Analytics Learning Hub

🎯 Problem → Model Selection

Technique Matchmaker

Given a business scenario, pick the right analytics technique. Logistic regression? Clustering? Time series? Learn why each choice fits — or doesn't — for the problem at hand.

📝 8 scenarios

⏱ ~15 min

🏷 Selection

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🔍 Data Pipeline

Data Detective

Examine datasets and spot hidden traps before they poison your model — data leakage, class imbalance, missing value patterns, temporal contamination, wrong granularity, and more.

📝 7 cases

⏱ ~15 min

🏷 Data Quality

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🤖 All Stages

AI Does the Math. You Do the Thinking.

For every technique we cover, see exactly what AI can automate vs. what requires YOUR judgment. The case for why understanding methodology matters more than ever in the age of AI.

📝 9 techniques

⏱ ~10 min

🏷 Mindset

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🔬 Failure Diagnosis

The 87% Case Autopsy

87% of ML projects never reach production. Investigate six failed projects, examine the clues, and diagnose what went wrong — from wrong problem framing to model drift.

📝 6 cases

⏱ ~20 min

🏷 Lifecycle

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🚀 Deployment

Model Deployment Guide

How does each model actually get to production? Real-time APIs, batch scoring, Excel formulas, in-database, and edge deployment — with real industry examples for every technique.

📝 13 models

⏱ Reference

🏷 Deployment

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📊 Monitoring

ML Health Dashboard

You're the analytics team lead. Watch model metrics drift over 12 months, read the warning signs, and make the call: retrain, alert, roll back, or do nothing?

📝 4 scenarios

⏱ ~15 min

🏷 Monitoring

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🗺️ All Stages

Deployment Unifying Framework

The master reference. Every technique side-by-side — what it predicts, how it deploys, which metrics matter, and what business questions it answers. One view, all 16 models.

📝 16 models

⏱ Reference

🏷 Big Picture

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