Statistics with R

The R Bootcamp (
Basel. 1/2 June 2019.

Course description

If you want to make robust, actionable data-driven decisions from data, you need to understand statistics. In this intensive bootcamp, you will learn the fundamental principles of statistical analysis and inference using the R statistical language in order to help you translate data into actionable analytics. Whether you are an experienced data analyst or completely new to the field of statistical inference, this course has something for you. You will start from the beginning, learning how to calculate summary statistics to gain immediate insights from data. You will then learn how to create, dissect, and implement regression models, including t-tests, correlation tests, and ANOVAs) to understand hidden relationships in your data. Advanced topics such as hierarchical regression, Bayesian methods, and non-parametric analyses will also be covered.

Each day will contain a series of short lectures and examples to introduce you to new topics. The bulk of each day will be dedicated to hands-on exercises to help you ‘learn by doing’. Dedicated time will be given for 1:1 feedback. All course materials, tutorials, examples, exercises, and solutions will be available online for you to view at any time during, and after the course.

Participants are requested to bring their own laptop with software installation rights. There are no strict knowledge prerequisites for this course. Prior experience with a programming language (e.g.; SAS, R, STATA), as well as an introductory course in statistics, is helpful but not necessary.

The course includes with a brief introduction to the R language to prepare participants for the content of the course. Prior experience with R, or any other programming language, as well as an introductory course in statistics, is helpful but not necessary.


  Day 1 Day 2
9:00 Welcome Recap
9:30 Intro to Statistics

Understanding the logic behind statistical inference.

Hierarchical models

Pros and Cons of hierarchical (mixed) models?
When and how to use them in R?

10:30 R for Statistics

Understanding the basics of working with R.

12:00 Lunch Lunch
13:00 Regression models

What is regression?
How to run and customize regression models?

Robust statistics

Understanding the assumptions of regression models?
Are there robust alternatives?

15:30 Regression applications

Implementing ANOVAs, t-tests, correlation tests as regression models.
Practical considerations in using regression?

New statistics

Statistics beyond p-values.
A primer on Bayesian statistics.

17:30 Next steps

How to continue the R journey?

18:00 Wrap-up Wrap-up & Apero