Centre for Applied Statistics Courses

We provide short courses in research methods and statistical analyses for anyone wanting to interpret or undertake their own research. Everyone is welcome to attend, including those based outside UCL.

Extensive notes are provided for all courses, and teaching is interspersed with practical examples and activities throughout courses to ensure that everyone has understood the key principles. We strive to create a relatively informal learning atmosphere, where participants are not afraid to ask questions or voice any confusion.

No prior statistical knowledge is assumed for our introductory courses (Introduction to Research Methods and Statistics / Introduction to Statistics in R), or our winter and summer schools, but all other courses assume that the basics are understood. Our live courses usually take place on weekdays during typical office-hours and selected evening dates may also be available for certain courses depending on timetable availability. We offer online self-paced versions of many of our courses too.

The courses running frequently at CASC are:

Winter / summer schools:

• Hybrid Summer School: Introduction to Statistics with R (07-11th July 2025)

Introductory statistics courses:

• Introduction to Research Methods and Statistics ✝§
• Introduction to Statistics in R §
• Critical Appraisal §

Theory-based courses:

• Introduction to Logistic Regression *^§
• Introduction to Poisson Regression *^
• Introduction to Survival/Time-to-Event Data Analysis *^
• Assessing Measurement Reliability and Validity *§
• Introduction to Bayesian Analysis *§
• Chi-square and Beyond for 2x2 Tables *
• Introduction to Latent Class Analysis *

Theory-based courses (with an optional software workshop):

• Introduction to Meta-Analysis *§ (with optional R workshop~ and Stata Workshop)
• Introduction to Dealing with Missing Data *§ (with optional R/SPSS/Stata workshop)
• Introduction to Regression Analysis *§ (with optional SPSS/R/Stata workshop)

Mixed courses with integrated statistical theory and software:

• Sample Size Estimation and Power Calculations with Excel *✝§
• Overview of Regressions with R *~^
• ANOVA/GLMs with SPSS *§

Statistics software courses:

• Introduction to SPSS *✝
• Introduction to R *✝§
• Further Topics in R *~ 
• Introduction to Stata *✝
• Introduction to tidyverse and ggplot2
• Introduction to Data Analysis using MATLAB
• Introduction to Machine Learning

Alternatively, please see the events calendar for a chronological list of statistics courses running at ICH (note: this calendar only shows the most imminent date for each course).

^{* Knowledge of basic statistical concepts is required/beneficial; we recommend the Introduction to Research Methods and Statistics course if you have little/no previous statistics experience; this course offers a good basis for all other CASC courses. 
~ requires prior knowledge/experience of the R software. 
^ We recommend attending the Introduction to Regression Analysis course first if you have no prior experience of regression analyses.} 
✝ ^{Evening dates often available for this course}
§ ^{Self-paced online materials available for purchase}