SQL for Data Science: Data Cleaning, Wrangling and Analytics with Relational Databases

SQL from the Data Analyst’s Perspective

Most database textbooks treat SQL as a tool for managing data, but Antonio Badia’s SQL for Data Science puts the needs of the data analyst front and center. Instead of starting with abstract database theory, the book follows the stages of the data life cycle, showing how SQL supports the tasks that actually consume time in real analysis work: loading, cleaning, shaping, and exploring data.

Work Through the Data Life Cycle

The book’s structure mirrors the journey data takes from raw source to final insight. Early chapters introduce the data life cycle and different types of datasets, including structured, semistructured, and unstructured data. From there, you move into relational data and database schemas, learning how tables are organized and how to get data in and out of a database. This foundation makes later chapters feel practical rather than theoretical.

Cleaning and Shaping Data That Others Overlook

A major strength of this book is the sustained attention to data cleaning and pre-processing. Readers work through exploratory data analysis, univariate and multivariate summaries, missing data, outlier detection, duplicate removal, and attribute transformation. The goal is not just to run queries but to develop habits that produce reliable, analysis-ready datasets.

From Basic Queries to Advanced SQL

The text builds from simple SELECT statements to complex subqueries, window functions, set operations, and recursive queries. Along the way, it covers joins in depth and shows how to express domain knowledge directly in SQL. This progression gives readers a coherent picture of what SQL can do beyond simple retrieval.

Connecting SQL with R and Python

For readers already comfortable with R or Python, the book demonstrates how to use SQL from within those languages. It is not an introduction to R or Python itself, but it shows how database queries can fit into a broader analytical workflow, making it easier to apply the skills to data science projects.

Who Will Find This Book Useful

The author assumes very little background, requiring only basic computer fluency. That makes the book a good fit for students, self-learners, and professionals moving into data science or analytics. Open-source database systems such as MySQL and PostgreSQL are used throughout, so you can follow along hands-on without expensive software. Appendices offer installation guidance and a basic introduction to large-scale data.

If you want to build SQL skills that are directly relevant to data analysis, this title provides a clear, organized path from raw data to useful insights.