DevelopmentAdded Sep 17, 2026

Hands-On R Programming: Build Real World Data Projects

Hands-on learning with R: Analyze, visualize, and interpret real world data like a pro.

4.0 / 5.0
16 ratings
2h 43m 44s
On-demand
English
Audio
Brighter Futures Hub
Instructor
Hands-On R Programming: Build Real World Data Projects100% OFF
  • 2h 43m 44s on-demand video
  • Certificate of Completion
  • Mobile, TV & Desktop Access
  • Full Lifetime Access

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 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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