SQL for Data Analytics

Turn SQL into your competitive edge for uncovering patterns and accelerating data-driven business decisionsKey FeaturesSolve real business problems with advanced SQL techniquesWork with time-series, geospatial, and text data using PostgreSQLBuild job-ready analytics skills with hands-on SQL projectsPurchase of the print or Kindle book includes a free PDF eBookBook 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 learnWrite queries to analyze and summarize structured dataUse JOINs, subqueries, views, and CTEs effectivelyApply window functions to identify patterns and trendsPerform statistical analysis and hypothesis testing in SQLAnalyze JSON, arrays, geospatial, and time-series dataImprove SQL performance using indexes and query plansLoad data with Python and automate analytics workflowsComplete a case study to experience solving real-world analytics problemsWho 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.]]>