HR Analytics KPI Dashboard Using Power BI

Abstract

1. Introduction

This project leverages data analytics to gain insights into gender diversity and inclusion within the company. The Power BI dashboard and KPIs allow HR and management to track important metrics, identify issues, and make informed decisions to improve the representation and experience of all genders in the workforce.

2. Objective

This project leverages data analytics to gain insights into gender diversity and inclusion within the company. The Power BI dashboard and KPIs allow HR and management to track important metrics, identify issues, and make informed decisions to improve the representation and experience of all genders in the workforce.

This project demonstrates how modern tools can be applied to a real-world Data Science problem.

3. Key Features / Modules

• View and analyze gender diversity and inclusion data easily.
• Track age, regional, job diversity, turnover rate, promotions, and more.
• Gain insights into gender diversity across different company dimensions

4. Technology Stack

• Utilized Microsoft Power BI for data modeling and visualization.
• Incorporated Microsoft Excel for data analysis.

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• See the project README for exact requirements
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/DataVisualizationExpert/HR-Analytics-KPI-Dashboard-using-Power-BI.git
cd HR-Analytics-KPI-Dashboard-using-Power-BI

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by DataVisualizationExpert and published on GitHub under the GNU General Public License v2.0. Please follow the license terms and credit the original author when you use or modify this code.