - Data Science Capstone 1 3.0

course - Data Science Capstone 2 3.0

course - Machine Learning 3.0

course - Special Topics in Statistics - You may take up to 3.0 credit hours  1.0v

course - Experimental Design 3.0

course - Survival Analysis 3.0

Requirement 9 — Complete 6 hours

Note: Courses used anywhere above will not double count here. Note: No more than 3.0 credit hours of any combination of Stat 496R and Stat 497R may be counted toward this requirement. Note: It is strongly recommended that students interested in graduate study in biostatistics complete Math 341 and 342.

course - Principles of Epidemiology 3.0

course - Theory of Analysis 1 3.0

course - Theory of Analysis 2 3.0

course - Methods of Survey Sampling 3.0

course - Intro to Bayesian Statistics 3.0

course - Theory of Interest 3.0

course - Data Visualization 3.0

course - Data Science Ecosystems 3.0

course - Predictive Analysis 3.0

course - Statistical Computing 3.0

course - Data Science Process 3.0

course - Special Topics in Applied Stat - You may take once 1.0v

course - Nonparametric Stat Methods 3.0

course - Applications in Biostatistics 3.0

course - Applied Bayesian Statistics 3.0

course - Intro to Reliability 3.0

course - Analysis of Correlated Data 3.0

course - Data Science Capstone 1 3.0

course - Data Science Capstone 2 3.0

course - Machine Learning 3.0

course - Special Topics in Statistics - You may take up to 3.0 credit hours

course - Academic Internship - You may take up to 3.0 credit hours

course - Intro to Research - You may take up to 3.0 credit hours

course - Experimental Design 3.0

course - Survival Analysis 3.0