This Specialization provides a comprehensive, beginner-friendly pathway to mastering data analytics and statistical modeling using R. Learners progress from core programming concepts and data structures to advanced statistical techniques, machine learning workflows, and exploratory data analysis using ggplot2. Through structured modules and applied case studies, participants build the ability to clean, analyze, model, and interpret real-world datasets. By the end of the program, learners will confidently apply quantitative analysis, visualization, and predictive modeling techniques in academic, research, and business environments.
Applied Learning Project
Learners complete hands-on, project-based assignments that simulate real-world analytical scenarios, including regression modeling, correlation analysis, decision trees, time series exploration, and business case studies such as insurance analytics. Each project requires learners to apply R programming, statistical reasoning, and visualization techniques to solve practical data-driven problems and present actionable insights.
