All Courses for £49An R Programming Course teaches learners how to analyse, manipulate, and visualise data using the R language. Moreover, it’s ideal for beginners and professionals looking to build skills in statistics, coding, and data science applications.
Overview of R Programming Course
The R Programming Course introduces learners to one of the most powerful programming languages used in modern Data Science training. R has become the backbone of statistical computing and predictive analytics, empowering professionals across industries to handle data efficiently. According to the Data Science UK Market Report, the demand for R-skilled professionals has grown by more than 35% in recent years, reflecting the surge in data-driven decision-making worldwide.
This R Programming Course is designed for aspiring data scientists, analysts, researchers, and students who wish to develop a strong analytical mindset. Moreover, learners will gain expertise in building, manipulating, and visualising datasets using R and RStudio. The Data Science Course materials simplify every concept—covering vectors, matrices, loops, functions, and data cleaning—so learners progress smoothly from basic to advanced levels.
How Learners Will Be Benefitted from this Course:
- CPD Accredited Strengthens your CV and career profile.
- Employability Boost Showcases your technical skills.
- 24/7 Support Help available anytime during R Programming Training.
- Career Edge Enhances opportunities in data roles.
Description R Programming Course
The R Programming Course is essential for anyone aiming to master data analysis and interpretation. As industries increasingly rely on analytics for decision-making, this Data Science Course equips learners with the tools to manage, transform, and visualise data effectively using R. Moreover, the course builds a strong foundation in programming logic, helping learners understand how statistical models and data structures work together to produce actionable insights.
Throughout the R Programming Training, you’ll explore structured modules covering topics such as vectors, data frames, functions, loops, and data visualisation. In addition, learners will develop the ability to clean, manipulate, and analyse complex datasets with ease. Consequently, this Data Science Training prepares you to extract meaningful patterns, present findings confidently, and contribute to data-driven decisions across any organisation.
Learning Outcomes of R Programming Course:
After completing R Programming Course, you will:
- Understand the core structure and environment of R and RStudio.
- Moreover, create and manipulate data using vectors, matrices, and lists.
- Consequently, use conditional statements, loops, and functions to automate tasks.
- Furthermore, apply relational and logical operators to analyse datasets.
- Additionally, clean, prepare, and transform data for analysis using dplyr.
- Hence, generate visual representations of data through effective plotting in R.
- Finally, integrate skills gained from this Data Science Training to interpret and communicate analytical results confidently.
Why Choose Us?
- This course is accredited by the CPD Quality Standards.
- Moreover, you gain lifetime access to the entire collection of learning materials.
- Additionally, an online test with immediate results is included.
- Importantly, enrolling in the course comes with no additional costs.
- Furthermore, you can study and complete the course at your own pace.
- Finally, you can access the course materials on any internet-connected device, such as a computer, tablet, or mobile device.
Certificate of Achievement
Quality Licence Scheme Endorsed Certificate of Achievement
Upon successful completion of the course, you will be eligible to order a QLS Endorsed HardCopy Certificate titled ‘Diploma in R Programming for Data Science at QLS Level 5’, providing tangible proof of your newly acquired skills. These certificates are not just tokens; they can be a valuable addition to your CV, enhancing your employability and opening doors to a myriad of career opportunities in this field.
- £109 for addresses within the UK. Please note that delivery within the UK is free of charge.
Please Note: NextGen Learning is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses.
Endorsement
The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards.
Who Is This Course For?
The R Programming for Data Science Course is ideal for:
- Data Science beginners who want to start their analytical journey.
- Additionally, Business analysts aiming to enhance decision-making using data insights.
- Furthermore, Students and researchers seeking to strengthen their statistical analysis capabilities.
- Therefore, IT and software professionals looking to diversify their skills with data-driven programming.
- Consequently, Career changers wishing to transition into the rapidly growing Data Science Course domain.
Requirements
The Data Science Course course requires no prior degree or experience. Therefore, all you require is English proficiency, numeracy literacy, and a gadget with a stable internet connection. Consequently, you can learn and train for a prosperous career in the thriving and fast-growing industry, without any fuss.
Career Path of R Programming Course
By completing the R Programming Course, you can unlock a variety of career opportunities, including:
- Data Analyst
- Junior Data Scientist
- Business Intelligence Specialist
- Research Assistant
- Machine Learning Assistant
- Data Visualisation Expert
- Statistical Modeller
- Quantitative Analyst
FAQs About R Programming for Data Science
Both languages are beginner-friendly, but learners often consider R more specialised for data analysis, while Python covers a broader range of applications. However, with structured R Programming Training, learning R becomes straightforward.
R may seem more technical than Excel initially, yet it offers far greater flexibility for complex data tasks. Additionally, once mastered, R Programming simplifies analysis far beyond Excel’s capabilities.
R is easier than C++ since it’s designed for data analysis rather than low-level programming. Therefore, learners in Data Science Courses usually find R more intuitive and practical.
The best Data Science Course combines theory with real-world applications. Moreover, a CPD-accredited R Programming for Data Science Course from NextGen Learning is highly recommended for those aiming to build strong analytical and statistical foundations.
Order Your Certificate
To order CPD Quality Standard Certificate, we kindly invite you to visit the following link:
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 | ||
| Assignment | |||
| Assignment – R Programming for Data Science | 00:00:00 | ||
| Order Your Certificate | |||
| Order Your Certificate | 00:00:00 | ||
| Order Your QLS Certificate | |||
| Order Your QLS Certificate | 00:00:00 | ||
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