Undergraduate Courses

STOR 52 – First-Year Seminar: Decisions, Decisions, Decisions
Credits: 3
Description: In this course, we will investigate the structure of these decision problems, show how they can be solved (at least in principle), and solve some simple problems.
Grading Status: Letter Grade
Gen Ed: QI

STOR 53 – FYS: Networks: Degrees of Separation and Other Phenomena Relating to Connected Systems
Credits: 3
Description: Networks, mathematical structures that are composed of nodes and a set of lines joining the nodes, are used to model a wide variety of familiar systems.
Grading Status: Letter Grade
Gen Ed: QI

STOR 54 – First-Year Seminar: Adventures in Statistics
Credits: 3
Description: This seminar aims to show that contrary to common belief, statistics can be exciting and fun. The seminar will consist of three modules: statistics in our lives, randomness, and principles of statistical reasoning.
Grading Status: Letter Grade
Gen Ed: QI

STOR 55 – First-Year Seminar: Risk and Uncertainty in the Real World
Credits: 3
Description: The aim of this class is to study the role of uncertainty in our daily lives, to explore the cognitive biases that impair us, and to understand how one uses quantitative models to make decisions under uncertainty in a wide array of fields including medicine, law, finance, and the sciences.
Grading Status: Letter Grade
Gen Ed: QI

STOR 56 – First-Year Seminar: The Art and Science of Decision Making in War and Peace
Credits: 3
Description: This seminar will use recently assembled historical material to tell the exciting story of the origins and development of operations research during and after World War II.
Grading Status: Letter Grade
Gen Ed: QI

STOR 60 – First-Year Seminar: Statistical Decision-Making Concepts
Credits: 3
Description: We will study some basic statistical decision-making procedures and the errors and losses they lead to. We will analyze the effects of randomness on decision making using computer experimentation and physical experiments with real random mechanisms like dice, cards, and so on.
Grading Status: Letter Grade
Gen Ed: QI

STOR 61 – First-Year Seminar: Statistics for Environmental Change
Credits: 3
Description: Studies the Environmental Protection Agency's Criteria Document, mandated by the Clean Air Act; this document reviews current scientific evidence concerning airborne particulate matter. Students learn some of the statistical methods used to assess the connections between air pollution and mortality, and prepare reports on studies covered in the Criteria Document.
Grading Status: Letter Grade
Gen Ed: QI

STOR 62 – First-Year Seminar: Probability and Paradoxes
Credits: 3
Description: The theory of probability, which can be used to model the uncertainty and chance that exist in the real world, often leads to surprising conclusions and seeming paradoxes. We survey and study these, along with other paradoxes and puzzling situations arising in logic, mathematics, and human behavior.
Grading Status: Letter Grade
Gen Ed: QI

STOR 63 – FYS: Statistics, Biostatistics, and Bioinformatics: An Introduction to the Ongoing Evolution
Credits: 3
Description: This course is designed to emphasize the motivation, philosophy, and cultivation of statistical reasoning in the interdisciplinary areas of statistical science and bioinformatics.
Grading Status: Letter Grade
Gen Ed: QI

STOR 64 – First-Year Seminar: A Random Walk down Wall Street
Credits: 3
Description: Introduces basic concepts in finance and economics, useful tools for collecting and summarizing financial data, and simple probability models for quantification of market uncertainty.
Grading Status: Letter Grade
Gen Ed: QI

STOR 66 – First-Year Seminar: Visualizing Data
Credits: 3
Description: This seminar looks at a variety of ways in which modern computational tools allow easy and informative viewing of data. Students will also study the kinds of choices that have to be made in data presentation and viewing.
Grading Status: Letter Grade
Gen Ed: QI

STOR 72 – First-Year Seminar: Unlocking the Genetic Code
Credits: 3
Description: Introduces students to the world of genetics and DNA and to the use of computers to organize and understand the complex systems associated with the structure and dynamics of DNA and heredity.
Grading Status: Letter Grade
Gen Ed: QI

STOR 89 – First-Year Seminar: Special Topics
Credits: 3
Description: Special Topics Course. Contents will vary each semester.
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit; may be repeated in the same term for different topics; 6 total credits. 2 total completions.

