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Hands-On R Programming: Build Real World Data Projects
Rating: 3.9 out of 5(15 ratings)
5,126 students

Hands-On R Programming: Build Real World Data Projects

Hands-on learning with R: Analyze, visualize, and interpret real world data like a pro.
Created byBrighter Futures Hub
Last updated 8/2025
English

What you'll learn

  • What is R?
  • History and applications of R
  • Installing and Configuring R and RStudio
  • Basic R Syntax and Data Types
  • Vectors, Matrices, and Arrays
  • Data Frames and Lists
  • Conditional Statements (if-else)
  • Loops (for, while)
  • Creating and Using Functions in R
  • Function Arguments and Scoping
  • Data Manipulation with dplyr (filter, select, mutate, arrange)
  • Data Tidying with Tidyr (pivot_longer, pivot_wider)
  • Joining and Merging Data Frames
  • Creating Various Types of Plots (scatter plots, bar plots, line plots, histograms)
  • Customizing Plot Aesthetics (colors, labels, themes)
  • Creating Interactive Plots
  • Descriptive Statistics (mean, median, standard deviation, quartiles)
  • Hypothesis Testing (t-tests, chi-squared tests)
  • Regression Analysis (linear regression, multiple regression)

Course content

5 sections • 18 lectures • 2h 43m total length

  • What is R? History and applications of R5:57
  • Installing and Configuring R and RStudio11:23
  • Basic R Syntax and Data Types9:47
  • Vectors, Matrices, and Arrays7:57

    Explore vectors, matrices, and arrays in R, creating and manipulating numeric vectors, accessing elements by index, and transposing matrices for 2D and 3D data.

  • Data Frames and Lists11:00

Requirements

  • No R programming experience needed

Description

Welcome to Hands-On R Programming: Build Real World Data Projects — your practical path to mastering R through real life applications. Whether you're a beginner or someone looking to strengthen your data skills, this course will give you hands-on experience with one of the most powerful tools in data science.


Why Learn R?

R is widely used in data science, statistics, machine learning, and academia — especially when working with large datasets and generating clean, meaningful visualizations. It’s a favorite among data analysts, researchers, and companies worldwide.


But instead of just learning R syntax in isolation, this course focuses on building real world projects that reflect the kinds of tasks data professionals face every day.


What You'll Learn

  • R programming fundamentals and best practices

  • Data cleaning and transformation

  • Exploratory Data Analysis (EDA)

  • Working with real world datasets: business, healthcare, finance, and more

  • Building dashboards and automated reports

  • Introduction to machine learning using caret and randomForest

  • Statistical analysis, hypothesis testing, and correlation techniques

  • How to structure, document, and present your projects


Course Features

  • Step-by-step, beginner friendly tutorials

  • Lifetime access

  • Certificate of Completion


Start Learning Today

By the end of this course, you'll be confident in using R to clean, analyze, visualize, and present data.

Who this course is for:

  • Anyone who wants to build a strong portfolio of R data projects
  • Students in statistics, economics, or data science
  • Beginners who want to learn R by doing, not just watching
  • Data analysts and professionals transitioning into R from Excel or Python

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