This five-days comprehensive R course takes you from foundational skills to advanced statistical modelling.

This hands‑on course will get you started with fundamentals of R, work confidently with the tidyverse, explore introductory statistics, and build models for continuous, binary, and time‑to‑event outcomes.

You will learn how to analyse continuous outcomes, perform logistic regression, and carry out survival analysis.

Whether you are new to R or looking to strengthen your analytical toolkit, this course offers a structured, supportive pathway to developing robust data skills.

 Course Tutor: Krupa Shukla

This course has been approved by the Royal College of Physicians for 25 CPD credits.

Early Bird rate is available for up to 2 weeks before the session runs.

For group/block bookings, please contact stathelp@imperial.ac.uk.

 

Fees and dates for 2025-2026

Course Fees

Students (Internal/External) : £900 (Early-bird fees: £800)

Imperial College Staff, Academics and Charity: £1500 (Early-bird fees: £1300)

External Fees : £2000 (Early-bird fees: £1800)

Click here for Cancellation Policy

Dates

  • Mon 17 to Fri 21 Aug 2026 - ONLINE

Please CLICK HERE to be redirected to the registration forms and booking links.

Time and Location

The course will be ONLINE ONLY via MS Teams from (10:00am to 4:30pm)

Class joining details will be sent a week prior to the course date.

Course Content

Day 1

  • Getting started with R
  • Dataframes and different types of data
  • Working with data
  • Working with Dataframes
  • R Workspace and R History

Day 2

  • Programming in R
  • Working with R Environment
  • Using "tidyverse" package for summarising
  • Recode, transform and compute variables
  • Summary measures and Descriptives

Day 3

  • Estimation and hypothesis testing
  • One sample tests: t-test, signed rank test
  • Two sample tests: t-test, Mann-Whitney test
  • Chi-square Test for Independence of two categorical variable
  • One-way ANOVA
  • Two-way ANOVA

Day 4

  • Simple linear regression
  • Multiple Linear regression
    • Selecting a Model – Stepwise selection and regression diagnostics
  • Analysis of CoVariance
  • Logistic Regression
    • Odds Ratio
    • Likelihood ratio test
    • Hosmer-Lemeshow Test

Day 5

  • Logistic Regression – ROC Curve for ordinal variable
  • Survival Analysis
    • Life tables
    • Kaplan Meier Survival graph
    • Log-rank test and Tarone-Ware test
    • Cox proportional hazards regression
    • Diagnostics for proportional hazards