An interactive platform for mastering product analytics with hands-on learning and real-world applications
🚀 Get Started • 📖 Documentation • 🤝 Contributing • 💡 Features • 📋 Installation Guide • 📚 Usage Guide
The Product Analytics Handbook is a comprehensive, interactive learning platform designed to help product managers, data analysts, and data scientists master the essential concepts and techniques of product analytics. Built with Streamlit, this application provides an engaging, hands-on approach to learning with practical examples, interactive demos, and real-world applications.
- 🎓 Learn by Doing: Interactive demos and exercises reinforce theoretical concepts
- 📈 Real-World Focus: Examples drawn from actual product analytics scenarios
- 🔬 Comprehensive Coverage: From fundamentals to advanced topics and specialized applications
- 💻 Open Source: Free to use, modify, and contribute
- 🚀 Modern Stack: Built with cutting-edge data science tools and libraries
- Probability: Master distributions, statistical inference, and probabilistic thinking
- Descriptive Statistics: Learn measures of central tendency, dispersion, and data summarization
- Data Visualization: Create compelling charts and graphs to communicate insights
- Data Quality & Validation: Ensure accuracy and reliability in your analyses
- KPIs & Metrics: Define and implement effective Key Performance Indicators
- Effective Communication: Present data insights to stakeholders with clarity
- Hypothesis Testing: Design and conduct rigorous statistical tests
- Time Series Analysis: Identify trends, patterns, and seasonality in temporal data
- A/B Testing: Optimize products through controlled experiments
- Survival Analysis: Analyze time-to-event data for customer lifecycle insights
- Churn Analysis: Identify and prevent customer attrition
- Marketing Analytics: Measure campaign effectiveness and ROI
- Fraud Risk Analysis: Detect and prevent fraudulent activities
- Supply Chain Optimization: Improve operational efficiency with data
- Economic Demand Forecasting: Predict future demand patterns
- Overview
- Features
- Quick Start
- Installation
- Usage
- Documentation
- Project Structure
- Technologies Used
- Contributing
- License
- Acknowledgments
Get up and running in 3 simple steps:
# 1. Clone the repository
git clone https://github.com/Analytical-Guide/Product-Analytical-Apps.git
cd Product-Analytical-Apps
# 2. Install dependencies
pip install -r streamlit_app/Product_Analytics/requirements.txt
# 3. Launch the application
streamlit run streamlit_app/Product_Analytics/main.pyThe application will open in your default browser at http://localhost:8501 🎉
- Python 3.8+ (Download)
- pip (Python package manager)
-
Clone the Repository
git clone https://github.com/Analytical-Guide/Product-Analytical-Apps.git cd Product-Analytical-Apps -
Create a Virtual Environment (Recommended)
# On macOS/Linux python3 -m venv venv source venv/bin/activate # On Windows python -m venv venv venv\Scripts\activate
-
Install Dependencies
pip install -r streamlit_app/Product_Analytics/requirements.txt
-
Verify Installation
streamlit --version
Start the Streamlit app:
streamlit run streamlit_app/Product_Analytics/main.py- Use the sidebar to navigate between different topics
- Start with Data Fundamentals for the best learning experience
- Each section includes:
- 📝 Theoretical concepts
- 💡 Interactive demos
- 🌍 Real-world applications
Recommended progression:
- Foundations → Probability, Statistics, Visualization
- Quality → Data Quality, Validation, Metrics
- Analysis → Hypothesis Testing, Time Series, A/B Testing
- Applications → Churn, Marketing, Fraud, Supply Chain
Comprehensive guides to help you get the most out of the Product Analytics Handbook:
- 📋 Installation Guide - Detailed setup instructions for all platforms
- 📖 Usage Guide - Learn how to navigate and use all features
- ❓ FAQ - Frequently asked questions and answers
- 🤝 Contributing Guide - Help improve this project
- 🔒 Security Policy - Report vulnerabilities and security best practices
- 📝 Changelog - Track all project changes and versions
- Need help installing? → Installation Guide
- First time user? → Usage Guide
- Have questions? → FAQ
- Want to contribute? → Contributing Guide
