Statistics (BS): Data Science Emphasis
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Variable Credit Min
Variable Credit Max
Major Academic Plan
Title
Learning Outcome
Title
Learning Outcome
Title
Learning Outcome
Title
Learning Outcome
Title
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 6 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 Bayesian Statistics 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 - Intro to Data 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 of 2 Options
Option 5.1 —Complete 2 Courses
course - Data Science Capstone 1 3.0
course - Data Science Capstone 2 3.0
Option 5.2 —Complete 2 Courses
course - Data Science Process 3.0
course - Machine Learning 3.0
Requirement 6 —Complete 2 Courses
course - Intro to Computer Science 3.0
course - Data Structures 3.0
Requirement 7 —Complete 3 hours
Courses taken in any of the requirements above will not double count here.
course - Statistical Computing 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 - Special Topics in Statistics - You may take once 1.0v
course - Experimental Design 3.0
course - Survival Analysis 3.0
Requirement 8 —Complete 6 hours
Courses taken in any of the requirements above will not double count here. 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 - Theory of Analysis 1 3.0
course - Theory of Analysis 2 3.0
course - Methods of Survey Sampling 3.0
course - Theory of Interest 3.0
course - Data Visualization 3.0
course - Data Science Ecosystems 3.0
course - Predictive Analytics 3.0
course - Statistical Computing 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 - Bayesian Reliability 3.0
course - Analysis of Correlated Data 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