Analyze Data Using R for Statistical and Predictive Modeling

Overview

By the end of this course, learners will be able to analyze data using R, apply statistical methods, build predictive models, and interpret analytical results for real-world decision-making. Learners will gain hands-on experience with R programming fundamentals, data manipulation, visualization techniques, and advanced analytics such as regression, decision trees, and time series analysis. This course is designed to guide learners from the basics of R—its origin, architecture, syntax, and data structures—to practical data analysis and business applications. Through structured modules, learners will work with vectors, data frames, loops, functions, and charts, and then progress to statistical analytics, distribution functions, and predictive modeling techniques. Real-world scenarios, including insurance industry case studies, help learners understand how analytics is applied in professional environments. What makes this course unique is its balanced focus on both programming and analytics, making it suitable for beginners as well as professionals transitioning into data analytics roles. With clearly aligned learning objectives, graded assessments, and practice quizzes, learners will build job-ready skills in R that can be applied across industries such as finance, insurance, and data science. Completing this course equips learners with a strong analytical mindset and practical R skills to confidently explore data, generate insights, and support data-driven decisions.

Syllabus

Foundations of R Programming

This module introduces learners to the R programming language by exploring its origin, architecture, syntax, and core data structures. Learners will build a strong foundation in R by understanding how to write basic programs, work with data types, create vectors, and define functions, preparing them for data analysis tasks.

Data Handling and Visualization in R

This module focuses on controlling program flow, manipulating text data, working with data frames, and creating visualizations in R. Learners will develop skills to process real-world datasets, apply logical conditions, and generate meaningful charts to communicate insights effectively.

Statistical Analysis and Real-World Applications

This module equips learners with statistical and analytical techniques using R, including regression models, decision trees, and time series analysis. Learners will apply data exploration, modeling, and interpretation skills to solve real-world business problems and make data-driven decisions.