Employee Performance Analytics (SQL + Python)

Analyze employee performance and departmental productivity using SQL (SQLite) for KPI aggregation and Python for analytics and visualization.
This project transforms HR data into actionable business insights, identifying high performers, efficiency trends, and departmental KPIs.

Overview

This project demonstrates how to:

• Use SQL for feature engineering and KPI calculation
• Use Python for data analysis and visualization
• Deliver data-driven HR insights in an end-to-end workflow

Objectives

• Compute department-level performance indicators
• Assess employee efficiency and attendance
• Visualize relationships between workload, tasks, and ratings
• Generate clean and ready-to-use summary reports

Project Structure

employee-performance-analytics/
├── README.md
├── requirements.txt
├── data/
│   └── employees.csv
├── src/
│   ├── create_db.py
│   ├── queries.sql
│   ├── analyze_performance.py
│   └── utils.py
└── outputs/
    ├── department_kpis.csv
    ├── performance_summary.csv
    └── charts/
        ├── avg_rating_by_department.png
        ├── performance_vs_hours.png
        └── task_completion_rate.png

Dataset Description

The dataset (employees.csv) is synthetic, generated with realistic departmental trends and biases.

SQL Logic: src/queries.sql

The SQL script creates views and extracts three analytical datasets:

1. department_kpis – Department-level KPIs:

   • Average rating
   • Tasks per department
   • Total hours
   • Absence rates

2. employee_summary – Individual performance summaries:

   • Total tasks, hours, projects, absences
   • Average ratings
   • Tasks per hour (efficiency)

3. daily_productivity – Day-wise workload and productivity data.

Visualizations

Average Rating by Department

Chart: outputs/charts/avg_rating_by_department.png

Insight:

• Clear variation between departments (Finance & Engineering higher, Support & Sales lower)
• Indicates which departments maintain strong consistency and performance culture.

Performance vs Hours Worked

Chart: outputs/charts/performance_vs_hours.png

Insight:

• Positive correlation: higher hours → more tasks (up to a plateau)
• Clusters show standard workloads; outliers can reveal inefficiency or exceptional performers.

Task Completion Rate Distribution

Chart: outputs/charts/task_completion_rate.png

Insight:

• Most employees average around 1 task/hour
• High performers exceed 1.4, low performers under 0.8
• Useful for spotting training needs or recognizing excellence.

Getting Started

Create Virtual Environment

python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate

Install Dependencies

pip install -r requirements.txt

Load Data into SQLite

python src/create_db.py --csv data/employees.csv --db hr.db

Run Analysis

python src/analyze_performance.py --db hr.db --sql src/queries.sql --outdir outputs

All CSV reports and charts will be saved in the outputs/ directory.

Key Insights

• Department-level performance gaps can reveal resource or leadership factors.
• Efficiency distribution (tasks/hour) identifies both low performers and power users.
• Workload-to-performance trends help balance effort vs productivity.
• Absence tracking adds HR alignment to the analysis.

Tools Used

Project Value

This project demonstrates:

• SQL + Python integration for analytics
• Data storytelling and visualization
• Human Resource analytics capability
• Clean, modular, and reproducible data science workflow