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Table of contents
Big Data Analytics (Beginner)
A beginner-friendly path from data foundations to scalable analysis, machine learning, and real-world data systems
Read each section in order. Every title can be opened as a TheoryTrace document.
- Cover1
- Copyright2
- How to read this book3
- Introduction4
- Chapter 1: What Big Data Analytics Is For5
- Chapter 2: The Data Mindset6
- Chapter 3: Types, Formats, and Sources of Data7
- Chapter 4: Core Statistics for Analytics8
- Chapter 5: Databases and SQL Foundations9
- Chapter 6: Data Cleaning and Preparation10
- Chapter 7: Exploratory Data Analysis11
- Chapter 8: Data Visualization and Communication12
- Chapter 9: Programming for Data Analytics13
- Chapter 10: Why Big Data Needs Distributed Systems14
- Chapter 11: Hadoop, Spark, and the Big Data Ecosystem15
- Chapter 12: Working with Data Pipelines16
- Chapter 13: Batch Analytics and Stream Analytics17
- Chapter 14: Introduction to Machine Learning for Big Data18
- Chapter 15: Common Analytics Models19
- Chapter 16: Cloud Analytics Platforms20
- Chapter 17: Data Governance, Privacy, and Ethics21
- Chapter 18: From Analysis to Decision22
- Chapter 19: Building a Complete Big Data Analytics Project23
- Chapter 20: Becoming Confident in Big Data Analytics24
- Conclusion25