Welcome to the course!
1. Welcome to the course!
My name is Ben Teusch, and I'm an HR analytics consultant.2. Introduction to HR analytics
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.3. Tools for the course
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.4. A general process for HR analytics
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.5. Identify the groups to compare
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 performers, or departments with high turnover rates with the rest of the company.6. Calculate summary statistics about each group
Next, calculate summary statistics for the groups. Examples of summary statistics include the mean or median, such as average employee tenure; the maximum or minimum; or the sum, such as the total number of sick days taken.7. Compare the differences statistically or visually
Finally, plot or test the differences between those groups. For exploratory analysis, or simple questions, you can quickly communicate the differences between the groups with a graph. For many questions, you will also want to quantify how big the difference is, which you can do with statistical tests. In this course, you'll use all three steps of this process to analyze HR data in a business context.8. Course overview
The course is outlined as a series of miniature case studies, with one business problem per chapter. Each one of these case studies covers an issue with the potential to affect real business outcomes. For example, employee accidents can cripple a brand and bring costly penalties or legal fees, and hiring the right employees can reduce turnover and increase revenue.9. Let's practice!
Now, it's time to dive into the first case study.Create Your Free Account
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