R Programming for Data Science

Course overview

A practical, project-based R programming programme designed for learners who want to develop professional skills in data analysis and statistical computing. Students learn how to work with datasets in R, clean and transform data, perform exploratory and statistical analysis, create professional visualisations with ggplot2 and build reproducible analytical workflows. The programme combines R programming with practical statistics and real-world data science projects.

Who it's for: Aspiring data scientists, data analysts, researchers, statisticians, graduates, business professionals and professionals who want to develop practical data analysis skills using R.

What you will learn

• Understand the fundamentals of R programming for data science
• Work confidently with vectors, lists, data frames and other R data structures
• Import and export data from common file formats
• Clean and transform real-world datasets using R
• Perform exploratory data analysis
• Apply descriptive and inferential statistical techniques
• Create professional data visualisations using ggplot2
• Analyse relationships between variables
• Build and interpret statistical models
• Perform regression analysis using R
• Work with categorical and numerical data
• Create reproducible data analysis workflows
• Build professional reports using R Markdown
• Communicate analytical findings effectively
• Complete a portfolio-ready data science project using R

Curriculum

Tools you'll use

Projects you'll build

• Sales & Customer Behaviour Analysis
• Employee Performance & Workforce Analytics
• Customer Satisfaction Statistical Analysis
• Healthcare Data Analysis
• Financial & Economic Data Analysis
• End-to-End R Data Science Capstone Project

Prerequisites

• • Basic computer literacy
• • No previous R programming experience required
• • Basic mathematics is recommended
• • Basic understanding of statistics is helpful but not required

Frequently asked questions