R is widely used in data science, statistics, machine learning, and academia, especially when working with large datasets and generating clean, meaningful visualizations. Instead of just learning R syntax in isolation, this course focuses on building real world projects that reflect the tasks data professionals face every day. You will learn R fundamentals, data cleaning and transformation with dplyr and tidyr, exploratory data analysis, working with real world datasets from business, healthcare, and finance, creating interactive plots, descriptive statistics, hypothesis testing, and regression analysis.

This Course Offers

• Complete R programming fundamentals: Learn what R is, its history and applications, installing and configuring R and RStudio, basic syntax and data types, vectors, matrices, and arrays, data frames and lists, conditional statements (if else), loops (for, while), creating and using functions, function arguments and scoping.
• Data manipulation and tidying with dplyr and tidyr: Master data manipulation with dplyr including filter, select, mutate, arrange. Data tidying with tidyr including pivot_longer and pivot_wider. Joining and merging data frames.
• Data visualization and exploratory data analysis: Create various types of plots including scatter plots, bar plots, line plots, histograms. Customize plot aesthetics including colors, labels, themes. Create interactive plots.
• Statistics and machine learning introduction: Learn descriptive statistics including mean, median, standard deviation, quartiles. Hypothesis testing including t tests and chi squared tests. Regression analysis including linear regression and multiple regression.

Why We Love This Course

1. It focuses on building real world projects, not just syntax. You learn by doing.
2. One student review noted the concept of practicality is great.
3. It covers both data manipulation with dplyr/tidyr and visualization with ggplot2 style plots. You learn the modern R workflow.
4. It is beginner friendly with no R programming experience needed.

R is a powerful language for statistics and data visualization. The question is whether you want to learn R through hands on projects that build a portfolio, or learn syntax without seeing how it applies to real data.

• 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.

• No R programming experience is needed.

Interested in exploring more lessons? Check out our full course library to continue building your skills and advancing your learning journey.