Employee Performance Analytics Hr
This is a solid end-to-end HR analytics pipeline that moves employee data through SQLite for KPI aggregation and into Python for visualization. You get department-level metrics like tasks per hour and absence rates, individual employee summaries with efficiency scores, and daily productivity tracking across teams. The SQL views are straightforward aggregations (AVG rating, SUM tasks, efficiency as tasks/hours), and the Python side uses pandas and matplotlib to generate charts like performance vs hours worked and efficiency distributions. It's practical if you're building HR dashboards from CSV exports and want a batteries-included analytics workflow rather than piecing together your own queries and plotting code.
- skills.sh installs
- 1.5K
- GitHub stars
- 2
- last updated
- Jul 14, 2026
Install
$ npx -y skills add aradotso/data-skills --skill employee-performance-analytics-hr --agent claude-codeInstalls into .claude/skills of the current project.
Files
Employee Performance Analytics HR Skill
Skill by ara.so — Data Skills collection.
Overview
Employee Performance Analytics is a Python and SQL-based HR analytics tool that transforms employee data into actionable insights. It uses SQLite for KPI aggregation and pandas/matplotlib for visualization, generating departmental performance reports, efficiency metrics, and workload analysis.
The project provides an end-to-end analytics pipeline: data loading → SQL feature engineering → Python analysis → visualization exports.
Installation
# Clone the repository
git clone https://github.com/AmirhosseinHonardoust/Employee-Performance-Analytics.git
cd Employee-Performance-Analytics
# Create virtual environment
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
Dependencies
pandas>=2.0.0
matplotlib>=3.7.0
seaborn>=0.12.0
sqlite3 # Built-in with Python
numpy>=1.24.0
Project Structure
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- Repository
- github.com/aradotso/data-skills
- Categories
- Data Science & ML, Debugging, Marketing & SEO
- First seen
- Jul 14, 2026
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