Intro to Data Science with R

The R Bootcamp (therbootcamp.com)
Basel. 23/24 February 2019.

Course description

Data science is one of the most exciting fields around, and the R statistical language is one of the best ways to learn and practice it. In this packed weekend, you will learn how to do data science using R from the ground-up. You will start by learning the basics of the R language and the RStudio programming environment. You will then cover topics ranging from loading data from external files, to cleaning and organising data, to conducting basic statistical analyses and creating striking visualisations.

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. We will provide a limited number of spare laptops for those that cannot bring their own. 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.

Schedule

  Day 1 Day 2
9:00 Welcome Recap
9:30 Introduction
+Tutorial

Understanding the basics of R.
How to work with R Studio and R Projects?

Analysing
+Practical

How to calculate descriptive statistics?
How to aggregate data?

 
 
 
 
12:00 Lunch Lunch
 
13:00 Data
+Practical

Understanding data objects and functions in R.
How to read and write data?

Plotting
+Practical

How to create plots? (barplot, scatterplot, heatmaps etc.)
How to customize plots? (colors, multi-panel, style, etc.)

 
 
 
 
15:30 Wrangling
+Practical

How to reformat and join data?
How to slice and dice data? (renaming, sorting, filter, etc.)

Case studies

Apply and expand what you have learned in a prepared case study or your own data.

 
 
 
17:30 Next steps

How to continue your R journey.

18:00 Wrap-up Wrap-up & Apero