This course covers the concepts and implementation of statistical methods tailored for non-statistics majors. It emphasizes understanding, analyzing, and interpreting data, with a particular focus on exploratory data analysis, inferences, hypothesis testing frameworks, and introductory linear regression.
This course covers advanced data analysis concepts and their implementation, including selected statistical techniques and introductory machine learning methods. The focus is on prediction, association, and relevant evaluation techniques.
This course covers the selection and application of appropriate statistical methods and data science technologies to accomplish analytical objectives. Participants will write code to implement descriptive, inferential, and predictive statistical techniques across various data types and purposes.
This course provides hands-on programming experience in statistical data analysis using a variety of data types. Topics include exploratory data analysis, hypothesis testing, confidence intervals, regression, classification, and model evaluation. The course also introduces foundational concepts in Markov chains and Bayesian inference through beginner-friendly examples. Students will use widely adopted data science tools, including JupyterLab, to develop practical skills in data-driven problem solving.
At the doctoral level, I have served as Statistics co-major advisor (1 Ph.D. student), Statistics minor advisor (1 Ph.D. student), dissertation committee member (2 Ph.D. students), and research mentor through funded projects (2 Ph.D. students).
At the master's level, I have supervised 1 M.S. Data Science capstone project, served as Statistics minor advisor for 1 M.S. student, and participated on committees for 5 M.S. students.
At the undergraduate level, I have advised 3 B.S. Data Science capstone projects.
This course introduces the fundamentals of database systems and data management in large-scale data environments. Topics include SQL, data storage architectures, data management strategies, and data provenance. Students will develop practical skills in designing, querying, and managing databases to support reliable and efficient data-driven applications.
Students will learn popular AI/Machine Learning (ML) methods and data science skills through hands-on activities using open-source analytical tools and pre-processed datasets. There are three levels of activities, which can be used standalone or as a series, based on students’ backgrounds. Activity Levels 1 and 2 focus on the most common data format—rectangular data. Specifically, Activity Level 1 leverages a GUI-based tool, requiring no programming skills, while Activity Level 2 encourages basic programming in R and R Studio. Activity Level 3 introduces two types of non-rectangular data analysis—deep learning for imaging data (using Google Colab) and Natural Language Processing (NLP) for text data. Activity Level 3 introduces beginner-level activities rather than programming skills.
R is one of the most commonly used programming languages in the biomedical sciences. In this module, students will learn basic programming skills in R and R Studio and begin using the ggplot package to visualize biomedical data, with no past coding experience needed.
2026 | “A Quick Start to Machine Learning” | 90th Annual Mississippi Academy of Sciences (MAS) Meeting – Mississippi INBRE Data Science Workshop | Biloxi, MS | March 19, 2026
2024 | “Beginner Tool Introduction and Demonstration for Data Science/AI” | 88th Annual Mississippi Academy of Sciences (MAS) Meeting – 1st Data Science Workshop | Hattiesburg, MS | March 1, 2024
2023 | “Careers in Data Science” | Mississippi INBRE Scholars Virtual Professional Development Experience, Mississippi INBRE Research Scholars (MIRS) Program | Virtual | July 14, 2023