1. Overview
2. Key Features
3. Repository Structure
4. Getting Started
5. Phase Details
6. Project Workflow
7. Contributing
8. License

The Human Resources Data Dynamos project delivers an end-to-end analytics pipeline for HR data, transforming raw employee information into data-driven insights useful for decision-makers. This repository includes everything from SQL scripts and Python notebooks to BI dashboards and strategic reports.

• Modular Phases: Organized into 13 clear, sequential phases covering data collection through reporting.
• Reproducibility: Environment defined in requirements.txt, with setup scripts and instructions.
• Multi-Tool Stack: Utilizes SQL, Python (pandas, scikit-learn), Jupyter notebooks, Tableau, Power BI, and standard office docs.
• Deliverables: Cleaned datasets, visualizations, predictive models, and formal presentations.

├── assets                     # Branding: team logo, images
├── DataSet                    # Raw HR data (Excel, CSV, archives)
├── Instructions               # Guides, proposals, PDFs, member info
├── Project-Operations         # Core analysis pipeline
│   ├── 01.Data-Collection     # Raw CSV exports of source tables
│   ├── 02.Data-Wrangling      # Transformation specs and scripts
│   ├── 03.Data-Cleaning       # Cleaned data snapshots + SQL scripts
│   ├── 04.Data-Exploration&Transformation  # EDA outputs & business questions
│   ├── 05.Data-Modeling       # ER diagrams and logical models
│   ├── 06.Data-Analysis       # Jupyter notebooks and detailed PDF report
│   ├── 07.Data-Forecasting    # Forecasting notebooks and code
│   ├── 08.Data-Visualization  # Tableau (.twbx) & Power BI dashboards
│   ├── 09.Data-Mining         # Clustering, association, and mining outputs
│   ├── 10.Data-Driven-Decision-Making # Strategic frameworks (SWOT, PESTEL, etc.)
│   ├── 11.Reporting           # Annual and management report drafts & finals
│   ├── 12.Application         # Proposal templates and Excel macros
│   └── 13.Presentation        # Stakeholder slide decks
├── LICENSE
├── README.md                  # This document
└── requirements.txt           # Python package dependencies

Follow these steps to replicate our environment and explore the analysis:

1. Clone the repo

   git clone https://github.com/0PeterAdel/Data-Dyanamos.git
   cd Data-Dyanamos

2. Create a virtual environment

   • Windows:

     python -m venv env
     env\Scripts\activate

   • Linux/macOS:

     python3 -m venv env
     source env/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Set up database (optional)

   • Load Project-Operations/01.Data-Collection/*.csv into your SQL engine.
   • Execute SQL scripts in 03.Data-Cleaning to create cleaned tables.

5. Run Notebooks

   • Launch Jupyter:

     jupyter lab

   • Navigate to Project-Operations/06.Data-Analysis and open analysis-part1.ipynb, analysis-part2.ipynb.

6. View Dashboards

   • Tableau: Open Project-Operations/08.Data-Visualization/Tableau/Data Dynamos Project (Data Forecasting).twbx.
   • Power BI: Open Project-Operations/08.Data-Visualization/Power-Bi/Data Dynamos Data Analysis.pbix.

Below are brief descriptions and key artifacts for each project phase:

• Objective: Gather raw HR tables.
• Files: Employee.csv, PerformanceRating.csv, etc.
• Outcome: Baseline CSV exports for import.

• Objective: Define transformations (e.g., normalizing codes).
• Artifacts: PDF with mapping rules, Python scripts.

• Objective: Remove duplicates, handle missing values, enforce types.
• Scripts: Employee.sql, PerformanceRating.sql.
• Snapshots: Cleaned CSVs and Excel files in Data-Cleaned/.

• Objective: Perform EDA to surface patterns.
• Deliverables: Business-Questions.pdf, KPI definitions.

• Objective: Design ER diagrams and logical data model.
• Files: Data-Modeling.png variants, HR-Data.xlsx model sheet.

• Objective: Answer core HR questions via notebooks.

• Notebooks:

  • analysis-part1.ipynb: Demographics & satisfaction analysis.
  • analysis-part2.ipynb: Turnover and performance insights.

• Report: Analysis-Report.pdf summarizing major findings.

• Objective: Build predictive models (e.g., satisfaction, turnover).
• Code: main.ipynb, main.py using scikit-learn.
• Report: Forecast-Report.pdf.

• Objective: Create interactive dashboards.
• Tableau: .twbx and PDF export.
• Power BI: .pbix, PDF, and PPTX templates.

• Objective: Uncover latent clusters and associations.
• Outputs: Excel dashboards, PDF & PPTX slides.

• Objective: Apply strategy frameworks.
• Frameworks: PESTEL, SWOT, SOAR, TOWS, VRIO.
• Artifacts: Each has paired PDF and Excel workbook.

• Objective: Consolidate insights into reports.
• Reports: Annual HR report, Management reports across functions.

• Objective: Provide templates for client proposals.
• Files: Proposal docs (.docx/.pdf), Excel-based macros.

• Objective: Stakeholder slide decks summarizing project.
• Formats: PPTX templates ready for customization.

1. Collect & Clean: Import raw CSVs → run SQL cleaning → export cleaned tables.
2. Explore: Run EDA notebooks → define business questions.
3. Model & Forecast: Build data models → train predictive models.
4. Visualize: Develop dashboards in Tableau/Power BI.
5. Report & Present: Compile insights into formal reports and slide decks.

1. Fork and create a branch: git checkout -b feature/XYZ
2. Commit: git commit -m "Add feature XYZ"
3. Push: git push origin feature/XYZ
4. Submit a Pull Request for review.

Please follow our coding standards and document any major changes in CHANGELOG.md (create one if needed).

This project is licensed under the Apache License 2.0. See LICENSE for details.