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