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R Programming for Data Science
75 Students (9 Reviews)
Learn R programming for data science with statistical analysis, data visualisation, data cleaning, programming, and analytical techniques.
GET THIS COURSE AND 3000+ OTHERS FOR ONLY £49 PER YEAR. FIND OUT MORE
Overview
R Programming for Data Science is a comprehensive programme designed to provide learners with the knowledge and practical understanding required to analyse, manage, and visualise data using the R programming language. This course introduces the core principles of R programming, statistical computing, data manipulation, data visualisation, exploratory data analysis, programming fundamentals, and analytical workflows. Participants will gain valuable insight into transforming raw data into meaningful information while using widely adopted tools and techniques within the field of data science.
Successful data science relies on accurate analysis, logical problem-solving, and the ability to interpret complex datasets effectively. Throughout the programme, participants will examine topics such as R syntax, variables, functions, vectors, data frames, loops, conditional statements, statistical methods, data cleaning, data visualisation with charts and graphs, reporting, and reproducible analysis. These subjects provide a strong foundation for handling data efficiently while supporting evidence-based decision-making across different industries.
Professional expertise in R programming can create opportunities within technology companies, financial institutions, healthcare organisations, research centres, marketing agencies, government departments, educational institutions, and data-driven businesses. The knowledge acquired through this training may support progression towards careers in data science, business intelligence, statistical analysis, and research while increasing confidence in working with large datasets and analytical projects.
Learning Outcomes
- Understand R programming language fundamentals thoroughly
- Write efficient scripts for data analysis
- Manipulate datasets using R programming tools
- Apply statistical methods to business data
- Create informative charts and data visualisations
- Clean and prepare datasets for analysis
- Interpret analytical results with confidence
Why choose this course
- Affordable, Accessible, informative and well-structured study materials
- Earn a globally recognised accredited qualification
- Study in your own time, at your own pace, through any device
- Benefit from feedback through mock exams and multiple-choice assessments
- Excellent career advancement/career switch opportunities
- Get 24/7 help or advice from our email and live chat teams
Certification
Certificate of Achievement
After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.
Entry Requirements
No previous R programming experience is required for this course. Basic computer literacy, numeracy, and an interest in data analysis are recommended. Learners will establish a strong foundation in R programming through structured and engaging learning materials.
Who is this course for?
This course is ideal for:
- Aspiring data scientists building programming expertise
- Data analysts expanding statistical computing knowledge
- Researchers analysing complex datasets using R
- Business professionals improving analytical capabilities
- Beginners exploring data science and programming
Career Paths
This course can support progression into roles and opportunities such as:
- Data Analyst
- Junior Data Scientist
- Business Intelligence Analyst
- Statistical Analyst
- Research Data Analyst
- Data Visualisation Specialist
Course Curriculum
| Unit 01: Data Science Overview | |||
| Introduction to Data Science | 00:01:00 | ||
| Data Science: Career of the Future | 00:04:00 | ||
| What is Data Science? | 00:02:00 | ||
| Data Science as a Process | 00:02:00 | ||
| Data Science Toolbox | 00:03:00 | ||
| Data Science Process Explained | 00:05:00 | ||
| What’s Next? | 00:01:00 | ||
| Unit 02: R and RStudio | |||
| Engine and coding environment | 00:03:00 | ||
| Installing R and RStudio | 00:04:00 | ||
| RStudio: A quick tour | 00:04:00 | ||
| Unit 03: Introduction to Basics | |||
| Arithmetic with R | 00:03:00 | ||
| Variable assignment | 00:04:00 | ||
| Basic data types in R | 00:03:00 | ||
| Unit 04: Vectors | |||
| Creating a vector | 00:05:00 | ||
| Naming a vector | 00:04:00 | ||
| Arithmetic calculations on vectors | 00:07:00 | ||
| Vector selection | 00:06:00 | ||
| Selection by comparison | 00:04:00 | ||
| Unit 05: Matrices | |||
| What’s a Matrix? | 00:02:00 | ||
| Analyzing Matrices | 00:03:00 | ||
| Naming a Matrix | 00:05:00 | ||
| Adding columns and rows to a matrix | 00:06:00 | ||
| Selection of matrix elements | 00:03:00 | ||
| Arithmetic with matrices | 00:07:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 06: Factors | |||
| What’s a Factor? | 00:02:00 | ||
| Categorical Variables and Factor Levels | 00:04:00 | ||
| Summarizing a Factor | 00:01:00 | ||
| Ordered Factors | 00:05:00 | ||
| Unit 07: Data Frames | |||
| What’s a Data Frame? | 00:03:00 | ||
| Creating Data Frames | 00:20:00 | ||
| Selection of Data Frame elements | 00:03:00 | ||
| Conditional selection | 00:03:00 | ||
| Sorting a Data Frame | 00:03:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 08: Lists | |||
| Why would you need lists? | 00:01:00 | ||
| Creating a List | 00:06:00 | ||
| Selecting elements from a list | 00:03:00 | ||
| Adding more data to the list | 00:02:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 09: Relational Operators | |||
| Equality | 00:03:00 | ||
