Data Analytics with R Programming Certification Training Course Syllabus

A comprehensive, project-driven R analytics course that equips you to clean, visualize, model, and deploy data insights end-to-end. This course spans approximately 20.5 hours of hands-on learning, structured across seven modules. You’ll progress from foundational R programming and data manipulation to advanced visualization and predictive modeling, culminating in a real-world capstone project that integrates all skills. Designed for beginners with basic R familiarity, the curriculum emphasizes practical application using real datasets and industry-standard tools.

Module 1: R Environment & Data Import

Estimated time: 2 hours

• Installing R and RStudio
• Package management and working directory setup
• Understanding R data types and structures
• Loading CSV, Excel, and JSON datasets
• Inspecting data with str(), glimpse(), and summary functions

Module 2: Data Wrangling with dplyr & tidyr

Estimated time: 3 hours

• Filtering and selecting data using dplyr
• Creating new variables with mutate()
• Summarizing data by groups using group_by() and summarize()
• Reshaping data with pivot_longer() and pivot_wider()
• Cleaning and transforming messy survey data

Module 3: Exploratory Data Visualization

Estimated time: 3 hours

• Understanding the grammar of graphics
• Building plots with ggplot2 aesthetics and geometries
• Customizing scales, legends, and themes
• Creating multi-panel visualizations using faceting
• Identifying trends and outliers through visualization

Module 4: Statistical Analysis in R

Estimated time: 2.5 hours

• Computing descriptive statistics and confidence intervals
• Performing t-tests for comparing group means
• Conducting chi-square tests for categorical associations
• Applying one-way ANOVA for multiple group comparisons
• Interpreting p-values and test results

Module 5: Predictive Modeling with caret

Estimated time: 4 hours

• Partitioning data into training and test sets
• Implementing cross-validation techniques
• Training linear and logistic regression models
• Building decision trees and random forests
• Evaluating model performance using RMSE and accuracy
• Tuning hyperparameters for optimal results

Module 6: Advanced Visualization & Reporting

Estimated time: 2 hours

• Creating interactive plots with plotly
• Developing dashboards using Shiny
• Generating reproducible reports with R Markdown
• Deploying a Shiny app for real-time data exploration

Module 7: Capstone Project – End-to-End Analytics Workflow

Estimated time: 4 hours

• Scoping an analytics project (e.g., customer churn or sales forecasting)
• Building a complete data pipeline from import to cleaning
• Performing exploratory analysis and statistical testing
• Developing predictive models and visualizations
• Presenting insights via an interactive Shiny dashboard

Prerequisites

• Basic familiarity with R programming
• Understanding of fundamental statistical concepts
• Comfort with installing and navigating RStudio

What You'll Be Able to Do After

• Import, clean, and manipulate diverse datasets using R
• Create publication-quality static and interactive visualizations
• Conduct hypothesis testing and interpret statistical results
• Build and evaluate predictive models for real-world problems
• Deliver automated, reproducible reports and dashboards using R Markdown and Shiny