Learn how to solve many commonly faced problems when applying R for Data Science and become an even more important part of your company. This course will introduce you to the most important Data Science tools in R to effectively solve many real life problems.

Participant profile

You have experience with programming from your daily work with data or from your education and have a basic Mathematical understanding. Basic experience with programming in R will be helpful.

Content

1. Introduction Introduction to the RStudio IDE, the most popular IDE for R programming.
2. Rmarkdown Interactive reporting of results to colleagues and non-technicals with R Markdown. New knowledge about data is not worth much if you aren’t able to communicate the findings with your colleagues. R Markdown is a strong tool for reporting.
3. Data Import Importing data from flat files and databases. Data comes from a variety of sources, it is therefore important to know how to get data into R.
4. Data Manipulation Manipulating data in R with a focus on the dplyr package. The dplyr package contains a grammar of data manipulation, this will help you solve the most common data manipulation challenges.
5. Data Visualization Plotting data in R with a focus on the ggplot2 package. The ggplot2 package contain similar grammar for plotting in R. To be able to plot is essential when new knowledge about data is to be shared with colleagues.
6. HTMLWidgets HTML Widgets are useful for an interactive view of your data.
7. Supervised Learning Program a prediction model in R, also called supervised learning. Learn how to implement models in R through exercises, with a focus on the most popular Machine Learning models.
8. Unsupervised Learning Clustering Basics in R, also called unsupervised learning. Learn how to group similar observations together.