Analyze Data Using R for Statistical Analytics

Overview

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.

Syllabus

Introduction to R Programming Fundamentals

This module introduces learners to the R programming language, covering its origin, architecture, file types, syntax rules, and core data types used in data analytics and statistical computing.

Core Programming Concepts in R

This module focuses on essential R programming constructs, including vectors, variables, functions, operators, control structures, and string manipulation techniques required for efficient data processing.

Data Structures and Visualization

This module introduces data frames and visualization techniques in R, enabling learners to organize data and create meaningful graphical representations for exploratory data analysis.

Statistical Analysis and Real-World Analytics

This module covers statistical methods, regression models, decision trees, time series analysis, and real-world business applications to perform predictive and descriptive analytics using R.