## First Session, 2022

### STOR 113 Decision Models for Business and Economics (3)

Prerequisite, MATH 110. An introduction to multivariable quantitative models in economics. Mathematical techniques for formulating and solving optimization and equilibrium problems will be developed, including elementary models under uncertainty.

### STOR 120 Foundations of Statistics and Data Science (4)

The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.

### STOR 155 Introduction to Data Models and Inference (3)

Prerequisite, MATH 110. Data analysis; correlation and regression; sampling and experimental design; basic probability (random variables, expected values, normal and binomial distributions); hypothesis testing and confidence intervals for means, proportions, and regression parameters; use of spreadsheet software.

### STOR 320 Introduction to Data Science (4)

Prerequisite, STOR 120 or STOR 155. Development of basic skill set for data analysis from obtaining data to data carpentry, exploration, modeling, and communication. Topics covered include regression, clustering, classification, algorithmic thinking, and non-standard data objects (networks and text data). Students may not receive credit for both STOR 320 and STOR 520.

### STOR 435 Introduction to Probability (MATH 535) (3)

Prerequisite, MATH 233. Introduction to the mathematical theory of probability, covering random variables; moments; binomial, Poisson, normal and related distributions; generating functions; sums and sequences of random variables; and statistical applications. Students may not receive credit for both STOR 435 and STOR 535.

### STOR 455 Methods of Data Analysis (3)

Prerequisite, STOR 120 or STOR 155. Review of basic inference; two-sample comparisons; correlation; introduction to matrices; simple and multiple regression (including significance tests, diagnostics, variable selection); analysis of variance; use of statistical software.

Prerequisite, permission of director of undergraduate studies. This course is intended primarily for students working on honors projects. May be repeated for credit.

## Second Session, 2022

### STOR 155 Introduction to Data Models and Inference (3)

Prerequisite, MATH 110. Data analysis; correlation and regression; sampling and experimental design; basic probability (random variables, expected values, normal and binomial distributions); hypothesis testing and confidence intervals for means, proportions, and regression parameters; use of spreadsheet software.

### STOR 320 Introduction to Data Science (4)

Prerequisite, STOR 120 or STOR 155. Development of basic skill set for data analysis from obtaining data to data carpentry, exploration, modeling, and communication. Topics covered include regression, clustering, classification, algorithmic thinking, and non-standard data objects (networks and text data). Students may not receive credit for both STOR 320 and STOR 520.

### STOR 455 Methods of Data Analysis (3)

Prerequisite, STOR 120 or STOR 155. Review of basic inference; two-sample comparisons; correlation; introduction to matrices; simple and multiple regression (including significance tests, diagnostics, variable selection); analysis of variance; use of statistical software.

### STOR 493 Internship in Statistics and Operations Research (3)

Prerequisite, permission of director of undergraduate studies. Statistics and analytics majors only. An opportunity to obtain credit for an internship related to statistics, operations research, or actuarial science. Pass/Fail only. Does not count toward the statistics and analytics major or minor.