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Full Description
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 DescriptionSQL 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 forThis 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.
Contents
Table of Contents
Introduction to Data Management Systems
Creating Table with Solid Structure
Exchange Data using COPY
Manipulating Data with Python
Presenting Data with SELECT
Transforming and Updating Data
Defining Datasets from Existing Datasets
Aggregating Data with GROUP BY
Inter-row Operation with Window Functions
Performant SQL
Processing JSON and Array
Advanced Data Types: Date, Geospatial, and Text
Inferential Statistics using SQL
A Case Study for Analytics using SQL
