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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

Getting started with Python

Python is usually not installed on the database server. Instead, it runs within the Python runtime on your workstation or on an application server. When you use Python for data analytics in PostgreSQL, you need to install a specific library called psycopg2 on your workstation or application server. This library, when called from the Python runtime environment, will connect to the PostgreSQL server and handle traffic between your Python script and the database server. In its simplest form, once you connect to the PostgreSQL server using psycopg2, you can submit SQL to the database using Python scripts, in the same way that you would with psql.

While there are many ways to get access to Python, the Anaconda distribution of Python makes it particularly easy to obtain and install it along with other analytical tools, as it comes with many commonly used analytics packages preinstalled alongside a great package manager. For that reason, we will be using the...

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SQL for Data Analytics
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