Teaching
Teaching Philosophy
My teaching philosophy emphasizes active learning, practical problem solving, and the connection between theoretical concepts and real-world applications. I aim to help students develop both a strong conceptual foundation and the computational skills needed to apply data science, statistics, and artificial intelligence methods to meaningful problems.
In my courses, I integrate lectures with hands-on programming, guided exercises, applied assignments, and project-based learning. I encourage students to move beyond simply using computational tools by understanding the reasoning behind the methods, evaluating results critically, and communicating their findings clearly.
Whenever appropriate, I incorporate examples from research and real-world datasets so that students can see how concepts taught in the classroom translate into practical data science and artificial intelligence workflows.
Current Courses
Fall 2026
An introductory course in probability, statistics, data analysis, and statistical reasoning. The course emphasizes interpretation of data, statistical methods, and computational analysis using R.
A doctoral-level research experience focused on developing independent research skills, reproducible computational workflows, research documentation, scholarly communication, and progress toward dissertation research.
Previous Courses
Spring 2026
A hands-on course in Python for data science covering Python programming, NumPy, Pandas, Matplotlib, data preprocessing, visualization, machine learning with scikit-learn, model evaluation, and project-based data analysis.
An introductory course emphasizing probability, statistical reasoning, data analysis, interpretation of results, and computational analysis using R.
A graduate-level applied project course in which students develop and complete a substantial data science project involving problem formulation, analysis, implementation, interpretation, and communication of results.
A doctoral research experience focused on independent research, computational experimentation, research documentation, and scholarly communication.
Fall 2025
A hands-on introduction to Python programming and its application to data science, including data manipulation, visualization, statistical analysis, machine learning, and applied projects.
An introductory statistics and probability course focused on statistical reasoning, data analysis, interpretation, and computational problem solving.
Student Learning and Projects
I strongly encourage students to apply course concepts through hands-on projects and independent exploration. My courses emphasize reproducible analysis, computational problem solving, interpretation of results, and clear communication of technical findings.
In Python for Data Science, students progress from foundational Python programming and data manipulation to visualization and machine learning before completing an applied data science project. Students are also encouraged to present their work through university research, analytics, and scholarly events.
In Fall 2025, students Luz Corral Parra and Anna Seville, whom I mentored in DATA 4140 — Python for Data Science, earned Third Place (tie) in the Undergraduate Poster Competition at KSU Analytics Day for their project, “Predicting Circuit Board Failure Using Machine Learning.”
In Spring 2026, Bryce Wishart, whom I mentored in DATA 4140 — Python for Data Science, earned Second Place in the Undergraduate Category at KSU Analytics Day for his project, “Steam Marketplace: Deep Model Prediction of Video Game User Ratings.”
Teaching Interests
- Python for Data Science
- Probability and Statistics
- Applied Machine Learning
- Artificial Intelligence and Deep Learning
- Medical Image Analysis
- Digital Image Processing
- Data Visualization
- Large Language Models and Agentic AI
- AI for Healthcare