Publisher's Synopsis
Turn SQL into your career's unfair advantage. Learn to uncover patterns, analyze real-world data, and make better business decisions, faster.
Key Features
- Solve real business problems with advanced SQL techniques
- Analyze time-series, geospatial, and text data in PostgreSQL
- Build job-ready analytics skills with hands-on SQL projects
- Purchase includes free PDF eBook with the print or Kindle edition
Book Description
SQL remains one of the most powerful tools in modern data analytics-and this book helps you use it not just to write queries, but to deliver insights. SQL for Data Analytics, Fourth Edition takes you beyond basic SQL syntax and teaches you how to analyze real-world data with confidence. Whether you're a beginner aiming to understand production data or a professional seeking to upgrade your analytics toolkit, this book gives you the skills to turn data into actionable outcomes. You'll begin by learning how to create and manage structured databases, before diving into data retrieval, transformation, and summarization. From there, you'll tackle more complex tasks: window functions, statistical operations, and analysis of geospatial, time-series, and text data. With hands-on exercises, case studies, and detailed guidance, this book prepares you to apply SQL in everyday business contexts-whether you're cleaning data, building dashboards, or presenting insights to stakeholders.What you will learn
- Write queries to analyze and summarize structured data
- Use JOINs, subqueries, views, and CTEs effectively
- Apply window functions to analyze patterns and trends
- Perform statistical analysis and hypothesis testing in SQL
- Analyze JSON, arrays, geospatial, and time-series data
- Improve SQL performance using indexes and query plans
- Load data with Python and automate analytics workflows
- Complete a case study to solve real-world analytics problems
Who this book is for
This book is for aspiring data engineers, backend developers, analysts, and students who want to use SQL for real-world data analytics. Readers should have basic SQL and college-level math knowledge and want to build skills in data transformation, pattern recognition, and business insight delivery.
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