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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.
- Cover1
- Copyright2
- How to read this book3
- Introduction4
- Chapter 1: The Big Data Analytics Landscape5
- Chapter 2: Mathematical and Statistical Foundations for Analytics6
- Chapter 3: Data Types, Schemas, and Representation7
- Chapter 4: Data Quality, Cleaning, and Trust8
- Chapter 5: Storage Systems for Big Data9
- Chapter 6: Distributed Computing Principles10
- Chapter 7: Batch Processing with MapReduce and Spark11
- Chapter 8: Streaming Analytics and Real-Time Data Systems12
- Chapter 9: Query Processing and Analytical SQL at Scale13
- Chapter 10: Exploratory Data Analysis for Massive Data14
- Chapter 11: Feature Engineering and Representation Learning15
- Chapter 12: Scalable Machine Learning16
- Chapter 13: Advanced Analytics for Text, Graphs, and Sequences17
- Chapter 14: Causal Analytics and Experimentation18
- Chapter 15: Evaluation, Validation, and Model Risk19
- Chapter 16: Privacy, Security, Ethics, and Governance20
- Chapter 17: Production Analytics and MLOps21
- Chapter 18: Decision Intelligence and Prescriptive Analytics22
- Chapter 19: Architecture Patterns and Case Studies23
- Chapter 20: Capstone: Designing an End-to-End Big Data Analytics System24
- Conclusion25