R Programming for Data Analysis, Part 2

Catalog description

Building on the foundations established in Part 1, this course deepens learners’ competence in applying R programming to more advanced analytical scenarios. Students will explore hands -on case studies to strengthen their proficiency, working with real -world datasets such as polling data, wildfire statistics, and basketball shot records. Additionally, they will be introduced to the fundamentals of predictive analysis, learning how to implement linear regression and classification models to forecast future outcomes. Finally, Part 2 covers methods for transforming completed analyses into engaging presentations using R Markdown, ensuring that students can effectively communicate their insights to various audiences. Upon completion, learners will possess the practical skills required to both analyze and clearly presen t complex data -driven findings.