Book Image

SQL for Data Analytics. - Third Edition

By : Jun Shan, Matt Goldwasser, Upom Malik
Book Image

SQL for Data Analytics. - Third Edition

By: Jun Shan, Matt Goldwasser, Upom Malik

Overview of this book

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.

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SQL for Data Analytics. - Third Edition
Jun Shan | Matt Goldwasser | Upom Malik
Aug, 2022 | 540 pages
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Table of Contents (11 chapters)
Preface
About the Book
About the Authors
Audience
About the Chapters
Conventions
Setting up Your Environment
Installing Git
Loading the Sample Datasets – Windows
Loading the Sample Datasets – Linux
Loading the Sample Datasets – macOS
Running SQL files
Accessing the Code Files
1
1. Understanding and Describing Data
1. Understanding and Describing Data
Introduction
Data Analytics and Statistics
Types of Statistics
Working with Missing Data
Statistical Significance Testing
SQL and Analytics
Summary
Free Chapter
2
2. The Basics of SQL for Analytics
2. The Basics of SQL for Analytics
Introduction
The World of Data
Relational Databases and SQL
PostgreSQL Relational Database Management System (RDBMS)
Creating Tables
Basic Data Types of SQL
Data Structures: JSON and Arrays
Column Constraints
Updating Tables
SQL and Analytics
Summary
3
3. SQL for Data Preparation
3. SQL for Data Preparation
Introduction
Assembling Data
Cleaning Data
Transforming Data
Summary
4
4. Aggregate Functions for Data Analysis
4. Aggregate Functions for Data Analysis
Introduction
Aggregate Functions
Aggregate Functions with the GROUP BY Clause
Aggregate Functions with the HAVING Clause
Using Aggregates to Clean Data and Examine Data Quality
Summary
5
5. Window Functions for Data Analysis
5. Window Functions for Data Analysis
Introduction
Window Functions
Statistics with Window Functions
Window Frame
Summary
6
6. Importing and Exporting Data
6. Importing and Exporting Data
Introduction
The COPY Command
Using Python with your Database
Going Passwordless
Summary
7
7. Analytics Using Complex Data Types
7. Analytics Using Complex Data Types
Introduction
Date and Time Data types for Analysis
Performing Geospatial Analysis in PostgreSQL
Using Array Data types in PostgreSQL
Using JSON Data types in PostgreSQL
Text Analytics Using PostgreSQL
Summary
8
8. Performant SQL
8. Performant SQL
Introduction
The Importance of Highly Efficient SQL
Database Scanning Methods
Killing Queries
Functions and Triggers
Summary
9
9. Using SQL to Uncover the Truth: A Case Study
9. Using SQL to Uncover the Truth: A Case Study
Introduction
Case Study
Summary
Appendix
1. Understanding and Describing Data
2. The Basics of SQL for Analytics
3. SQL for Data Preparation
4. Aggregate Functions for Data Analysis
5. Window Functions for Data Analysis
6. Importing and Exporting Data
7. Analytics Using Complex Data Types
8. Performant SQL
9. Using SQL to Uncover the Truth: A Case Study

About the Chapters

Chapter 1, Understanding and Describing Data, helps you learn the basics of data analytics. You will learn how to form hypotheses and generate descriptive statistics that can provide insights into your data. You will achieve this goal by using mathematical and graphical techniques to analyze data with Excel.

Chapter 2, The Basics of SQL for Analytics, helps you learn the basics of SQL in the world of data and CRUD operations. You will learn how to use basic SQL to manipulate data in a relational database.

Chapter 3, SQL for Data Preparation, shows you how to clean and prepare data for analysis using SQL techniques. You will begin by learning how to combine multiple tables and queries into a dataset before moving on to more advanced materials.

Chapter 4, Aggregate Functions for Data Analysis, covers SQL's aggregate functions, which are powerful techniques for summarizing data. You will be able to apply these functions to generate descriptive statistics and learn how to aggregate data across all rows and break out subpopulations for further analysis.

Chapter 5, Window Functions for Data Analysis, covers SQL's window functions, which take order into account for a group of data. You will be able to apply these functions to gain new insights into data and gain important knowledge about the data, such as orders and ranks.

Chapter 6, Importing and Exporting Data, provides you with the skills required to interact with your database from other software tools (such as Python).

Chapter 7, Analytics Using Complex Data Types, gives you a rich understanding of the various data types available in SQL and shows you how to extract insights from datetime data, geospatial data, arrays, JSON, and text.

Chapter 8, Performant SQL, helps you optimize your queries so that they run faster. In addition to learning how to analyze query performance, you will also learn how you can use additional SQL functionality, such as functions and triggers, to expand the default functionality.

Chapter 9, Using SQL to Uncover the Truth: A Case Study, reinforces your acquired skills to help you solve real-world problems outside of those described in this book. Using the scientific method and critical thinking, you will be able to analyze your data and convert it into actionable tasks and information.

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