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 6 hours

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 once 1.0v

course - Experimental Design 3.0

course - Survival Analysis 3.0

Requirement 6 —Complete 15 hours

Note: Courses used in Requirements 4 and 5 will not double count here. Note: No more than 3.0 hours of any combination of STAT 496R and STAT 497R can be used for this requirement.

course - Spreadsheets for Bus Analysis 3.0

course - Spreadsheet Automation 3.0

course - Calculus of Several Variables 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 - Long-term Actuarial Math 3.0

course - Short-term Actuarial Math 3.0

course - Predictive Analytics 3.0

course - Statistical Computing 3.0

course - Data Science Process 3.0

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

course - Academic Internship - You may take up to 3.0 credit hours 0.5v

course - Intro to Research - You may take up to 3.0 credit hours 0.5v

course - Experimental Design 3.0

course - Survival Analysis 3.0