Glossary

Common Mistake

The error that every junior data scientist makes at least once. We name it so you can avoid it.

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Intermediate Data Science — How to Use This Book

Intermediate Data Science — Chapter 1: From Analysis to Prediction

Intermediate Data Science — Chapter 2: The Machine Learning Workflow

Intermediate Data Science — Chapter 3: Experimental Design and A/B Testing

Intermediate Data Science — Chapter 4: The Math Behind ML — Probability, Linear Algebra, Calculus, and Loss Functions

Intermediate Data Science — Case Study 1: StreamFlow Feature Extraction Pipeline — From Schema to Model-Ready Table

Intermediate Data Science — Chapter 5: SQL for Data Scientists — Window Functions, CTEs, and Query Optimization

Intermediate Data Science — Chapter 6: Feature Engineering

Intermediate Data Science — Chapter 8: Missing Data Strategies

Intermediate Data Science — Case Study 1: StreamFlow Churn --- Building the Logistic Regression Baseline

Intermediate Data Science — Chapter 11: Linear Models Revisited

Intermediate Data Science — Chapter 14: Gradient Boosting

Intermediate Data Science — Case Study 2: KNN at TurbineTech --- Limitations and When It Shines

Intermediate Data Science — Chapter 15: Naive Bayes and Nearest Neighbors

Intermediate Data Science — Chapter 25: Time Series Analysis and Forecasting

Intermediate Data Science — Chapter 26: NLP Fundamentals

Intermediate Data Science — Chapter 27: Working with Geospatial Data

Intermediate Data Science — Chapter 28: Working with Large Datasets

Intermediate Data Science — Chapter 29: Software Engineering for Data Scientists

Intermediate Data Science — Chapter 34: The Business of Data Science

Related Terms