What will this book teach you?
How to approach a data science problem from scratch – Learn to ask the right questions, define business goals, and determine the data needed.
Feature thinking made practical – Understand what features to engineer for each use case and why.
Designing the right ML solution – Learn which models to use, how to evaluate them, and how to interpret results.
End-to-end analytics frameworks – Go beyond ML with strong descriptive, diagnostic, and inferential techniques.
System design for data science – Learn how to productionize your ML work with pipelines, feedback loops, and model serving.
What’s inside the book?
1. Foundations (Sections 1–3):
Introduction to Data Science, Analytics, and Machine Learning
Real-world analogies and industry insights
Clear differentiation of when to use analytics vs. ML
2. Retail & eCommerce:
Churn Prediction, Demand Forecasting
Recommendation Systems, Customer Segmentation
Price Elasticity using A/B Testing
3. Finance & Fintech:
Credit Scoring, Fraud Detection
Portfolio Risk, CLTV Prediction
Smart Transaction Routing
4. Supply Chain & Operations:
Inventory &...