
In attachment: a few examples of the figures that you will learn to produce as part of the data visualisation sections of this course.
Download all the scripts and datasets used throughout the course from here, or download them one by one from each lecture.
This course is aimed at those that already have a theoretical understanding of statistical concepts and want to learn the practical side of data analysis.
Learning how to analyse data can be a daunting test. Applying the statistical knowledge learned from books to real-world scenarios can be challenging, and it's often made harder by seemingly complicated data analysis softwares.
This course will help you to develop a reliable data analysis pipeline, creating a solid basis that will make it easy for you to further your data analysis skills throughout your career.
We will use R, a free, state-of-the-art software environment for modelling, data handling, data analysis, and data visualisation.
We will start from installing R and taking baby steps to become familiar with the R programming language. We will then learn how to load data in R, how to visualise them with publication-level quality graphs, and how to analyse them.
I will provide you with the scripts that I use throughout the course, so that you can easily use them and adapt them to your own research objectives.
We will learn R one small step at a time, starting from absolute zero:
· how to enter data in R
· how to visualise data using function plot() and package ggplot2
· how to fit, interpret, and evaluate general linear models for a variety of study designs, including t test, ANOVA, regression, ANCOVA, and multiple regression scenarios
· how to fit polynomial regression
· an introduction to user-defined non-linear models
· an introduction to generalised linear models for non-normally distributed data (case study: count data)
· optimal data organisation and "data wrangling" - merging, subsetting, and summarising data