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Wed 7 Dec - Fri 9 Dec 2016
09:30 - 17:30

Venue: Bioinformatics Training Room, Craik-Marshall Building

Provided by: Bioinformatics


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Basic statistics and data handling

Wed 7 Dec - Fri 9 Dec 2016


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 lecture, workshop and hands-on practice – and will focus on the following specific elements.

Day 1 focuses on basic approaches and the computer skills required to do downstream analysis. Covering: Basic skills for data manipulation in R. How to prepare your data effectively. Principles of experimental design and how this influences analysis.

On day 2, participants will explore the core concepts of statistics – so that they can begin to see how they can be applied to their own work, and to also help with better critical evaluation of the work of others. Covering: Basic statistics concepts and practice: power, variability, false discovery, t-test, effect size, simulations to understand what a p-value means.

On day 3 will use some practical statistics examples in R to introduce concepts in data presentation for publication. Covering: Some practical examples of statistics in R. Visualising and publishing your data.

Course materials are available here.

This event is sponsored by CRUK.

Target audience
  • 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
  • Further details regarding eligibility criteria are available here

Number of sessions: 3

# Date Time Venue Trainers
1 Wed 7 Dec 2016   09:30 - 17:30 09:30 - 17:30 Bioinformatics Training Room, Craik-Marshall Building map Dr Florian Markowetz,  Hugo Tavares,  Dr Sandra Cortijo,  Dr A. Lun
2 Thu 8 Dec 2016   09:30 - 17:30 09:30 - 17:30 Bioinformatics Training Room, Craik-Marshall Building map Catalina Vallejos,  Dr A. Lun
3 Fri 9 Dec 2016   09:30 - 17:30 09:30 - 17:30 Bioinformatics Training Room, Craik-Marshall Building map Catalina Vallejos,  Dr A. Lun,  Aiora Zabala
Topics covered

Bioinformatics, Data handling, Data visualisation, Statistical calculation


During this course you will learn about:

  • The importance of Reproducible Research and how tools such as R can help
  • 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

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

Presentations, demonstrations and practicals


Day 1 Topics Speaker(s)
9:30 – 10:30 Lecture: Experimental design Aaron Lun
10:30 – 12:30 Lecture/practical: Data organization and introduction to R Hugo Tavares, Sandra Cortijo
12:30 - 13:30 Lunch
13:30 – 14:30 Lecture: Reproducible research Florian Markowetz
14:30 – 17:30 Lecture/practical: Data manipulation in R Hugo Tavares, Sandra Cortijo
Day 2
9:30 – 10:30 Lecture: Introduction to Statistics + Descriptive analysis

Catalina Vallejos, Aaron Lun
10:30 – 11:00 Lecture: Descriptive analyses with R markdown

Catalina Vallejos, Aaron Lun
11:00 – 12:30 Lecture: Statistical inference (up to the definition of a p-value)

Catalina Vallejos, Aaron Lun
12:30 - 13:30 Lunch
13:30 – 17:30 Lecture/practical: Statistics in R: tests to compare 2 populations

Catalina Vallejos, Aaron Lun
Day 3
9:30 – 12:30 Lecture/practical: Statistics in R: linear regression

Catalina Vallejos, Aaron Lun
12:30 - 13:30 Lunch
13:30 – 16:30 Lecture/practical: Figure design

Aiora Zabala

3 days


Three times a year

Related courses
Basic Skills and Programming

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