SQL for Data Analytics - Third Edition: Harness the power of SQL to extract insights from data

by Jun Shan, Matt Goldwasser, Upom Malik

Overview

Take your first steps to becoming a fully qualified data analyst by learning how to explore complex datasets

Key Features:

Master each concept through practical exercises and activities

Discover various statistical techniques to analyze your data

Implement everything you've learned on a real-world case study to uncover valuable insights

Book Description:

Every day, businesses operate around the clock, and a huge amount of data is generated at a rapid pace. This book helps you analyze this data and identify key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level.

SQL for Data Analytics, Third Edition is a great way to get started with data analysis, showing how to effectively sort and process information from raw data, even without any prior experience.

You will begin by learning how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you will learn how to write SQL queries to aggregate, calculate, and combine SQL data from sources outside of your current dataset. You will also discover how to work with advanced data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you will be able to understand your business at a deeper level. Finally, the book lets you in on the secret to getting information faster and more effectively by using advanced techniques like profiling and automation.

By the end of this book, you will be proficient in the efficient application of SQL techniques in everyday business scenarios and looking at data with the critical eye of analytics professional.

What You Will Learn:

Use SQL to clean, prepare, and combine different datasets

Aggregate basic statistics using GROUP BY clauses

Perform advanced statistical calculations using a WINDOW function

Import data into a database to combine with other tables

Export SQL query results into various sources

Analyze special data types in SQL, including geospatial, date/time, and JSON data

Optimize queries and automate tasks

Think about data problems and find answers using SQL

Who this book is for:

If you're a database engineer looking to transition into analytics or a backend engineer who wants to develop a deeper understanding of production data and gain practical SQL knowledge, you will find this book useful. This book is also ideal for data scientists or business analysts who want to improve their data analytics skills using SQL.

Basic familiarity with SQL (such as basic SELECT, WHERE, and GROUP BY clauses) as well as a good understanding of linear algebra, statistics, and PostgreSQL 14 are necessary to make the most of this SQL data analytics book.

Product Details

ISBN-13:	9781801812870
Publisher:	Packt Publishing
Publication date:	08/29/2022
Edition description:	3rd ed.
Pages:	540
Product dimensions:	7.50(w) x 9.25(h) x 1.09(d)

About the Author

Jun Shan is an expert information technology professional who has been designing and implementing data management systems for more than 20 years. He also teaches SQL and Relational Database at Columbia University in the City of New York and Saint Peter's University. He completed his Master of Science in Computer Science from Virginia Tech and is currently a solution architect in a top 3 cloud computing service provider.

Matt Goldwasser is the Head of Applied Data Science at the T. Rowe Price NYC Technology Development Center. Prior to his current role, Matt was a data science manager at OnDeck, and prior to that, he was an analyst at Millennium Management. Matt holds a bachelor of science in mechanical and aerospace engineering from Cornell University.

Upom Malik is a data science and analytics leader who has worked in the technology industry for over 8 years. He has a master's degree in chemical engineering from Cornell University and a bachelor's degree in biochemistry from Duke University. As a data scientist, Upom has overseen efforts across machine learning, experimentation, and analytics at various companies across the United States. He uses SQL and other tools to solve interesting challenges in finance, energy, and consumer technology. Outside of work, he likes to read, hike the trails of the Northeastern United States, and savor ramen bowls from around the world.

Table of Contents

• Understanding and Describing Data

• The Basics of SQL for Analytics

• SQL for Data Preparation

• Aggregate Functions for Data Analysis

• Window Functions for Data Analysis

• Importing and Exporting Data

• Analytics Using Complex Data Types

• Performant SQL

• Using SQL to Uncover the Truth – a Case Study