Articles & Case Studies

The Ultimate Guide to A/B Testing Statistics: From Theory to Practice
Go beyond p-values. This deep dive covers everything from choosing the right statistical test for your metrics to calculating sample size with power analysis. A complete guide for data-driven professionals in e-commerce, marketing, and product.

Manual Predictions & Backpropagation in a 2–2–1 Neural Network (Sigmoid + MSE)
A full, step-by-step walkthrough that computes a 2–2–1 neural network by hand: forward pass, MSE loss, formal objective, dependency map, backpropagation derivations, and one complete weight update—ready-to-learn math with concrete numbers.

The Strategic Imperative of RAG: An Investment Framework for Unlocking Enterprise Knowledge
Generative AI is a transformative platform, but its true enterprise value is unlocked when grounded in proprietary data. This analyst report provides a strategic framework for investing in Retrieval-Augmented Generation (RAG), detailing the technology, a portfolio of high-ROI use cases, and a phased roadmap for implementation. RAG is not an experiment; it is a critical investment in building a durable competitive moat.

The Foundations of a Digital Mind: How Neural Networks Actually Work
From basic algebra to the calculus of learning, this in-depth guide demystifies neural networks. Using a simple, step-by-step example, we'll explore the core concepts of linear algebra, statistics, and backpropagation that allow machines to learn.

From Monolith to Modular: A Case Study in Migrating to a Modern Data Stack with dbt Cloud
Our most critical dashboard was failing, powered by a monolithic tangle of Airflow DAGs and raw SQL scripts. This is the in-depth story of how we broke down that monolith and migrated our legacy infrastructure to dbt Cloud, applying Kimball's dimensional modeling to build a platform that restored trust and accelerated the entire business.

The Secret Language of Data: From Random Chance to Causal Impact
Every data leader must move beyond surface-level metrics to the deeper concepts of randomness, hypothesis testing, and causality. This article breaks down the foundations of data science with simple analogies—helping you go from being data-driven to truly data-informed.