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SQL for Data Analytics

You're reading from SQL for Data Analytics Analyze data effectively, uncover insights and master advanced SQL for real-world applications

Product type Paperback

Published in Nov 2025

Publisher Packt

ISBN-13 9781836646259

Length 336 pages

Edition 4th Edition

Languages

SQL

Tools

MySQL

Concepts

Data Analysis

Authors (5):

Jun Shan

Haibin Li

Matt Goldwasser

Upom Malik

Benjamin Johnston

+1 more

View More author details

Table of Contents (21) Chapters

Preface

Free Benefits with Your Book

1. Part 1: Data Management Systems

2. Introduction to Data Management Systems FREE CHAPTER

3. Creating Tables with Solid Structures

4. Exchanging Data Using COPY

5. Manipulating Data with Python

6. Part 2: Data Presentation and Manipulation

7. Presenting Data with SELECT

8. Transforming and Updating Data

9. Defining Datasets from Existing Datasets

10. Aggregating Data with GROUP BY

11. Inter-Row Operation with Window Functions

12. Part 3: Advanced Topics on Analytics

13. Performant SQL

14. Processing JSON and Arrays

15. Advanced Data Types: Date, Text, and Geospatial

16. Inferential Statistics Using SQL

17. A Case Study for Analytics Using SQL

18. Unlock Your Exclusive Benefits

Unlock this Book’s Free Benefits in 3 Easy Steps

19. Other Books You May Enjoy

20. Index

Activity 9

From the procurement department’s perspective, the most important measure is the rolling 30-day sales, which helps them determine the replenishing schedule. As such, they would like to calculate the rolling 30-day average for the daily total sales amount for the year 2024. They would also like to calculate which decile each date would be in compared to other days based on their daily 30-day rolling sales amount.

Hint: In order to calculate the rolling 30-day average for the daily total sales amount for the year 2024, you will need to calculate the total sales amount by day for all of the days in the year 2024, plus the 29 days in 2023 that precede 2024.

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83

Tech Concepts

36

Programming languages

73

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Authors (5)

Jun Shan

Jun Shan

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.

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Haibin Li

Haibin Li

Haibin Li obtained his Ph.D. in Atmospheric Science from Rutgers University. He is currently a lead predictive modeler with a decade of data science experience in the insurance industry. He has extensive working knowledge of data management and SQL. Hiabin is the technical reviewer of SQL for Data Analytics, Third Edition.

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Matt Goldwasser

Matt Goldwasser

Matt Goldwasser is Vice President and Head of AI and Data Science for Global Distribution at T. Rowe Price. He leads strategic initiatives using machine learning (ML) and advanced analytics across the organization. With over 8 years at T. Rowe Price, he brings expertise in applied data science, MLOps, and AWS, with a strong focus on operationalizing AI at scale. Previously, Matt held multiple roles at OnDeck, leading marketing analytics and building predictive models and automated ML pipelines. He also worked in data engineering, risk analysis, and product management at Millennium Management, GE, and the Port Authority of NY and NJ. He is known for turning complex challenges into scalable solutions and bridging strategy with hands-on innovation.

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Upom Malik

Upom Malik

Upom Malik is a data science and analytics leader who has worked in the technology industry for over eight years. He holds 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 throughout the United States. He uses SQL and other tools to solve complex challenges in finance, energy, and consumer technology. Outside of work, he enjoys reading, hiking the trails of the Northeastern United States, and savoring ramen bowls from around the world.

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Benjamin Johnston

Benjamin Johnston

Benjamin Johnston is a senior data scientist for one of the world's leading data-driven MedTech companies and is involved in the development of innovative digital solutions throughout the entire product development pathway, from problem definition to solution research and development, through to final deployment. He is currently completing his Ph.D. in ML, specializing in image processing and deep convolutional neural networks. He has more than 10 years of experience in medical device design and development, working in a variety of technical roles, and holds a first-class honors bachelor's degree in both engineering and medical science from the University of Sydney, Australia.

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