STOR 112 – Decision Models for Business
Credits: 3
Description: An introduction to the basic quantitative models of business with linear and nonlinear functions of single and multiple variables. Linear and nonlinear optimization models and decision models under uncertainty will be covered.
Prerequisite: MATH 110
Grading Status: Letter Grade
Gen Ed: QR

STOR 113 – Decision Models for Business and Economics
Credits: 3
Description: 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.
Prerequisite: MATH 110
Grading Status: Letter Grade
Gen Ed: QR

STOR 115 – Reasoning with Data: Navigating a Quantitative World
Credits: 3
Description: Students will use mathematical and statistical methods to address societal problems, make personal decisions, and reason critically about the world. Authentic contexts may include voting, health and risk, digital humanities, finance, and human behavior. This course does not count as credit towards the psychology or neuroscience majors.
Same As: MATH 115, BIOL 155, PSYC 115
Grading Status: Letter Grade
Gen Ed: QR

STOR 120 – Foundations of Statistics and Data Science
Credits: 4
Description: 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.
Grading Status: Letter Grade
Gen Ed: QR

STOR 151 – Introduction to Data Analysis
Credits: 3
Description: Elementary introduction to statistical reasoning, including sampling, elementary probability, statistical inference, and data analysis. STOR 151 may not be taken for credit by students who have credit for ECON 400 or PSYC 210.
Prerequisite: MATH 110
Grading Status: Letter Grade
Gen Ed: QR

STOR 155 – Introduction to Data Models and Inference
Credits: 3
Description: 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.
Prerequisite: MATH 110
Grading Status: Letter Grade
Gen Ed: QR

STOR 190 – Special Topics
Credits: 3
Description: Examines selected topics from statistics and operations research. Course description is available from the department office.
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit. 12 total credits. 4 total completions.

STOR 215 – Foundations of Decision Sciences
Credits: 3
Description: Introduction to basic concepts and techniques of discrete mathematics with applications to business and social and physical sciences. Topics include logic, sets, functions, combinatorics, discrete probability, graphs, and networks.
Prerequisite: MATH 110
Grading Status: Letter Grade
Gen Ed: QR

STOR 290 – Special Topics
Credits: 3
Description: Examines selected topics from statistics and operations research. Course description is available from the department office.
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit. 12 total credits. 4 total completions.

STOR 305 – Introduction to Decision Analytics
Credits: 3
Description: The use of mathematics to describe and analyze large-scale decision problems. Situations involving the allocation of resources, making decisions in a competitive environment, and dealing with uncertainty are modeled and solved using suitable software packages.
Prerequisite: MATH 152, or STOR 155
Grading Status: Letter Grade
Gen Ed: QI

STOR 320 – Introduction to Data Science
Credits: 4
Description: 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.
Prerequisite: STOR 120 or STOR 155
Grading Status: Letter Grade

STOR 358 – Sample Survey Methodology
Credits: 4
Description: Fundamental principles and methods of sampling populations, with emphasis on simple, random, stratified, and cluster sampling. Sample weights, nonsampling error, and analysis of data from complex designs are covered. Practical experience through participation in the design, execution, and analysis of a sampling project.
Prerequisite: BIOS 550
Permission: Permission of the instructor for students lacking the prerequisite.
Same As: BIOS 664
Grading Status: Letter Grade
Gen Ed: EE- Field Work

STOR 390 – Special Topics in Statistics and Operations Research
Credits: 3
Description: Examines selected topics from statistics and operations research. Course description is available from the department office.
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit; may be repeated in the same term for different topics; 12 total credits. 4 total completions.

