SQL for Data Analytics - Fourth Edition - Goldwasser, Matt

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Résumé :
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...

Biographie:
Jun Shan is a principal cloud solution advisor and data architect with 20+ years of professional experience. He has been working in the data management field since the beginning of his career and has delivered data solutions to various companies, such as Amazon and Bank of America. He also teaches about relational databases and SQL at several universities. Jun is the author of SQL for Data Analytics,Third Edition, and received his Master of Science in Computer Science from Virginia Tech....

Sommaire:
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 Description: SQL 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 for: This 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. 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...