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

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)

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
More Info

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