STOR 415 – Introduction to Optimization
Credits: 3
Description: Linear, integer, nonlinear, and dynamic programming, classical optimization problems, network theory.
Prerequisite: MATH 547
Grading Status: Letter Grade

STOR 435 – Introduction to Probability
Credits: 3
Description: Introduction to 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.
Prerequisite: MATH 233
Same As: MATH 535
Grading Status: Letter Grade
Gen Ed: QI

STOR 445 – Stochastic Modeling
Credits: 3
Description: Introduction to Markov chains, Poisson process, continuous-time Markov chains, renewal theory. Applications to queueing systems, inventory, and reliability, with emphasis on systems modeling, design, and control.
Prerequisite: STOR 435 or STOR 535, or BIOS 660
Grading Status: Letter Grade

STOR 455 – Methods of Data Analysis
Credits: 3
Description: 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: STOR 120 or STOR 155
Grading Status: Letter Grade

STOR 471 – Long-Term Actuarial Models
Credits: 3
Description: Probability models for long-term insurance and pension systems that involve future contingent payments and failure-time random variables. Introduction to survival distributions and measures of interest and annuities-certain.
Prerequisite: STOR 435 or STOR 535
Grading Status: Letter Grade
Gen Ed: QI

STOR 472 – Short Term Actuarial Models
Credits: 3
Description: Short term probability models for potential losses and their applications to both traditional insurance systems and conventional business decisions. Introduction to stochastic process models of solvency requirements.
Prerequisite: STOR 435 or STOR 535
Grading Status: Letter Grade

STOR 475 – Healthcare Risk Analytics
Credits: 3
Description: This course will introduce students to the healthcare industry and provide hands-on experience with key actuarial and analytical concepts that apply across the actuarial field. Using real world situations, the course will focus on how mathematics and the principles of risk management are used to help insurance companies and employers make better decisions regarding employee benefit insurance products and programs.
Prerequisite: STOR 435 or STOR 535
Grading Status: Letter Grade

STOR 490 – Special Topics
Credits: 3
Description: Examines selected topics from statistics and operations research. Course description is available from the department office.
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit. 12 total credits. 4 total completions.

STOR 496 – Undergraduate Reading and Research in Statistics and Operations Research
Credits: 1-3
Description: Permission of the director of undergraduate studies. This course is intended mainly for students working on honors projects. May be repeated for credit.
Permission: Permission of the director of undergraduate studies.
Grading Status: Letter Grade
Gen Ed: EE- Mentored Research
Repeat Rules: May be repeated for credit; may be repeated in the same term for different topics; 6 total credits. 6 total completions.

STOR 512 – Optimization for Machine Learning and Neural Networks
Credits: 3
Description: An upper-level course focusing on optimization aspects of common and practical problems and topics in statistical learning, machine learning, neural networks, and modern AI. It covers several topics such as optimization perspective of linear regression, nonlinear regression, matrix factorization, stochastic gradient descent, regularization techniques, neural networks, deep learning techniques, and minimax models.
Prerequisite: MATH233 or MATH235, MATH347, COMP110 or COMP116
Grading Status: Letter grade

STOR 515 – Dynamic Decision Analytics
Credits: 3
Description: An introduction to algorithms and modeling techniques that use knowledge gained from prior experience to make intelligent decisions in real time. Topics include Markov decision processes, dynamic programming, multiplicative weights update, exploration vs. exploitation, multi-armed bandits, and two player games.
Prerequisite: STOR 435 or STOR 535, MATH 347
Grading Status: Letter grade

STOR 520 – Statistical Computing for Data Science
Credits: 4
Description: This course provides hands-on experience working with data sets provided in class and downloaded from certain public websites. Lectures cover basic topics such as R programming, visualization, data wrangling and cleaning, exploratory data analysis, web scraping, data merging, predictive modeling, and elements of machine learning. Programming analyses in more advanced areas of data science. Students may not receive credit for both STOR 320 and STOR 520.
Prerequisite: STOR 435 or MATH 535
Grading Status: Letter Grade

