This course introduces the statistical foundations essential for data science, integrating probability theory, statistical inference, and modern modeling techniques with hands-on applications in R. Students will develop the ability to summarize, visualize, and clean complex datasets, apply principles of probability and estimation, and conduct hypothesis testing and analysis of variance. The course also covers regression modeling, supervised and unsupervised learning methods, resampling techniques, and model evaluation. Emphasis is placed on reproducible workflows, interpretation of results, and effective communication of statistical findings. By the end of the course, students will be equipped with both theoretical knowledge and applied skills that prepare them for advanced coursework in data science and for professional roles requiring statistical reasoning and data analysis.