Business Analytics iCademy: Tidying Data in R

This skill starts with a discussion on the importance of Data Quality in business analytics. It then transitions to a focus on data transformations using Tidyverse, a group of useful R packages. Data quality is an important concern and when organizations do not invest in creating perfect data, they suffer from data debt. Thus, organizations must invest in creating quality data to maximize return on investment (ROI) on their data investments. To develop appreciation for data transformation, we discuss the relationship between managerial decision, analysis, and data transformation. In this skill, you will be introduced to useful R packages such as dplyr, tidyr, and stringr for the following data manipulation tasks: - Sub-setting Data - Creating new features - Changing data format: wide to long and long to wide - Handling missing values - Summarizing data by groups - Manipulating strings Learning Outcomes - Understand why and how Data Quality affects Business Analytics - Appreciate the relationship between managerial decisions, analysis, and data transformation - Perform basic data manipulation tasks - Perform basic functions of dplyr, tidyr and stringr package for data transformation