First steps in data analysis with R

First steps in data analysis with R. Data analysis from zero to hero.

What you’ll learn

  • Develop a reliable data analysis framework
  • Master general linear models: regression, ANOVA, etc.
  • Visualise your data with publication-ready figures
  • Refresh your statistical knowledge in a visual, intuitive way
  • Learn and apply the principles of hypothesis testing and model selection
  • Introduction to generalised linear models and to non-linear modelling


  • No programming experience needed. You’ll learn how to use R from absolute 0.
  • You should be familiar with statistical concepts covered in any introductory statistics course, such as: Normal distribution, model parameters, variance, standard deviation, standard error, F-test, p-value.

First steps in data analysis with R Course Description

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.

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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

Who this course is for:

  • Junior researchers moving their first steps into practical research in natural sciences, biology, medicine, etc.

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