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  • Book Overview & Buying SQL for Data Analytics
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SQL for Data Analytics

SQL for Data Analytics - Fourth Edition

By : Jun Shan, Benjamin Johnston, Haibin Li, Matt Goldwasser, Upom Malik
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SQL for Data Analytics

SQL for Data Analytics

By: Jun Shan, Benjamin Johnston, Haibin Li, Matt Goldwasser, Upom Malik

Overview of this book

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. *Email sign-up and proof of purchase required
Table of Contents (21 chapters)
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1
Part 1: Data Management Systems
6
Part 2: Data Presentation and Manipulation
12
Part 3: Advanced Topics on Analytics
19
Other Books You May Enjoy
20
Index

A Case Study for Analytics Using SQL

So far in this book, you have learned about the various features and functionalities offered by SQL. Now, if you take a step back and think about the fundamental question of “why” (i.e., why do you need these features and functionalities at all), you will see that it all stems from the need for data analysis.

Data originates in individual activities. Each activity creates its own data point and context. When the data of all the activities in a certain domain is collected and grouped together, statistical patterns emerge. As discussed from the very first chapter of this book, ancient Egyptian pharaohs collected census information to determine the size of the army they could recruit, and modern companies dive into sales data to find the most attractive products for promotion. All these involve the same process of gathering activity level data (the number of people in each household, or the quantity and amount of each sales transaction...

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SQL for Data Analytics
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