Data Analysis with R
Get comfortable importing, cleaning, and transforming datasets in R, building a strong practical foundation for every analysis you'll do afterward.
Course Overview
Most analysis fails before it starts, not because of the wrong statistical test, but because the data was never properly imported, cleaned, or structured. This course fixes that gap. You'll work in R and RStudio from day one, learning to bring in real datasets, spot and fix the messy parts, and reshape them into a form that's ready for analysis.
The course is built around the tidyverse, the modern standard for working with data in R. By the end, you'll be able to take a raw spreadsheet or export from data collection software and turn it into a clean, well-structured dataset, confidently and repeatably, using code you can read back and reuse.
This is the foundation course in our R track. It's designed to come before Statistical Analysis with R and Data Modeling with R, and gives you everything you need to move confidently into either.
Course at a Glance
What You'll Learn
A step-by-step path from a blank R script to a clean, analysis-ready dataset.
R & RStudio Fundamentals
Set up R and RStudio, understand projects and scripts, and get comfortable with R's core syntax and data types.
Importing Data
Bring in datasets from CSV, Excel, and other common formats using readr and readxl, and handle the quirks that come with real-world files.
Data Wrangling with dplyr
Filter, select, mutate, arrange, and summarise data using dplyr's grammar, the core toolkit for shaping data in R.
Tidying Data
Reshape wide and long data with tidyr, and develop a reliable approach to identifying and handling missing values.
Exploratory Data Analysis
Summarise your cleaned data, build frequency tables, and produce your first visualisations with ggplot2.
Capstone Project
Take a real, messy dataset from import to clean output, applying everything from the course in one guided project.
Who This Course Is For
Built for people who need to work with real data, not just learn the theory behind it.
Prerequisites
No prior programming experience is required. Comfort with basic computer use and everyday Excel is helpful, but everything specific to R is taught from the ground up.
Ready to build these skills?
Reach out to our team to ask questions about this course or express your interest. We'll notify you as soon as the next intake opens.