Free Course • Vidaara

R Programming &
Data Analytics

A complete, Coursera-quality course in R — from syntax basics to Shiny web apps and machine learning. Concept notes, working code, solved examples, and self-assessment quizzes all in one place.

21 Chapters 250+ Hours Beginner to Advanced No Prerequisites
21Chapters
100+Code Examples
200+Practice MCQs
FreeAlways

Start Learning Today

Begin with Chapter 1 — no software install needed to read. When you're ready, install R and RStudio (both free) and run the code yourself.

Start Chapter 1

What You Will Learn

Install and use R and RStudio professionally
Master vectors, matrices, lists, and data frames
Import, clean, and reshape data with tidyverse
Transform data at scale with dplyr and tidyr
Create publication-ready plots with ggplot2
Build interactive visualisations with plotly
Run regression, ANOVA, and hypothesis tests
Apply machine learning with the caret package
Build decision trees and random forests
Perform text mining and NLP in R
Connect R to SQL and NoSQL databases
Write reproducible reports with R Markdown
Build interactive Shiny web applications
Apply PCA and clustering algorithms
Understand data ethics and responsible AI
Build a capstone data analytics project
Choose Your Route

Learning Paths

📊

Data Analyst

Go from raw spreadsheets to clean charts and summary stats — the core analytics workflow.

7 chapters • in order
1. Introduction to R & RStudio 2. R Data Structures In Depth 3. Data Import & Export 4. Data Wrangling with dplyr 5. Data Reshaping with tidyr 6. Data Visualisation with ggplot2 7. Statistical Analysis in R
Start this path
🤖

Data Scientist

Everything an analyst knows, plus modelling: regression, machine learning and clustering.

7 chapters • in order
1. Data Wrangling with dplyr 2. Statistical Analysis in R 3. Regression Analysis 4. Machine Learning with caret 5. Decision Trees & Random Forests 6. Unsupervised Learning & PCA 7. Time Series Analysis
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🎓

Researcher / Academic

Statistics done right, reproducible reports, and ethics — built for theses and journals.

6 chapters • in order
1. Introduction to R & RStudio 2. Statistical Analysis in R 3. Regression Analysis 4. Reproducible Research — R Markdown 5. Data Ethics & Best Practices 6. Capstone Project
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🏛️

Govt-Exam & Public Data

Work with official datasets: import, wrangle, query databases and chart the results.

6 chapters • in order
1. Introduction to R & RStudio 2. R Data Structures In Depth 3. Data Import & Export 4. Data Wrangling with dplyr 5. Data Visualisation with ggplot2 6. Working with Databases in R
Start this path
🚀

App Builder (Shiny)

Turn analysis into interactive web dashboards anyone can use in a browser.

6 chapters • in order
1. Introduction to R & RStudio 2. R Data Structures In Depth 3. Data Wrangling with dplyr 4. Data Visualisation with ggplot2 5. Shiny Web Applications 6. Capstone Project
Start this path
Curriculum

21 Chapters — From Zero to Production

Beginner Beginner Track — 3 Chapters
Chapter 1

Introduction to R & RStudio

10 hrs
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Chapter 2

R Data Structures In Depth

12 hrs
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Chapter 3

Data Import & Export

10 hrs
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Intermediate Intermediate Track — 8 Chapters
Chapter 4

Data Wrangling with dplyr

15 hrs
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Chapter 5

Data Reshaping with tidyr

8 hrs
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Chapter 6

Data Visualisation with ggplot2

15 hrs
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Chapter 7

Advanced ggplot2 & plotly

10 hrs
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Chapter 8

Statistical Analysis in R

12 hrs
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Chapter 9

Regression Analysis

12 hrs
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Chapter 15

Working with Databases in R

8 hrs
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Chapter 17

Reproducible Research — R Markdown

8 hrs
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Advanced Advanced Track — 8 Chapters
Chapter 10

Time Series Analysis

10 hrs
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Chapter 11

Machine Learning with caret

15 hrs
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Chapter 12

Decision Trees & Random Forests

10 hrs
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Chapter 13

Unsupervised Learning & PCA

10 hrs
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Chapter 14

Text Mining & NLP with R

10 hrs
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Chapter 16

Functional Programming with purrr

8 hrs
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Chapter 18

Shiny Web Applications

15 hrs
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Chapter 20

Capstone Project

30 hrs
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All Levels All Levels Track — 2 Chapters
Chapter 19

Data Ethics & Best Practices

5 hrs
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Chapter 21

Career Paths & Certification

4 hrs
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Your Progress, Levelled Up

The R Skill Ladder

Each rung is a real, recognisable level of R skill. Work up the ladder chapter by chapter — your progress is tracked, and finishing the course earns your Vidaara completion certificate.

Level 1

R Beginner

Run R, use variables, vectors and data frames.

Chapters 1–2
Level 2

R Associate

Import, wrangle, reshape and visualise data.

Chapters 3–7
Level 3

R Professional

Statistics, regression and reproducible reports.

Chapters 8–10, 17
Level 4

R Data Scientist

Machine learning, trees, clustering, text mining.

Chapters 11–14
Level 5

R Research Analyst

Databases, functional pipelines, clean code.

Chapters 15–16, 19
Level 6

R App & AI Engineer

Shiny apps and a full capstone project.

Chapters 18, 20–21

These levels are a learning guide to track your growth. They are not external professional certifications — but every concept maps to skills used in real analyst, data-science and research roles.

How the Course Works

1

Read the Theory

Each concept opens with a precise explanation — no filler, no fluff. Built for real understanding.

2

Run the Code

Every concept includes working R code. Copy it into RStudio and experiment. Change values and see what happens.

3

Study Solved Problems

3 worked examples per concept — real data analysis scenarios with step-by-step solutions.

4

Self-Assess

MCQ quizzes after each concept. Try before you reveal — builds memory better than passive reading.