Table of contents

Big Data Analytics

A graduate path from data systems and statistical foundations to scalable modeling, inference, and production analytics

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

• Cover
• Copyright
• How to read this book
• Introduction
• Chapter 1: The Big Data Analytics Landscape
• Chapter 2: Mathematical and Statistical Foundations for Analytics
• Chapter 3: Data Types, Schemas, and Representation
• Chapter 4: Data Quality, Cleaning, and Trust
• Chapter 5: Storage Systems for Big Data
• Chapter 6: Distributed Computing Principles
• Chapter 7: Batch Processing with MapReduce and Spark
• Chapter 8: Streaming Analytics and Real-Time Data Systems
• Chapter 9: Query Processing and Analytical SQL at Scale
• Chapter 10: Exploratory Data Analysis for Massive Data
• Chapter 11: Feature Engineering and Representation Learning
• Chapter 12: Scalable Machine Learning
• Chapter 13: Advanced Analytics for Text, Graphs, and Sequences
• Chapter 14: Causal Analytics and Experimentation
• Chapter 15: Evaluation, Validation, and Model Risk
• Chapter 16: Privacy, Security, Ethics, and Governance
• Chapter 17: Production Analytics and MLOps
• Chapter 18: Decision Intelligence and Prescriptive Analytics
• Chapter 19: Architecture Patterns and Case Studies
• Chapter 20: Capstone: Designing an End-to-End Big Data Analytics System
• Conclusion