This book is a single guide to SQL for Data Science and will teach aspiring Data Scientists how to construct datasets for exploration, analysis, and machine learning. The reader will learn how to approach query design and develop SQL code to extract insights from data while avoiding common pitfalls.

There has been a recent surge of people entering the field of Data Science from a wide variety of career and educational backgrounds such as business analytics, social science, physics, economics, and computer science. Many of them have conducted analyses using spreadsheets as data sources, but have never had to retrieve and engineer their own datasets from a relational database using SQL (Structured Query Language).

Rather than covering SQL broadly, this book will teach the subset of SQL skills that data analysts and data scientists use frequently, while providing practical advice and intuition on "how to think about constructing your dataset". Many SQL instructional resources teach syntax and functions, but are written for people in different job functions like database design and administration, therefore don't go into strategies and approaches for designing analytical datasets. After reading this book and practicing the techniques using the provided database and SQL code, you will gain an understanding of relational database structure, query design, and SQL syntax, and will be able to develop queries to construct datasets from their own databases for use in applications like interactive reports and machine learning algorithms.