STOR 535 – Probability for Data Science
Credits: 3
Description: This course is an advanced undergraduate course in probability with the aim to give students the technical and computational tools for advanced courses in data analysis and machine learning. It covers random variables, moments, binomial, Poisson, normal and related distributions, generating functions, sums and sequences of random variables, statistical applications, Markov chains, multivariate normal and prediction analytics. Students may not receive credit for both STOR 435 and STOR 535.
Prerequisite: MATH 233
Grading Status: Letter Grade

STOR 538 – Sports Analytics
Credits: 3
Description: This course will survey the history of sports analytics across multiple areas and challenge students in team-based projects to practice sports analytics. Students will learn how applied statistics and mathematics help decision makers gain competitive advantages for on-field performance and off-field business decisions.
Prerequisite: STOR 320 or STOR 455
Grading Status: Letter Grade

STOR 555 – Mathematical Statistics
Credits: 3
Description: Functions of random samples and their probability distributions, introductory theory of point and interval estimation and hypothesis testing, elementary decision theory.
Prerequisite: STOR 435 or STOR 535
Grading Status: Letter Grade

STOR 556 – Time Series Data Analysis
Credits: 3
Description: This course covers the fundamental theory and methods for time series data, as well as related statistical software and real-world data applications. Topics include the autocorrelation function, estimation and elimination of trend and seasonality, estimation and forecasting procedures in ARMA models and nonstationary time series models.
Prerequisite: STOR 435 or STOR 535, STOR 455
Grading Status: Letter Grade

STOR 557 – Advanced Methods of Data Analysis
Credits: 3
Description: The course covers advanced data analysis methods beyond those in STOR 455 and how to apply them in a modern computer package, specifically R or R-Studio which are the primary statistical packages for this kind of analysis. Specific topics include (a) Generalized Linear Models; (b) Random Effects; (c) Bayesian Statistics; (d) Nonparametric Methods (kernels, splines and related techniques).
Prerequisite: STOR 435 or STOR 535, STOR 455
Grading Status: Letter Grade

STOR 565 – Machine Learning
Credits: 3
Description: Introduction to theory and methods of machine learning including classification; Bayes risk/rule, linear discriminant analysis, logistic regression, nearest neighbors, and support vector machines; clustering algorithms; overfitting, estimation error, cross validation.
Prerequisite: STOR 215 or MATH 381, STOR 435 or STOR 535
Grading Status: Letter Grade

STOR 572 – Simulation for Analytics
Credits: 3
Description: This upper-level-undergraduate and beginning-graduate-level course introduces the concepts of modeling, programming, and statistical analysis as they arise in stochastic computer simulations. Topics include modeling static and discrete-event simulations of stochastic systems, random number generation, random variate generation, simulation programming, and statistical analysis of simulation input and output.
Prerequisite: STOR 120 or STOR 155, STOR 435 or STOR 535
Grading Status: Letter Grade

STOR 590 – Special Topics in Statistics and Operations Research
Credits: 3
Description: Examines selected topics from statistics and operations research. Course description is available from the department office.
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit; may be repeated in the same term for different topics; 12 total credits. 4 total completions.

STOR 690 – Special Topics
Credits: 3
Description: Examines selected topics from statistics and operations research. Course description is available from the department office.
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit. 12 total credits. 4 total completions.

Independent Study

The Statistics and Analytics program has several options for independent study available. These require permission from the Director of Undergraduate Studies as well as the faculty member who will be supervising/facilitating the study. The workload must be equal to that of any regular seated course – at least three hours of independent work per week is required for each unit of credit, and students must submit a final paper at the end of the semester.

