teaching

Course materials are available upon request.

Introduction to SAS for Data Scientists

• SAS syntax, programming structure, and macro development
• Data preparation, cleaning, and merging workflows
• File handling and reproducible analysis pipelines
• Hypothesis testing and foundational modeling

Data Science with R

• Data manipulation and exploratory analysis in R
• Statistical graphics with ggplot2
• Data acquisition and web data collection
• End-to-end project workflows

Data Visualization

• Principles of effective visual communication
• Visualization strategies for different data types
• Interactive dashboards and web apps with Shiny
• Project-based communication of analytical findings

Linear Algebra

• Vectors, matrices, and linear transformations
• Vector spaces and basis concepts
• Eigenvalues and eigenvectors
• Applications to data science and modeling