Turn SQL into your career's unfair advantage. Learn to uncover patterns, analyze real-world data, and make better business decisions, faster.
Key Features
Solve real business problems with advanced SQL techniques
Analyze time-series, geospatial, and text data in PostgreSQL
Build job-ready analytics skills with hands-on SQL projects
Purchase includes free PDF eBook with the print or Kindle edition
Book DescriptionSQL remains one of the most powerful tools in modern data analytics-and this book helps you use it not just to write queries, but to deliver insights.
SQL for Data Analytics, Fourth Edition takes you beyond basic SQL syntax and teaches you how to analyze real-world data with confidence. Whether you're a beginner aiming to understand production data or a professional seeking to upgrade your analytics toolkit, this book gives you the skills to turn data into actionable outcomes.
You'll begin by learning how to create and manage structured databases, before diving into data retrieval, transformation, and summarization. From there, you'll tackle more complex tasks: window functions, statistical operations, and analysis of geospatial, time-series, and text data.
With hands-on exercises, case studies, and detailed guidance, this book prepares you to apply SQL in everyday business contexts-whether you're cleaning data, building dashboards, or presenting insights to stakeholders.What you will learn
Write queries to analyze and summarize structured data
Use JOINs, subqueries, views, and CTEs effectively
Apply window functions to analyze patterns and trends
Perform statistical analysis and hypothesis testing in SQL
Analyze JSON, arrays, geospatial, and time-series data
Improve SQL performance using indexes and query plans
Load data with Python and automate analytics workflows
Complete a case study to solve real-world analytics problems
Who this book is forThis book is for aspiring data engineers, backend developers, analysts, and students who want to use SQL for real-world data analytics. Readers should have basic SQL and college-level math knowledge and want to build skills in data transformation, pattern recognition, and business insight delivery.
Auflage
Sprache
Verlagsort
Editions-Typ
Maße
Höhe: 235 mm
Breite: 191 mm
ISBN-13
978-1-83664-625-9 (9781836646259)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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. Mr. Haibin Li obtained his Ph.D. in Atmospheric Science from Rutgers University. He is currently a lead predictive model with a decade of data science experience in the insurance industry. He has extensive working knowledge of data management and SQL. Mr. Li is the technical reviewer of the SQL for Data Analytics, 3rd Edition. Matt Goldwasser is the Vice President and Head of AI and Data Science for Global Distribution at T. Rowe Price, where he leads strategic initiatives leveraging machine learning and advanced analytics across the organization. With over 8 years at T. Rowe Price and a strong foundation in applied data science, MLOps, and cloud technologies like AWS, Matt brings a deep understanding of how to operationalize AI at scale.
Prior to this, Matt held multiple roles at OnDeck, where he led marketing analytics, developed predictive models, and built automated ML pipelines. His earlier experience spans data engineering, risk analysis, and product management roles at Millennium Management, GE, and the Port Authority of NY & NJ.
Matt's expertise lies in transforming complex problems into scalable solutions, with a passion for bridging strategy and technology. He is a recognized leader in the AI community, combining hands-on technical depth with a strong vision for impactful, data-driven innovation. 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. 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. 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 PhD in machine learning, specializing in image processing and deep convolutional neural networks. He has more than 10 years' experience in medical device design and development, working in a variety of technical roles and holds first-class honors bachelor's degrees in both engineering and medical science from the University of Sydney, Australia. 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 machine learning, 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 first-class honors bachelor's degrees in both engineering and medical science from the University of Sydney, Australia.
Table of Contents
Introduction to Data Management Systems
Creating Table with Solid Structure
Exchange Data using COPY
Manipulating Data with Python
Presenting Data with SELECT
Transforming and Updating Data
Defining Datasets from Existing Datasets
Aggregating Data with GROUP BY
Inter-row Operation with Window Functions
Performant SQL
Processing JSON and Array
Advanced Data Types: Date, Geospatial, and Text
Inferential Statistics using SQL
A Case Study for Analytics using SQL