Statistics: Statistical Science (BS)

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Statistics Bachelors BS

Variable Credit Min

53.5

Variable Credit Max

53.5

Major Academic Plan

Learning Outcomes

Title

Skills 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 findings 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 use appropriate statistical methods document and debug code in one or more profession statistical 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

Academic Preparation

Learning Outcome

Be prepared to enter top graduate (masters and/or PhD) programs in statistics

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 6 hours

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 once 1.0v

course - Experimental Design 3.0

course - Survival Analysis 3.0

Requirement 7 —Complete 12 hours

Note: Courses used in Requirements 4 and 6 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 - Ordinary Differential Equation 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 Analytics 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 1.0v

course - Academic Internship - You may take once 0.5v

course - Intro to Research - You may take once 0.5v

course - Experimental Design 3.0

course - Survival Analysis 3.0

It is strongly recommended that students interested in graduate study in statistics choose electives to prepare for the BYU BS/MS statistics integrated program by meeting with the Statistics Department graduate coordinator.