R Tutorial: Human Resources Analytics: Exploring Employee Data in R | Intro
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My name is Ben Teusch, and I'm an HR analytics consultant.
In nearly every organization, employees create products, sell products, deliver services to customers, and generally make the business profitable. An organization's success depends on having the right people doing the right thing at the right time in the right way.
Even so, using data to analyze and optimize the people aspect of business operations is only recently beginning to become widespread. Using data about a company's workforce to create value is the thrust of what is called human resources analytics, people analytics, workforce analytics, or talent analytics. In this course, we'll refer to it as HR analytics. Put simply, HR analytics is a data-driven approach to managing people at work.
In this course, you'll learn how to use dplyr and other core parts of the tidyverse family of packages to perform HR analytics. The tidyverse makes it easy to get from data to insights to visualization, and to communicate what you've done to stakeholders and other analysts.
The data you'll use for this course is based on a dataset produced by IBM scientists that closely resembles actual HR data. Real employee data cannot usually be shared outside a company due to privacy and ethical concerns. We've made modifications and additions to the dataset for the purposes of this course.
Throughout the course, you will perform several analyses to answer questions about a company's workforce. Most of the analyses you will do in HR analytics can be tackled in three general steps.
First, identify the groups to compare. Many questions in HR analytics can be turned into a question about why one group is different than another group. You might compare high performers with low performe
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