| Greater and Less Than | 00:03:00 | ||
| Compare Vectors | 00:03:00 | ||
| Compare Matrices | 00:02:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 10: Logical Operators | |||
| AND, OR, NOT Operators | 00:04:00 | ||
| Logical operators with vectors and matrices | 00:04:00 | ||
| Reverse the result: (!) | 00:01:00 | ||
| Relational and Logical Operators together | 00:06:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 11: Conditional Statements | |||
| The IF statement | 00:04:00 | ||
| IF…ELSE | 00:03:00 | ||
| The ELSEIF statement | 00:05:00 | ||
| Full Exercise | 00:03:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 12: Loops | |||
| Write a While loop | 00:04:00 | ||
| Looping with more conditions | 00:04:00 | ||
| Break: stop the While Loop | 00:04:00 | ||
| What’s a For loop? | 00:02:00 | ||
| Loop over a vector | 00:02:00 | ||
| Loop over a list | 00:03:00 | ||
| Loop over a matrix | 00:04:00 | ||
| For loop with conditionals | 00:01:00 | ||
| Using Next and Break with For loop | 00:03:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 13: Functions | |||
| What is a Function? | 00:02:00 | ||
| Arguments matching | 00:03:00 | ||
| Required and Optional Arguments | 00:03:00 | ||
| Nested functions | 00:02:00 | ||
| Writing own functions | 00:03:00 | ||
| Functions with no arguments | 00:02:00 | ||
| Defining default arguments in functions | 00:04:00 | ||
| Function scoping | 00:02:00 | ||
| Control flow in functions | 00:03:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 14: R Packages | |||
| Installing R Packages | 00:01:00 | ||
| Loading R Packages | 00:04:00 | ||
| Different ways to load a package | 00:02:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 15: The Apply Family - lapply | |||
| What is lapply and when is used? | 00:04:00 | ||
| Use lapply with user-defined functions | 00:03:00 | ||
| lapply and anonymous functions | 00:01:00 | ||
| Use lapply with additional arguments | 00:04:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 16: The apply Family – sapply & vapply | |||
| What is sapply? | 00:02:00 | ||
| How to use sapply | 00:02:00 | ||
| sapply with your own function | 00:02:00 | ||
| sapply with a function returning a vector | 00:02:00 | ||
| When can’t sapply simplify? | 00:02:00 | ||
| What is vapply and why is it used? | 00:04:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 17: Useful Functions | |||
| Mathematical functions | 00:05:00 | ||
| Data Utilities | 00:08:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 18: Regular Expressions | |||
| grepl & grep | 00:04:00 | ||
| Metacharacters | 00:05:00 | ||
| sub & gsub | 00:02:00 | ||
| More metacharacters | 00:04:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 19: Dates and Times | |||
| Today and Now | 00:02:00 | ||
| Create and format dates | 00:06:00 | ||
| Create and format times | 00:03:00 | ||
| Calculations with Dates | 00:03:00 | ||
| Calculations with Times | 00:07:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 20: Getting and Cleaning Data | |||
| Get and set current directory | 00:04:00 | ||
| Get data from the web | 00:04:00 | ||
| Loading flat files | 00:03:00 | ||
| Loading Excel files | 00:05:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 21: Plotting Data in R | |||
| Base plotting system | 00:03:00 | ||
| Base plots: Histograms | 00:03:00 | ||
| Base plots: Scatterplots | 00:05:00 | ||
| Base plots: Regression Line | 00:03:00 | ||
| Base plots: Boxplot | 00:03:00 | ||
| Unit 22: Data Manipulation with dplyr | |||
| Introduction to dplyr package | 00:04:00 | ||
| Using the pipe operator (%>%) | 00:02:00 | ||
| Columns component: select() | 00:05:00 | ||
| Columns component: rename() and rename_with() | 00:02:00 | ||
| Columns component: mutate() | 00:02:00 | ||
| Columns component: relocate() | 00:02:00 | ||
| Rows component: filter() | 00:01:00 | ||
| Rows component: slice() | 00:04:00 | ||
| Rows component: arrange() | 00:01:00 | ||
| Rows component: rowwise() | 00:02:00 | ||
| Grouping of rows: summarise() | 00:03:00 | ||
| Grouping of rows: across() | 00:02:00 | ||
| COVID-19 Analysis Task | 00:08:00 | ||
| Additional Materials | 00:00:00 | ||
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Frequently asked questions
Can’t find the anwser you’re looking for ? Reach out to customer support team.
This course introduces the principles of R programming and demonstrates how the language is used for statistical analysis, data visualisation, and data science.
Yes, the course is suitable for beginners and provides a structured introduction to R programming and data analysis.
You will study R programming, data manipulation, statistical analysis, data visualisation, scripting, data cleaning, reporting, and analytical techniques.
R offers powerful statistical capabilities, extensive data analysis libraries, and advanced visualisation tools, making it a popular choice for researchers and data professionals.
No, previous programming experience is not required. The course introduces R programming concepts from the beginner level.
The course covers R syntax, variables, functions, vectors, data frames, loops, statistical methods, charts, data cleaning, visualisation, and reporting.
Yes, the knowledge gained may support opportunities in data analysis, business intelligence, research, technology, finance, healthcare, and analytics.
For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime.
Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks.
We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase.
Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course.
If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.