- Found a bug? → Open an Issue
Product-Analytical-Apps/
├── streamlit_app/
│ └── Product_Analytics/
│ ├── main.py # Main application entry point
│ ├── requirements.txt # Python dependencies
│ ├── pages/ # Individual topic pages
│ │ ├── 1 1_Probability_Fundementals.py
│ │ ├── 1 2_Descriptive_Statistics_Fundementals.py
│ │ ├── 1 3_Data_visualization_Fundementals.py
│ │ ├── 1 4_Hypothesis_testing_Fundementals.py
│ │ ├── 1 5_Data-Quality_Fundementals.py
│ │ ├── 1 6_Metrics_Fundementals.py
│ │ ├── 1 8_Practial_Data-validation.py
│ │ ├── 1 9_Effective-Communication.py
│ │ ├── 3_Churn-Analytics-Intro.py
│ │ ├── 3_Fraud-Risk-Analytics-Intro.py
│ │ ├── 3_Marketing-Analytics-Into.py
│ │ ├── 7_survival_Analytics-Intro.py
│ │ └── 9_Feedback.py
│ ├── features/ # Advanced feature modules
│ │ ├── 6_A-B_Testing.py
│ │ ├── Combinatorics.py
│ │ ├── Economic-Demand-Forcasting.py
│ │ ├── Economic_dashboard.py
│ │ ├── Supply-Chain.py
│ │ └── Time-Series.py
│ ├── utils/ # Utility functions
│ │ ├── data_utils.py
│ │ ├── stats_utils.py
│ │ ├── viz_utils.py
│ │ └── style_utils.py
│ └── data/ # Sample datasets
├── docs/ # Documentation
│ ├── FAQ.md # Frequently asked questions
│ ├── INSTALLATION.md # Installation guide
│ ├── USAGE.md # Usage guide
│ └── images/ # Screenshots and assets
├── .github/
│ ├── ISSUE_TEMPLATE/ # Issue templates
│ │ ├── bug_report.md
│ │ ├── feature_request.md
│ │ └── documentation.md
│ ├── PULL_REQUEST_TEMPLATE.md # PR template
│ └── workflows/ # CI/CD workflows
├── .gitignore # Git ignore rules
├── AUTHORS.md # Contributors list
├── CHANGELOG.md # Version history
├── CITATION.cff # Citation information
├── CODE_OF_CONDUCT.md # Community guidelines
├── CONTRIBUTING.md # Contribution guide
├── LICENSE # MIT License
├── README.md # This file
└── SECURITY.md # Security policy
This project leverages modern data science and web technologies:
| Category | Technologies |
|---|---|
| Frontend |  |
| Data Analysis |    |
| Visualization |    |
| Machine Learning |   |
| Specialized | Lifelines (Survival Analysis), pmdarima (Time Series), imbalanced-learn (ML), Folium (Maps) |
| Data Sources | FRED API, yfinance |
We welcome contributions from the community! Whether you're fixing bugs, adding features, or improving documentation, your help is appreciated.
-
Fork the Repository
Click the "Fork" button at the top right of this page.
-
Clone Your Fork
git clone https://github.com/YOUR_USERNAME/Product-Analytical-Apps.git cd Product-Analytical-Apps -
Create a Branch
git checkout -b feature/your-feature-name
-
Make Your Changes
- Follow the existing code style
- Add comments where necessary
- Update documentation if needed
-
Test Your Changes
streamlit run streamlit_app/Product_Analytics/main.py
-
Commit Your Changes
git add . git commit -m "Add: your descriptive commit message"
-
Push to Your Fork
git push origin feature/your-feature-name
-
Submit a Pull Request
Go to the original repository and click "New Pull Request"
- Code Quality: Write clean, readable, and well-documented code
- Testing: Test your changes thoroughly before submitting
- Documentation: Update relevant documentation
- Respect: Follow our Code of Conduct
- 📝 Add new analytical topics or case studies
- 🐛 Fix bugs or improve existing features
- 📚 Enhance documentation and examples
- 🎨 Improve UI/UX
- 🧪 Add unit tests
- 🌐 Add internationalization support
This project is licensed under the MIT License - see the LICENSE file for details.
MIT License
Copyright (c) 2024 Moshe Shamouilian
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction...
- Streamlit Team - For creating an amazing framework for data apps
- Open Source Community - For the incredible libraries that power this project
- Contributors - Everyone who has helped improve this project
- You - For using and learning from this handbook!
- 📧 Issues: GitHub Issues
- 💬 Discussions: GitHub Discussions
- ⭐ Star this repo if you find it helpful!
- Interactive quizzes and assessments
- More real-world case studies
- Video tutorials integration
- API for programmatic access
- Docker containerization
- Cloud deployment guides
- Multi-language support
- Mobile-responsive design enhancements
Made with ❤️ for the Product Analytics Community