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Tue 13 Apr, Tue 20 Apr, Tue 27 Apr 2021
09:30 - 17:30

Venue: Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST in Summer, GMT in Winter)

Provided by: Bioinformatics


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Core Statistics (ONLINE LIVE TRAINING)

Tue 13 Apr, Tue 20 Apr, Tue 27 Apr 2021

Description

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:

  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.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Target audience
  • Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated 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 be aware that these courses are only free for registered University of Cambridge students. All other participants will be charged a registration fee in some form. Registration fees and further details regarding the charging policy are available here.
  • Further details regarding eligibility criteria are available here
Prerequisites

This course requires users to be familiar with either the R or Python languages. Attending an introductory course An Introduction to Solving Biological Problems with Python or Introduction to R for biologists is definitely advantageous if you do not have a working knowledge of either language already. If you are unable to book a place on the course of your choice language prior to the Core Stats sessions, please contact the Bioinfo Team.

Sessions

Number of sessions: 3

# Date Time Venue Trainers
1 Tue 13 Apr   09:30 - 17:30 09:30 - 17:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST in Summer, GMT in Winter) Dr Matt Castle,  N. Walker-Hale,  Anna Orteu
2 Tue 20 Apr   09:30 - 17:30 09:30 - 17:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST in Summer, GMT in Winter) Dr Matt Castle,  N. Walker-Hale,  Anna Orteu
3 Tue 27 Apr   09:30 - 17:30 09:30 - 17:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST in Summer, GMT in Winter) Dr Matt Castle,  N. Walker-Hale,  Anna Orteu
Objectives

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
Aims

After this course you should be able to:

  • Analyse datasets using standard statistical techniques
  • Know when each test is and is not appropriate
Format

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

System requirements

This course will require you to have an up to date installation of R and RStudio or Python and Spyder on your computer beforehand. Brief installation guides will be provided and support will be available from the tutors during the sessions.

Registration Fees
  • Free for registered University of Cambridge students
  • £ 50/day for all University of Cambridge staff, including postdocs, temporary visitors (students and researchers) and participants from Affiliated Institutions. Please note that these charges are recovered by us at the Institutional level
  • It remains the participant's responsibility to acquire prior approval from the relevant group leader, line manager or budget holder to attend the course. It is requested that people booking only do so with the agreement of the relevant party as costs will be charged back to your Lab Head or Group Supervisor.
  • £ 50/day for all other academic participants from external Institutions and charitable organizations. These charges must be paid at registration
  • £ 100/day for all Industry participants. These charges must be paid at registration
  • Further details regarding the charging policy are available here
Notes

The course is designed to allow participants to engage with the material either synchronously and asynchronously. If you are unable to attend either the live lecture component or the live practical support component of any session then you should still be able to access support asynchronously via the virtual help desk and view the recordings of the lecture material on the course V.L.E.

Duration

3

Frequency

Several times per term

Theme
Core skills

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