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Mon 10 Feb, Mon 17 Feb, Mon 24 Feb 2020
10:00, ...

Venue: Clinical School, E-learning 1, 2, 3 (Level 2)

Provided by: Graduate School of Life Sciences


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Mon 9 Mar 2020


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Core Statistics
Beginners

Mon 10 Feb, Mon 17 Feb, Mon 24 Feb 2020

Description

This 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.

Target audience
  • The course is open to graduate students and postdocs from all departments and affiliated institutions within the GSLS
  • 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.
Prerequisites

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.

Sessions

Number of sessions: 6

# Date Time Venue Trainer
1 Mon 10 Feb   10:00 - 13:00 10:00 - 13:00 Clinical School, E-learning 1, 2, 3 (Level 2) map Dr Matt Castle
2 Mon 10 Feb   14:00 - 17:00 14:00 - 17:00 Clinical School, E-learning 1, 2, 3 (Level 2) map Dr Matt Castle
3 Mon 17 Feb   10:00 - 13:00 10:00 - 13:00 Clinical School, E-learning 1, 2, 3 (Level 2) map Dr Matt Castle
4 Mon 17 Feb   14:00 - 17:00 14:00 - 17:00 Clinical School, E-learning 1, 2, 3 (Level 2) map Dr Matt Castle
5 Mon 24 Feb   10:00 - 13:00 10:00 - 13:00 Clinical School, E-learning 1, 2, 3 (Level 2) map Dr Matt Castle
6 Mon 24 Feb   14:00 - 17:00 14:00 - 17:00 Clinical School, E-learning 1, 2, 3 (Level 2) map Dr Matt Castle
Objectives

Learning Objectives After this course you should be able to:

  1. Use R and RStudio or Python and Spyder to manipulate data, produce figures and perform exploratory data analyses
  2. Analyse datasets using standard statistical techniques
  3. Know when each test is and is not appropriate
Aims

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
Format

The course is primarily based around computer practicals interspersed with short lectures and presentations used to explain core ideas and principles.

Notes

The course is split over six 3 hour sessions all held in the eLearning Suite within the Clinical School. If you book onto this course you must attend all of the sessions as detailed below.

Duration

Six three hour sessions

Frequency

Several times per term

Themes

Booking / availability