Guides¶

Welcome to the AIC Data Science team's guide collection. This section contains our core methodologies, standard operating procedures, and architectural frameworks.

What You'll Find Here¶

Our guides are organized into focused areas that reflect our data science practice:

🏗️ Architecture¶

Frameworks for evolving data science projects from experimental prototypes to enterprise-ready systems. Covers our three-phase approach: Experiment, Productionalize, and Platform Integration.

🤖 AI-SOPs¶

Standard operating procedures for AI-assisted development, documentation practices, and team collaboration workflows.

📊 Statistics¶

Statistical methods, comparative analysis techniques, and quantitative research methodologies for robust data science applications.

💼 Margin¶

Portfolio risk management frameworks, automated data processing, and comprehensive risk analysis for sophisticated trading strategies.

🔧 Launcher¶

Application management and deployment systems for our Streamlit-based data science tools.

📦 Product¶

Customer-focused product development frameworks and methodologies, including Amazon's Working Backwards PR/FAQ process for building products that customers actually want.

🛡️ Data Governance¶

Comprehensive framework for data principles, governance standards, and symbols table architecture to ensure data quality, security, and business value alignment.

Our Approach¶

These guides reflect our philosophy of progressive evolution - starting simple and adding complexity only when justified by business value and technical requirements. Each guide is designed to be:

Practical - Based on real implementation experience
Focused - Covering specific domains without overlap
Actionable - Providing clear frameworks and procedures
Maintainable - Updated as our practices evolve

Use the navigation menu to explore specific guide sections. Each area contains both overview information and detailed implementation guidance tailored to our team's workflows and standards.