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Basic statistics and data handling



This three day course is intended to open doors to applying statistics - whether directly increasing skills and personally undertaking analyses, or by expanding knowledge towards identifying collaborators.

The end goal is to drive confident engagement with data analysis and further training - increasing the quality and reliability of interpretation, and putting that interpretation and subsequent presentation into the hands of the researcher.

Each day of the course will deliver a mixture of lectures, workshops and hands-on practicals – and will focus on the following specific elements. 



Hugo Tavares, Sainsbury Laboratory

Sandra Cortijo, Sainsbury Laboratory

Aaron Lun, CRUK

Catalina Vallejos, Alan Turing Institute


Audience and Prerequisites

  • The course is aimed primarily at mid-career scientists – especially those whose formal education likely included statistics, but who have not perhaps put this into practice since.
  • Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals.


Syllabus, Tools and Resources

During this course you will learn about:

  • Planning your experiment and why good experimental design is critical
  • How to use spreadsheet programs (such as Excel) more effectively, and the limitations of such programs
  • Writing and executing basic data analysis workflows in R
  • Formulating and interpreting the result of a statistical test
  • Choosing the appropriate graphics to understand and present your data


Learning Objectives

After this course you should be able to:

  • Identify sources of variation and confounding factors in your experimental design
  • Assess the distribution of your data and choose the appropriate statistical test; recognising any limitations that may exist
  • Create a reproducible piece of R code to import, visualise and perform a statistical test on biological data
  • Know how to develop your data analysis skills after the course



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Course materials


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