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Overview

Welcome to the AIC Holdings Data Science Team! This section will help you understand our workflows, tools, and methodologies.

Our Tech Stack

Core Tools

  • Python: Primary programming language for data analysis and modeling
  • Streamlit: Interactive applications and dashboards
  • Jupyter: Notebooks for exploration and prototyping
  • Git: Version control for code and documentation

Data Science Libraries

  • pandas: Data manipulation and analysis
  • numpy: Numerical computing
  • scikit-learn: Machine learning algorithms
  • matplotlib/seaborn: Data visualization

Deployment & Infrastructure

  • Docker: Containerization for reproducible environments
  • FastAPI: API development for model serving
  • PostgreSQL: Primary database for structured data

Team Workflows

1. Project Structure

All projects follow a standardized directory structure:

project-name/
├── data/           # Raw and processed datasets
├── notebooks/      # Jupyter notebooks for exploration
├── src/           # Source code and modules
├── tests/         # Unit and integration tests
├── docs/          # Project documentation
└── requirements.txt

2. Development Process

  1. Exploration: Start with Jupyter notebooks in notebooks/
  2. Development: Move stable code to modules in src/
  3. Testing: Write tests in tests/
  4. Documentation: Update docs as you go
  5. Deployment: Use Docker for consistent environments

3. Code Quality

  • Follow PEP 8 style guidelines
  • Write docstrings for all functions and classes
  • Use type hints where possible
  • Test your code before committing

Getting Help

  • Documentation: Check this wiki first
  • Team Chat: Ask questions in our team channel
  • Code Review: Use pull requests for feedback
  • Office Hours: Weekly team sync meetings

Ready to get started? Head to the Setup Guide next!