Human Resources Analytics: Predicting Employee Churn in Python
In this course you'll learn how to apply machine learning in the HR domain.
- 4 hours
- 14 Videos
- 44 Exercises
- 4,171 Participants
- 3,500 XP
Create Your Free Account
By pressing Continue 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 top companies:
Course Description
Among all of the business domains, HR is still the least disrupted. However, the latest developments in data collection and analysis tools and technologies allow for data driven decision-making in all dimensions, including HR. This course will provide a solid basis for dealing with employee data and developing a predictive model to analyze employee turnover.
-
1
Introduction to HR Analytics
FreeIn this chapter you will learn about the problems addressed by HR analytics, as well as will explore a sample HR dataset that will further be analyzed. You will describe and visualize some of the key variables, transform and manipulate the dataset to make it ready for analytics.
-
Evaluating the turnover prediction model
Here, you will learn how to evaluate a model and understand how "good" it is. You will compare different trees to choose the best among them.
-
Predicting employee turnover
This chapter introduces one of the most popular classification techniques: the Decision Tree. You will use it to develop an algorithm that predicts employee turnover.
-
Choosing the best turnover prediction model
In this final chapter, you will learn how to use cross-validation to avoid overfitting the training data. You will also learn how to know which features are impactful, and which are negligible. Finally, you will use these newly acquired skills to build a better performing Decision Tree!
-
1
Introduction to HR Analytics
FreeIn this chapter you will learn about the problems addressed by HR analytics, as well as will explore a sample HR dataset that will further be analyzed. You will describe and visualize some of the key variables, transform and manipulate the dataset to make it ready for analytics.
-
Predicting employee turnover
This chapter introduces one of the most popular classification techniques: the Decision Tree. You will use it to develop an algorithm that predicts employee turnover.
-
Evaluating the turnover prediction model
Here, you will learn how to evaluate a model and understand how "good" it is. You will compare different trees to choose the best among them.
-
Choosing the best turnover prediction model
In this final chapter, you will learn how to use cross-validation to avoid overfitting the training data. You will also learn how to know which features are impactful, and which are negligible. Finally, you will use these newly acquired skills to build a better performing Decision Tree!
What do other learners have to say?
“I've used other sites, but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyd's 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
Hrant Davtyan
Assistant Professor of Data Science at the American University of Armenia
Hrant is an Assistant Professor of Data Science at the American University of Armenia and founder of METRIC research center. His courses are concentrated on Data collection, analysis, visualization and reporting using Python and R in all 4 domains of business: customers, people, operations and finance. Hrant also holds PhD in Economics. His research is related to applications of Machine Learning in Economics and Finance.
See MoreCollaborators
-
Lore Dirick
-
Nick Solomon