Additional information
by Jun Shan (Author), Haibin Li (Author), Matt Goldwasser (Author)
Level up from basic SQL to advanced, analytics-grade data analysis and use real PostgreSQL datasets, modern features, and practical business scenarios to turn raw data into clear, actionable insights.
Key Features:
- Solve real business problems with advanced SQL techniques
- Work with time-series, geospatial, and text data using PostgreSQL
- Build job-ready data analysis skills with hands-on SQL projects
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description:
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.
What You Will Learn:
- Write SQL Queries to explore and analyze structured data
- Use JOINs, subqueries, views, and CTEs to build analytics-ready datasets
- Apply window functions to identify trends, patterns, and cohort behavior
- Perform statistical analysis and hypothesis testing directly in SQL
- Analyze JSON, arrays, text, geospatial, and time-series data
- Improve SQL performance with indexing strategies and query plan optimization
- Load data with Python and automate analytics workflows
- Complete a full case study simulating a real-world data analysis project
Who this book is for:
This book is for aspiring and early-career data analysts, data engineers, backend developers, business analysts, and students who want to apply SQL to real-world data analytics. You should have basic SQL familiarity and college-level math knowledge, along with the desire to advance toward analytics-grade SQL, data transformation, pattern discovery, and business insight generation.
Table of Contents
- Introduction to Data Management Systems
- Creating Tables with Solid Structures
- Exchanging 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
