Core Statistics

BeginnersPrerequisites

PLEASE NOTE that this course will be taught live online, with demonstrators available to help you throughout if have any questions. All lecture components will be recorded and uploaded to the course Moodle 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 the live sessions.

This 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:

1. Use R or Python confidently for statistics and data analysis
2. Be able to analyse datasets using standard statistical techniques
3. 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.

• The course is open to graduate students and postdocs from all departments and affiliated institutions within the PSLS
• This course is included as part of several DTP programmes as well as other departmental training within the university (potentially under a different name) so participants who have attended statistics training elsewhere should check before applying.
• Please be aware that this course is only free for University of Cambridge students. Any University of Cambridge staff, including postdocs, wishing to attend will be charged a registration fee. A purchase order will need to be provided.

This course requires users to be familiar with either the R or Python languages. Attending an introductory course (or doing a bit of Googling) is definitely advantageous if you do not have a working knowledge of either language already.

Number of sessions: 6

Learning Objectives After this course you should be able to:

1. Analyse datasets using standard statistical techniques
2. Know when each test is and is not appropriate

During this course you will learn about:

• One and two sample hypothesis tests
• ANOVA
• Simple linear Regression
• ANCOVA
• Linear Models
• Model selection techniques
• Power Analyses

The course is primarily based around computer practicals interspersed with short lectures and presentations used to explain core ideas and principles. Participants must bring their own laptops to work on.

The course is deigned to be accessed both synchronously and asynchronously. If you are unable to attend either the live lecture component or the live practical support component of each session then you should still be able to access support asynchronously via the Slack help desk and view the recordings of the lecture material on the Cambridge Moodle V.L.E.

Six three hour sessions

Several times per term

• Mathematics & Statistics
• Research Skills & Techniques