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  • Book Overview & Buying SQL for Data Analytics
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SQL for Data Analytics

SQL for Data Analytics - Fourth Edition

By : Jun Shan, Benjamin Johnston, Haibin Li, Matt Goldwasser, Upom Malik
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SQL for Data Analytics

SQL for Data Analytics

By: Jun Shan, Benjamin Johnston, Haibin Li, Matt Goldwasser, Upom Malik

Overview of this book

SQL remains one of the most essential tools for modern data analysis and mastering it can set you apart in a competitive data landscape. This book helps you go beyond basic query writing to develop a deep, practical understanding of how SQL powers real-world decision-making. SQL for Data Analytics, Fourth Edition, is for anyone who wants to go beyond basic SQL syntax and confidently analyze real-world data. Whether you're trying to make sense of production data for the first time or upgrading your analytics toolkit, this book gives you the skills to turn data into actionable outcomes. You'll start by creating and managing structured databases before advancing to data retrieval, transformation, and summarization. From there, you’ll take on more complex tasks such as window functions, statistical operations, and analyzing geospatial, time-series, and text data. With hands-on exercises, case studies, and detailed guidance throughout, this book prepares you to apply SQL in everyday business contexts, whether you're cleaning data, building dashboards, or presenting findings to stakeholders. By the end, you'll have a powerful SQL toolkit that translates directly to the work analysts do every day. *Email sign-up and proof of purchase required
Table of Contents (21 chapters)
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1
Part 1: Data Management Systems
6
Part 2: Data Presentation and Manipulation
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12
Part 3: Advanced Topics on Analytics
19
Other Books You May Enjoy
20
Index

Data Presentation and Manipulation

This part of the book explores SQL techniques for extracting, transforming, and analyzing data. It begins with using SELECT expressions and filtering results to retrieve specific data based on conditions. You’ll learn how to update data, modify table structures, and apply both built-in and user-defined transformation functions. This part also covers joining tables, performing set operations, and aggregating data using GROUP BY and HAVING to uncover broader insights. Finally, it introduces window functions to analyze row positions and relationships within datasets for deeper, context-aware analysis.

This part of the book includes the following chapters:

  • Chapter 5, Presenting Data with SELECT
  • Chapter 6, Transforming and Updating Data
  • Chapter 7, Defining Datasets from Existing Datasets
  • Chapter 8, Aggregating Data with GROUP BY
  • Chapter 9, Inter-Row Operation with Window Functions
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Tech Concepts
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Programming languages
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SQL for Data Analytics
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