Learners will analyze data using R, apply core statistical techniques, build analytical models, and interpret insights through visualization and real-world use cases. By the end of this course, learners will be able to confidently use R programming to perform data analysis, statistical modeling, and exploratory analytics.

This beginner-friendly course provides a structured, end-to-end introduction to Data Analytics using R, starting from R’s origin, architecture, and syntax, and progressing through vectors, data frames, visualization, and statistical methods. Learners gain hands-on exposure to essential programming concepts, data handling techniques, and analytical workflows that are widely used in academia and industry. What makes this course unique is its subtitles-driven, concept-aligned curriculum, ensuring every topic directly reflects real instructional explanations rather than abstract theory. The course emphasizes practical analytics, including regression, decision trees, time series analysis, and business-focused case studies such as insurance analytics. Designed for aspiring data analysts, students, and professionals, this course builds a strong foundation in R programming while developing analytical thinking skills that are transferable to real-world data science and statistical problem-solving scenarios.