HR-Analytics-PowerBI

This project is based on a DataCamp course, where I applied data cleaning, transformation, and visualization techniques using Power BI to analyze HR data. It reflects my ability to implement learned concepts and produce actionable insights. This is an HR Data Analytics portfolio project focusing on key metrics such as performance tracking and attrition rates. The project aims to assist Atlas Labs in understanding the factors driving employee turnover and developing strategies to improve retention.

Project Overview

The original dataset contained over 80K records. For the purpose of this analysis, the dataset was cleaned and processed, resulting in a final dataset of 76K records used in this Power BI project. Data cleaning and transformation were conducted using Microsoft Excel and Power BI's Query Editor, ensuring that only relevant and high-quality data were utilized.

Key Insights

• Age Distribution: Majority of employees are between 20-29 years old.
• Gender Ratio: Atlas Labs employs 2.7% more women than men.
• Diversity:
  • Non-binary employees make up 8.5% of the workforce.
  • White employees have the highest average salary, while mixed ethnic groups have one of the lowest.

Business Recommendations

• Retention Focus: Improve engagement/training programs for younger employees (20-29 years) to enhance their retention.
• Salary Fairness Review: Review salary structure to ensure fairness, especially among mixed ethnic groups.
• Diversity Initiatives: Enhance support for non-binary employees, who represent a significant proportion of the workforce.

Tools Used

• Data Cleaning & Preprocessing: Power BI Query Editor
• Visualization & Reporting: Microsoft Power BI

Data Visualizations

1. Entity Relationship Diagram: Shows the relationships between employee, performance, and satisfaction data.
2. Attrition Analysis Dashboard: Interactive visuals that provide insights into employee attrition based on different factors such as department, overtime, and job role.

Files in this Repository

• CSV Folder: Contains the cleaned datasets used for analysis.
• Power BI File Folder: Includes the Power BI dashboard file (.pbix).
• Visualization Folder: Screenshots of the Power BI visualizations and dashboards.

Challenges and Solutions

• Data Quality Issues: Handled missing values through data imputation and removed outliers to ensure accurate analysis.
• Data Integration: Combined data from multiple sources and created meaningful relationships between tables to support comprehensive analysis.