Statistics: Biostatistics (BS)
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Variable Credit Min
Variable Credit Max
Major Academic Plan
Title
Learning Outcome
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Learning Outcome
Title
Learning Outcome
Title
Learning Outcome
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Learning Outcome
Program Requirements
Requirement 1 — Complete 2 Courses
course - Intro to Stat Data Analysis 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 - Bayesian 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 - Bayesian 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
