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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 in...
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SQL for Data Analytics - Fourth Edition
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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 eBookBook 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&aposre 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&aposll start by creating and managing structured databases before advancing to data retrieval, transformation, and summarization. From there, you&aposll 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&aposre cleaning data, building dashboards, or presenting findings to stakeholders. By the end, you&aposll 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 projectWho 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
