Gain a solid understanding of R, one of the most widely used languages for data analysis. Explore fundamental data types and one-dimensional values, then advance to multi-dimensional structures, including matrices, data frames, and lists. Build the skills needed to apply R effectively in statistical analysis and data-driven decision-making.

In the second part of R Introduction, you will learn about multi-dimensional built-in structures, such as matrices, data frames, and lists.

Gain practical experience in data analysis with R by learning how to clean, transform, and visualize datasets. Explore essential workflows such as selecting and filtering data, handling missing values, and summarizing results. Build confidence in preparing data for insights, reporting, and deeper statistical exploration.

This project settled for beginners to learn the very basic principles and concepts of Jupyter notebooks with R.

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R is one of the most popular programming languages among data analysts and data scientists. R has a wide community that will always help you with any problem. Also, R has tons of different packages designed to solve various problems. The language can be easily applied to statistics and data analytics problems. In the first part, you will be introduced to the very basics of R and will deal with single 1-D values of different types.

Master the essentials of control flow in R, including conditional statements, loops, and the powerful apply family of functions. This course is designed for beginners and will guide you through practical, real-world examples to help you write efficient and readable R code.

Master the essential skills for ensuring data quality in R. Learn to detect and handle missing, duplicated, and inconsistent data, identify outliers, apply data validation rules, and build robust data cleaning pipelines. This course combines engaging theory with hands-on challenges to prepare you for real-world data cleaning tasks.

Master the essential data manipulation skills in R using dplyr, grouping and aggregation, and joining data frames. This course is designed for beginners and provides hands-on, real-world tasks to build your confidence in transforming and analyzing data for analytics.

Master the essential data manipulation skills in R using dplyr, grouping and aggregation, and joining data frames. This course is designed for beginners and provides hands-on, real-world tasks to build your confidence in transforming and analyzing data for analytics.

A hands-on project-based course guiding learners through the process of exploring, analyzing, and visualizing the msleep dataset using R and ggplot2. Learners will develop practical skills in data visualization, from basic plots to advanced customization and storytelling with data.

This beginner-level course introduces data visualization in R using the powerful ggplot2 package. Learn how to create, customize, and interpret a variety of charts and plots essential for effective data analysis. The course covers the fundamentals of ggplot2, guiding you through building basic plots, enhancing them with themes and annotations, and understanding how to choose the right visualizations for different data types.

Master the art of writing and using functions in R, from basic syntax to advanced functional programming techniques. This course guides you through creating your own functions, leveraging anonymous functions, and applying functional programming concepts to solve real-world problems in R.

This beginner-friendly course introduces learners to the fundamentals of data visualization using the ggplot2 package in R. Through practical exercises, students will learn how to create a variety of essential visualizations, including scatter plots, bar charts, histograms, and boxplots. The course emphasizes hands-on experience, guiding learners step-by-step through the process of building, customizing, and interpreting visualizations to effectively communicate data insights.

A beginner-friendly course designed to introduce you to the fundamentals of R programming. Learn the essential concepts, syntax, and techniques to start analyzing and visualizing data using R.