Data analysis from zero to hero
What you'll learn:
Develop a reliable data analysis framework
Refresh your statistical knowledge in a visual, intuitive way
Visualise your data with publication-ready figures
Become confident with testing general linear models: regression, ANOVA, etc.
Learn and apply the principles of hypothesis testing and model selection
Introduction to generalised linear models and to non-linear modelling
Requirements
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.
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.
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 Description() and package ggDescription2
· 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.
Homepage

https://www.udemy.com/course/first-steps-in-data-analysis-with-r/