Overview
In this course, students will learn the fundamentals of R programming and its application in data science., They will practice data manipulation, visualization, and statistical analysis with real-world datasets., Advanced topics such as probability, hypothesis testing, and regression modeling will also be covered., By the end, students will be able to analyze, interpret, and visualize data confidently using R.
This course is perfect for beginners who want to start learning data science with R., It’s great for students, analysts, or professionals looking to boost their data skills., QA engineers or developers wanting to explore data analysis will also benefit., Anyone curious about data and eager to learn practical R programming is welcome!
No programming experience is needed., Just a computer with internet access and R installed., Basic math helps, but it’s not required., Bring curiosity and a desire to learn data science with R!
Welcome to “R Programming for Data Science: From Basics to Advanced Analysis” — your complete guide to learning R and applying it to real-world data science tasks.
This course is designed for beginners and aspiring data analysts who want to build a strong foundation in R programming and data analysis, even if they have no prior coding experience.
You’ll start by learning how to install and use R and RStudio, understand the core concepts of R programming, and work with data structures like vectors, matrices, and data frames. Step by step, you’ll move into data manipulation, visualization, and statistical analysis, using tools like ggplot2 and R’s built-in functions.
As the course progresses, you’ll explore probability, hypothesis testing, regression, working with Data structures, understanding R Fundamentals, Data Input and management, and advanced Data visualization in R techniques, gaining the practical skills needed to analyze and interpret data with confidence. By the end of the course, you’ll not only master the fundamentals of R but also know how to apply them in data-driven projects.
Whether you’re a student, QA engineer, developer, or analyst looking to move into data science, this course will guide you every step of the way — from the basics to advanced analysis.
This course contains the use of artificial intelligence.
Joro Trifonov
I started my career as a Quality Assurance Engineer, where I developed a strong eye for detail, analytical thinking, and a deep understanding of how software behaves in real-world conditions. Over time, my curiosity about how things work under the hood led me to transition into software development, where I now specialize in building reliable, maintainable, and user-focused applications.
With a foundation in testing and a passion for clean code, I bring a unique perspective to programming—combining precision, quality, and innovation.
Today, I also create and teach courses on QA automation and software development, helping others bridge the gap between testing and programming, just as I once did. My mission is to empower testers to become confident coders and developers to write higher-quality software.