Statistics: Data Science (BS)

Statistics Bachelors BS

Variable Credit Min

53.5

Variable Credit Max

53.5

Major Academic Plan

Learning Outcomes

Title

Skill in fitting statistical models

Learning Outcome

Graduates of the program will demonstrate the ability to analyze data by appropriately fitting, assessing, and interpreting a variety of statistical models.

Title

Communication Skills

Learning Outcome

Graduates of the program will write technical reports and make technical presentations containing statistical results, and work in teams to demonstrate the consulting skills of a professional statistician

Title

Computing Skills

Learning Outcome

Graduates of the program will be able to manipulate data, implement statistical methods, document, debug code, understand basic programming constructs, use fundamental data types of computing in one or more statistics or data science software programs

Title

Theoretical Foundations

Learning Outcome

Graduates of the program will be able to solve problems in basic probability theory, statistical inference, and calculus.

Title

Professional Preparation

Learning Outcome

Be employable in jobs with BS Statistics requirement and/or many jobs with BS Data Science requirement

Program Requirements

Requirement 1 —Complete 2 Courses

course - Principles of Statistics 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 - Intro to 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 - Intro to 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