Human Resources Analytics: Predicting Employee Churn in R

Predict employee turnover and design retention strategies.
Start Course for Free
4 Hours14 Videos50 Exercises3,086 Learners
4000 XP

Create Your Free Account

GoogleLinkedInFacebook
or
By continuing you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.

Loved by learners at thousands of companies


Course Description

Organizational growth largely depends on staff retention. Losing employees frequently impacts the morale of the organization and hiring new employees is more expensive than retaining existing ones. Good news is that organizations can increase employee retention using data-driven intervention strategies. This course focuses on data acquisition from multiple HR sources, exploring and deriving new features, building and validating a logistic regression model, and finally, show how to calculate ROI for a potential retention strategy.

  1. 1

    Introduction

    Free
    This chapter begins with a general introduction to employee churn/turnover and reasons for turnover as shared by employees. You will learn how to calculate turnover rate and explore turnover rate across different dimensions. You will also identify talent segments for your analysis and bring together relevant data from multiple HR data sources to derive more useful insights.
    Play Chapter Now
  2. 2

    Feature Engineering

    In this chapter, you will create new variables from existing data to explain employee turnover. You will analyze compensation data and create compa-ratio to measure pay equity of all employees. To identify the most important variables influencing turnover, you will use the concept of Information Value (IV).
    Play Chapter Now
  3. 3

    Predicting Turnover

    In this chapter, you will build a logistic regression model to predict turnover by taking into account multicollinearity among variables.
    Play Chapter Now
  4. 4

    Model Validation, HR Interventions, and ROI

    In this chapter, you will calculate the accuracy of your model and categorize employees into specific risk buckets. You will then formulate an intervention strategy and calculate the ROI for this strategy.
    Play Chapter Now
Datasets
Employee data
Collaborators
Sumedh PanchadharSascha Mayr
Anurag Gupta Headshot

Anurag Gupta

People Analytics Practitioner
Anurag holds post graduate degree in Human Resources Management from XLRI, Jamshedpur. He has several years of experience in setting up People Analytics function for global MNCs and believes that for HR to get a seat at the C-suite table, they should incorporate data driven decisions across the employee lifecycle.
See More
Abhishek Trehan Headshot

Abhishek Trehan

People Analytics Practitioner
Abhishek is an SHRM Certified Professional, a CAP® (Certified Analytics Professional from INFORMS), and a Six Sigma Black Belt. Has worked and held various roles in fortune 500 companies including setting up HR Analytics and Digital HR COE. He has been a visiting faculty to Indian Institute of Management and is a speaker on HR Analytics.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA