...The repository is designed as a shared space for code examples, datasets, and instructional materials, helping learners follow along with lectures and assignments. It spans essential topics such as R programming, data cleaning, exploratory data analysis, statistical inference, regression models, machine learning, and practical data science projects. By providing centralized resources, the repo makes it easier for students to practice concepts and replicate examples from the curriculum. It also offers a structured view of how multiple disciplines—programming, statistics, and applied data analysis—come together in a professional workflow.