Table of contents

The Data Intelligence Path

From data fundamentals to data science, machine learning, and production data engineering

Read each section in order. Every title can be opened as a TheoryTrace document.

• Cover
• Copyright
• How to read this book
• Introduction
• Chapter 1: Thinking in Data
• Chapter 2: Data Types, Structures, and Measurement
• Chapter 3: Computing Foundations for Data Work
• Chapter 4: Programming for Data with Python
• Chapter 5: Data Wrangling and Exploratory Analysis
• Chapter 6: Databases and SQL
• Chapter 7: Probability for Data Science
• Chapter 8: Statistics and Inference
• Chapter 9: Linear Algebra and Optimization for Models
• Chapter 10: Data Visualization and Communication
• Chapter 11: The Data Science Workflow
• Chapter 12: Supervised Machine Learning
• Chapter 13: Feature Engineering and Model Improvement
• Chapter 14: Tree Ensembles and Practical Predictive Modeling
• Chapter 15: Unsupervised Learning and Representation
• Chapter 16: Time Series, Forecasting, and Sequential Data
• Chapter 17: Deep Learning Foundations
• Chapter 18: Natural Language, Documents, and Modern AI Data
• Chapter 19: Causal Thinking and Experimentation
• Chapter 20: Data Engineering Foundations
• Chapter 21: Data Warehouses, Data Lakes, and Lakehouses
• Chapter 22: Building Reliable Data Pipelines
• Chapter 23: Production Machine Learning and MLOps
• Chapter 24: Ethics, Privacy, Security, and Data Leadership
• Conclusion