Bigdata Research Tools - Interactive Tutorial

Welcome to the hands-on tutorial for the Bigdata Research Tools library! This interactive Jupyter notebook demonstrates all key functionality with practical, working examples.

🚀 Quick Start

Prerequisites

• Python 3.9+
• Bigdata.com API credentials
• uv (recommended) or pip

Setup with uv (Recommended)

1. Navigate to tutorial directory:

   cd tutorial

2. Create virtual environment and install dependencies:

   uv venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   uv pip install -r requirements.txt
   uv pip install -e ../.

3. Set up authentication:

   # Create .env file with your credentials
   echo "BIGDATA_API_KEY=your_api_key_here" > .env
   echo "OPENAI_API_KEY=your_openai_api_key_here" >> .env   # Required to run the Advanced Workflows

4. Launch Jupyter:

   jupyter lab tutorial_notebook.ipynb

Alternative Setup with pip

python -m venv tutorial_env
source tutorial_env/bin/activate  # On Windows: tutorial_env\Scripts\activate
pip install -r requirements.txt
pip install -e ..
jupyter lab tutorial_notebook.ipynb

📚 What You'll Learn

The tutorial covers:

• 🔑 Authentication Setup - Connect to the Bigdata.com API
• 🔍 Basic Searches - Search by companies and custom queries
• 📊 Data Processing - Work with search results and DataFrames
• 💡 Best Practices - Optimize searches and handle results efficiently

🎯 Tutorial Structure

1. Setup and Authentication - Import libraries and connect to API
2. Search by Companies - Find documents mentioning specific companies
3. Custom Query Search - Build and execute advanced search queries
4. Next Steps - Explore advanced workflows and examples

⚠️ Important Notes

• Run cells in order - Each cell depends on previous ones
• API credentials required - Create a .env file with your Bigdata.com credentials
• Sample data - The tutorial uses real API calls with sample companies

🔗 Next Steps

After completing this tutorial:

1. Explore Advanced Workflows:

   • NarrativeMiner - Track narrative evolution
   • ThematicScreener - Analyze theme exposure
   • RiskAnalyzer - Assess risk factors

2. Run Complete Examples: Check out the ../examples/ directory

3. Build Custom Workflows: Use the Bigdata Cookbook for more examples

🆘 Troubleshooting

• Import errors? Make sure you installed the package: pip install -e ../.
• Authentication issues? Verify your .env file has correct credentials
• No results? Try different date ranges or search terms

📖 Documentation

For comprehensive documentation, see the USER_GUIDE.md in the parent directory.