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Full Description
Turn SQL into your competitive edge for uncovering patterns and accelerating data-driven business decisions
Key Features
Solve real business problems with advanced SQL techniques
Work with time-series, geospatial, and text data using PostgreSQL
Build job-ready analytics skills with hands-on SQL projects
Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionSQL remains one of the most powerful tools in modern data analytics, helping you turn data into decisions. This book shows you how to go beyond writing queries to deliver insights that matter.
SQL for Data Analytics, Fourth Edition, is for anyone who wants to move past basic SQL syntax and use it to interpret real-world data with confidence. 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.What you will learn
Write queries to analyze and summarize structured data
Use JOINs, subqueries, views, and CTEs effectively
Apply window functions to identify 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 experience solving 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. You should have basic SQL and college-level math knowledge, and along with the desire to advance your skills in data transformation, pattern recognition, and business insight delivery.
Contents
Table of Contents
Introduction to Data Management Systems
Creating a Table with a 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 Arrays
Advanced Data Types: Date, Text, and Geospatial
Inferential Statistics Using SQL
A Case Study for Analytics Using SQL
