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.

• Cover
• Copyright
• How to read this book
• Introduction
• Chapter 1: What Big Data Analytics Is For
• Chapter 2: The Data Mindset
• Chapter 3: Types, Formats, and Sources of Data
• Chapter 4: Core Statistics for Analytics
• Chapter 5: Databases and SQL Foundations
• Chapter 6: Data Cleaning and Preparation
• Chapter 7: Exploratory Data Analysis
• Chapter 8: Data Visualization and Communication
• Chapter 9: Programming for Data Analytics
• Chapter 10: Why Big Data Needs Distributed Systems
• Chapter 11: Hadoop, Spark, and the Big Data Ecosystem
• Chapter 12: Working with Data Pipelines
• Chapter 13: Batch Analytics and Stream Analytics
• Chapter 14: Introduction to Machine Learning for Big Data
• Chapter 15: Common Analytics Models
• Chapter 16: Cloud Analytics Platforms
• Chapter 17: Data Governance, Privacy, and Ethics
• Chapter 18: From Analysis to Decision
• Chapter 19: Building a Complete Big Data Analytics Project
• Chapter 20: Becoming Confident in Big Data Analytics
• Conclusion