STOR 493 – Internship in Statistics and Operations Research
Credits: 3
Description: Requires permission of the department. 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.
Permission: Permission of the department.
Grading Status: Pass/Fail
Gen Ed: EE- Academic Internship
Repeat Rules: May be repeated for credit. 6 total credits. 2 total completions.

Extended information:

An opportunity to obtain credit for an internship related to statistics, operations research, or actuarial science. This course does not count toward the major or minor. Receives General Ed -EE credit as academic internship. The student intern is required to work at least 8 hours per week, for a minimum of 100 hours, at the internship agency.

In addition to the hours worked, the student must, under the supervision of the faculty supervisor, write a report summarizing her/his activities during the internship and complete other requirements, regular meetings, reading assignments et cetera, as the faculty supervisor sees fit. To receive three hours of academic credit, the student is expected to devote at least nine, 9, combined hours per week on the internship and the academic component of the course.

The student registers for STOR 493 either for the semester during which the internship will be complete or for the first semester that follows the completion of the internship. Typically, students do their internship over the summer and register for STOR 493 either for the second summer session or for the Fall semester of that year.

Below are the steps the student should take in order to be able to register for STOR 493. Note that all these steps should be successfully completed before the student starts her/his internship.

The student contacts the agency she/he will be interning for and has the “Internship Agency Supplement” form filled and signed by her/his supervisor at the agency.
The student emails the Director of Undergraduate Studies expressing her/his interest for signing up for STOR 493 with the filled and signed Internship Agency Supplement form attached.
If the Director of Undergraduate Studies finds the internship suitable for STOR 493, she/he informs the student which faculty member will serve as the student’s faculty supervisor.
The student initiates the Independent Study Learning Contract through the Online Learning Contract Manager (OLCM).

Once these steps are successfully completed and the contract has been officially approved by the Director of Undergraduate Studies, the student is registered for the appropriate section of STOR 493. The contract must be completed and approved by the Friday of the first week of classes or by the first day of the internship, whichever comes first.

At the completion of their internship, students must arrange the “Internship Evaluation Form” to be filled out by their agency supervisor and emailed to their faculty supervisors.

Extended information:

Individual reading, study or project supervised by a faculty member. Receives General Ed- EE credit as mentored research. A student who wishes to pursue this independent study option should contact a faculty member who teaches in the research area the student is interested to inquire about whether the faculty member would be willing to supervise the project.

Alternatively, the student can also contact faculty members or the Director of Undergraduate Studies to ask about any potential projects that are appropriate for the student’s level of training. It will be helpful for the student to email a short statement of interest along with a transcript and a resume.

Once the student reaches an agreement with a faculty supervisor, he/she must complete an Independent Study Learning Contract through the Online Learning Contract Manager (OLCM). The contract must be completed and approved by the Friday of the first week of classes.

STOR 691H/STOR 692H – Honors in Statistics and Analytics
Credits: 3
Description: Permission of the department. Majors only. Individual reading, study, or project supervised by a faculty member.
Grading Status: Letter Grade
Gen Ed: EE- Mentored Research

Extended information:

Individual senior thesis project supervised by a faculty member. Receives General Ed- EE credit as mentored research. This course should be taken for two semesters: first as 691H and second as 692H. Open to students with a GPA of 3.3 or above. Students who successfully complete the two-course sequence graduate with “Honors” or “Highest Honors.”

A student who wishes to pursue this independent study option should contact a faculty member who teaches in the research area the student is interested to inquire about whether the faculty member would be willing to supervise the project.

Once the student reaches an agreement with a faculty supervisor, the student must complete an Independent Study Learning Contract through the Online Learning Contract Manager (OLCM). The contract must be completed and approved by the Friday of the first week of classes.

For more information and deadlines et cetera, see Honors Carolina’s Senior Honors Thesis page and the Senior Thesis Guidelines.

Here is a listing of recent Senior Honors Thesis submissions.

You will find Instructions for filling out the Online Learning Contract Manager, OLCM, below: