Core Statistics (ONLINE LIVE TRAINING)
Organizer: University of Cambridge
Host institution: University of Cambridge Bioinformatics Training
Start: Monday, 08 February 2021 @ 14:00
End: Wednesday, 24 February 2021 @ 17:00
Venue: Craik-Marshall Building
Country: United Kingdom
Postcode: CB2 3ARTarget audience:
- Graduate students
- Postdocs and Staff members from the University of Cambridge
- Institutions and other external Institutions or individuals
- This course is included as part of several DTP and MPhil programmes
- as well as other departmental training within the University of Cambridge (potentially under a different name) so participants who have attended statistics training elsewhere should check before applying.
PLEASE NOTE that this course will be taught live online, with tutors available to help you throughout if have any questions. All resources and lectures will be recorded and uploaded to the course VLE page so that you will be able to access that information even if technical or time zone restrictions means that you aren't able to join us for all of the live sessions.
This award winning virtually delivered course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.
There are three core goals for this course:
Use R or Python confidently for statistics and data analysis
Be able to analyse datasets using standard statistical techniques
Know which tests are and are not appropriate
Both R and Python are free software environments that are suitable for statistical and data analysis.
In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory.
After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.
- Workshops and courses