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SQL for Data Analysis Advanced Techniques for Transforming Data into Insights

This book teaches data analysts advanced SQL techniques to prepare, analyze, and transform data from various sources into actionable insights.

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What it’s about

This book is for data analysts and scientists who want to move beyond basic SQL and master advanced techniques for data analysis. It covers the entire analytical workflow, from preparing and cleaning data to performing complex analyses like time series, cohort analysis, text analysis, anomaly detection, and experiment analysis. The author, Cathy Tanimura, draws on her 20 years of experience to provide practical, real-world examples and SQL code (primarily in PostgreSQL) to solve common data challenges. You'll learn how to profile data for quality, handle duplicates and nulls, shape data by pivoting and unpivoting, and construct complex datasets for BI tools and machine learning models. This book aims to be the resource that bridges the gap between introductory SQL texts and those for database developers, empowering analysts to turn raw data into valuable insights.

The through-line

Who it’s for
The reader is a data analyst, data scientist, or business analyst who knows basic SQL but wants to become more effective and efficient at their job. They want to move beyond simple data retrieval and perform complex data transformations and analyses to uncover deeper insights and have a greater impact in their organization.
The problem
They struggle to solve complex analytical problems with SQL. They spend too much time on data preparation ('data wrangling'), and their queries are often inefficient, messy, or don't produce the right results. They lack a structured approach to common analysis patterns like cohort or time series analysis in SQL. They feel frustrated and limited by their current SQL skills. They feel like they're hitting a wall and can't answer the harder questions asked of them. They may feel like 'imposters' or that they learned SQL in an ad-hoc way and are missing foundational advanced techniques.
The plan
  1. Understand the role of SQL in the full data analysis workflow.
  2. Master the techniques of data preparation: profiling, cleaning, and shaping data.
  3. Learn to apply SQL to specific, powerful analysis patterns: time series, cohort, text, anomaly detection, and experiment analysis.
  4. Learn techniques for building and managing complex, reusable data sets.
The payoff
The reader becomes a highly proficient data analyst who can confidently tackle complex problems using SQL. · They can efficiently prepare, transform, and analyze data, producing more accurate and impactful insights. · They become a more valuable partner to their organization, able to answer harder questions and drive better decision-making.

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Additional reading

  • The Data Warehouse Toolkit · Ralph Kimball and Margy Ross

    Referenced for its foundational concepts on star schema modeling, which makes row-store databases more suitable for analytic workloads.

  • How to Measure Anything · Douglas W. Hubbard

    Recommended in the conclusion for improving one's approach to both analysis and communicating findings.

  • Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing · Ron Kohavi, Diane Tang, and Ya Xu

    Recommended as a key resource for learning more about the specific challenges and methods of online A/B testing.

  • Learning Regular Expressions · Ben Forta

    Suggested as a resource for readers who want a more thorough introduction to regular expressions beyond what the book covers.

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