Overview

From a tidyverse perspective, a data analysis problem is solved in a series of data-centric steps: data acquisition and representation (Import), data cleaning (Tidy), and an iterative sequence of data transformation (Transform), data modelling (Model) and data visualization (Visualize). The end result of the process is to communicate insights obtained from the data (Communicate). This class will take you through all the steps in the process and will teach you how to approach such problems in a systematic manner. You will learn how to design data analysis pipelines as well as how to implement data analysis pipelines in R. The class will also emphasize how elegant code leads to reproducible science.