Hands-On R Programming: Build Real World Data Projects
Date: 5 September 2025 | Posted by: AD-TEAM
Hands-On R Programming: Build Real World Data Projects
Published 8/2025
Duration: 2h 44m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 837.66 MB
Genre: eLearning | Language: English
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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