SQL for Data Analysis: Advanced Techniques for Transforming Data into Insights

Tanimura, Cathy

ISBN 10: 1492088781 / ISBN 13: 9781492088783

Published by O'Reilly Media, 2021

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Bibliographic Details

• Title: SQL for Data Analysis: Advanced Techniques for Transforming Data into Insights
• Publisher: O'Reilly Media
• Publication Date: 2021
• Language: English
• ISBN 10: 1492088781
• ISBN 13: 9781492088783
• Binding: Paperback
• Condition: new

About this title

Synopsis

With the explosion of data, computing power, and cloud data warehouses, SQL has become an even more indispensable tool for the savvy analyst or data scientist. This practical book reveals new and hidden ways to improve your SQL skills, solve problems, and make the most of SQL as part of your workflow.

You'll learn how to use both common and exotic SQL functions such as joins, window functions, subqueries, and regular expressions in new, innovative ways--as well as how to combine SQL techniques to accomplish your goals faster, with understandable code. If you work with SQL databases, this is a must-have reference.

• Learn the key steps for preparing your data for analysis
• Perform time series analysis using SQL's date and time manipulations
• Use cohort analysis to investigate how groups change over time
• Use SQL's powerful functions and operators for text analysis
• Detect outliers in your data and replace them with alternate values
• Establish causality using experiment analysis, also known as A/B testing

About the Author

Cathy Tanimura has a passion for connecting people and organizations to the data they need to make an impact. She has been analyzing data for over 20 years across a wide range of industries, from finance to B2B software to consumer services. She has experience analyzing data with SQL across most of the major proprietary and open source databases. She has built and managed data teams and data infrastructure at a number of leading tech companies. Cathy is also a frequent speaker at top conferences, on topics including building data cultures, data-driven product development, and inclusive data analysis.