Statistics: Applied Statistics & Analytics (BS)
Variable Credit Min
Variable Credit Max
Major Academic Plan
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Learning Outcome
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Learning Outcome
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Learning Outcome
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Learning Outcome
Program Requirements
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