For current students at NC Wesleyan University, please refer to the For Current Students page for more information.
I have had the pleasure of teaching brilliant students across 8 different courses (17 different sections total) during my time at NC Wesleyan University, UNC Chapel Hill, and East Carolina University. Course Descriptions and syllabi for recently taught courses can be found below. For syllabi from before Spring 2023, including all courses taught at East Carolina University, please email me to request.
News: Both the Minor in Data Science and the APS Certificate in Data Science at NC Wesleyan have been approved! Please contact me or the registrar's office if you would like more information on these exciting (and resume-boosting) opportunities.
I am proud to announce the availability of the Data Science Minor at NC Wesleyan University. In addition, we have established a Data Science Certificate through the Adult & Professional Studies (APS) program — with the certificate’s first cohort set to begin in Summer 2026. These new programs reflect our commitment to providing students with rigorous, market-relevant training in data analysis, computing, visualization, and decision-making skills. Feel free to email me or NC Wesleyan's registrar's office for more information.
Fall 2025
Course Catalog Description: Intended for first-year students at NCWU, this course is designed
to invite students to participate in the university's academic life
through the study of a discipline-specific issue, problem, or topic
of interest. Taught by a member of NCWU's full-time faculty, the
course will introduce students to a scholarly discipline through a
specialized topic in which the faculty member possesses interest
and expertise. Designed for students with little or no prior college
experience, the course will also introduce and develop habits of
mind that will help to foster success throughout students' careers
at the university.
This course, in particular, will serve as an introduction to modern
data science techniques that can be used in business decision
making. These topics are either broad or advanced in nature, so
instead of going into depth, we will only break the surface while
giving a gentle introduction to each of the following topics: Data
exploration (and basic coding), Simulation, Modeling using
Machine Learning, Reinforcement learning, and Ethics.
Fall 2025
Course Catalog Description: Topics will include descriptive statistical techniques including sampling techniques, collection of data, frequency distributions, graphing of data and analysis of data (measures of central tendency, location and spread); introduction to relationships between quantitative variables (correlation and least squares regression); probability rules with known probability distributions such as binomial and normal distributions; introduction to statistical inference (confidence intervals, hypothesis testing and making predictions).
Fall 2024
Course Catalog 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).
Fall 2023
Course Catalog Description: The course gives a solid introduction to rigorous mathematical thinking and problem solving, all of which are fundamental in data science. It covers proofs, mathematical induction, counting, and the basics of graph theory.
Fall 2022
Course Catalog 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.
Last Instructed Spring 2023
Course Catalog 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.