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Updated in [July 21st, 2023]

This course, Hands-on HR Analytics - Predict job offer drop out using R, is the third course in the series “People Analytics : Learn ~ Practice ~ Implement”. It is a project-based course that offers an end-to end statistical project, guiding students to develop and master practical skills to solve any HR business problem using Step-by-step approach called “Anatomy of a Statistical Model for HR Analytics”. This course is designed to help students build a portfolio of great HR Analytics projects and advance their HR analytics and data science career.

The course starts with a fundamental understanding of what is Recruitment process, Various Recruitment metrics, and how renege can actually impact the Business ROI. Students will learn to use a logistic regression machine learning technique to reduce an important HR issue “RENEGE”. They will also learn how to convert Renege business problem into a Statistical problem, discover and collect data, prepare and explore the data for meaningful insights using various methods such as Uni-variate and Bi-variate Analysis, hypothesis testing etc, apply appropriate machine learning technique to predict offer dropout, extract major findings and insights from the statistical solution and finally how the insights will help leaders make strategies and policies to reduce offer dropout.

The course is developed by a team of analytics professionals with in-depth knowledge and understanding of HR domain. It is geared specifically for people who want to learn employable skills in 2019. By the end of the course, students will be able to do an end-to-end Statistical project on Renege using logistic regression algorithm in R, master practical skills to solve an HR business problem, understand how Renege affect business in terms of money, apply feature engineering techniques to get in-depth knowledge hidden inside the data, understand model validation method to check whether the model which they used is giving the accurate result or not, and extract major findings and insights from the statistical solution.

Course Syllabus

Introduction

The Business Problem

Installation of R and R studio.

Data Collection

Data Preparartion

Model Building

Model Evaluation

Conclusion