R Programming for Data Science and Data Analytics

R programming course for data science and analytics. Learn RStudio, data analysis, cleaning, visualisation and reporting skills.

This course gives a clear start to R programming for data science and data analytics. It is made for people who want to learn how to work with data using simple coding steps in R. You will start with basic ideas of data science, then move into how R and RStudio work as tools for analysis. Each unit builds your skills step by step using simple examples and practice tasks.

You will learn how to handle data using vectors, matrices, factors, lists, and data frames. These are the main building blocks used in R programming. The course also explains how to use functions, loops, and conditional statements to solve real data problems. You will also learn how to install packages and use them for data work.

Later in the course, you will work with data cleaning, plotting, and data manipulation using dplyr. You will also learn regular expressions, date handling, and file import methods. These skills are important for real data analysis tasks in business and research.

By the end, you will be able to write simple R scripts, analyse datasets, and create clear data outputs. The course is suitable for beginners who want a strong base in data science using R programming.

Learning Outcomes

• Understand basic ideas of data science and R programming
• Use RStudio for coding and data analysis
• Work with vectors, matrices, and data frames
• Apply loops, functions, and conditions in R
• Clean and manage datasets using R tools
• Create simple plots and visual data outputs
• Use dplyr for data manipulation tasks

Who Is This Course for?

This course is for beginners who want to start learning R programming for data science and data analytics. It suits students, job seekers, and working professionals who want to build data skills. It is also useful for people moving into data-related roles such as analysis, reporting, or research work. No coding background is needed. The lessons are simple and focus on practical use of R in real data tasks.

Why Take This Course

R programming is widely used in data science and analytics roles. Many companies use R for data handling, reporting, and statistical work. This course gives you a clear base in R so you can handle data tasks with confidence. You will learn how to read, clean, and analyse data step by step. It also helps you understand how real datasets are processed in business and research settings. These skills are useful for entry-level data roles and can support your growth in the data field.

Who Should Take This R Programming for Data Science and Data Analytics Course?

This course is best for learners who want to start a career in data science or data analytics. It is useful for students studying IT, business, or maths. It also helps working professionals who want to shift into data-related roles. Business analysts, junior data analysts, and research assistants can also benefit. If you work with spreadsheets or reports and want to improve your data skills, this course will help you move into R programming with a structured approach.

Are There Any Entry Requirements?

No prior coding experience is required. Basic computer use and internet access are enough to start this course.

Certification

After finishing all units, learners will receive a digital certificate of completion. This certificate can support job applications and skill proof in data roles.

Topics Covered in This R Programming for Data Science and Data Analytics Course

• Introduction to Data Science
• Data Science: Career of the Future
• What is Data Science?
• Data Science as a Process
• Data Science Toolbox
• Data Science Process Explained
• What’s Next?
• Engine and coding environment
• Installing R and RStudio
• RStudio: A quick tour
• Arithmetic with R
• Variable assignment
• Basic data types in R
• Creating a vector
• Naming a vector
• Arithmetic calculations on vectors
• Vector selection
• Selection by comparison
• What’s a Matrix?
• Analyzing Matrices
• Naming a Matrix
• Adding columns and rows to a matrix
• Selection of matrix elements
• Arithmetic with matrices
• What’s a Factor?
• Categorical Variables and Factor Levels
• Summarizing a Factor
• Ordered Factors
• What’s a Data Frame?
• Creating Data Frames
• Selection of Data Frame elements
• Conditional selection
• Sorting a Data Frame
• Why would you need lists?
• Creating a List
• Selecting elements from a list
• Adding more data to the list
• Equality
• Greater and Less Than
• Compare Vectors
• Compare Matrices
• AND, OR, NOT Operators
• Logical operators with vectors and matrices
• Reverse the result: (!)
• IF statement
• IF…ELSE
• ELSEIF statement
• Write a While loop
• Looping with more conditions
• Break: stop the While Loop
• What’s a For loop?
• Loop over a vector
• Loop over a list
• Loop over a matrix
• What is a Function?
• Writing own functions
• Installing R Packages
• Loading R Packages
• What is lapply and when is used?
• Use sapply
• What is vapply and why is it used?
• Mathematical functions
• grepl & grep
• sub & gsub
• Today and Now
• Create and format dates
• Calculations with Dates
• Get and set current directory
• Loading Excel files
• Base plotting system
• Scatterplots
• Boxplot
• Introduction to dplyr package
• select(), mutate(), filter(), arrange()
• summarise() and across()
• COVID-19 Analysis Task

Career Path

• Data Analyst
• Junior Data Scientist
• Business Data Analyst
• R Programmer
• Reporting Analyst
• Research Data Assistant

Disclaimer

This is a self-paced, online training course with pre-recorded modules. No live sessions are included. Learners will receive access to the course materials within 24–48 hours of purchase.