Data Wrangling with SQL Год издания: 2023 Автор: Kandarpa R., Saxena S. Издательство: Packt ISBN: 9781837630028 Язык: Английский Формат: EPUB Качество: Издательский макет или текст (eBook) Интерактивное оглавление: Да Количество страниц: 350 Описание: The amount of data generated continues to grow rapidly, making it increasingly important for businesses to be able to wrangle this data and understand it quickly and efficiently. Although data wrangling can be challenging, with the right tools and techniques you can efficiently handle enormous amounts of unstructured data. The book starts by introducing you to the basics of SQL, focusing on the core principles and techniques of data wrangling. You’ll then explore advanced SQL concepts like aggregate functions, window functions, CTEs, and subqueries that are very popular in the business world. The next set of chapters will walk you through different functions within SQL query that cause delays in data transformation and help you figure out the difference between a good query and bad one. You’ll also learn how data wrangling and data science go hand in hand. The book is filled with datasets and practical examples to help you understand the concepts thoroughly, along with best practices to guide you at every stage of data wrangling. By the end of this book, you’ll be equipped with essential techniques and best practices for data wrangling, and will predominantly learn how to use clean and standardized data models to make informed decisions, helping businesses avoid costly mistakes.
Оглавление
Preface Part 1:Data Wrangling Introduction Chapter 1: Database Introduction Chapter 2: Data Profiling and Preparation before Data Wrangling Part 2:Data Wrangling Techniques Using SQL Chapter 3: Data Wrangling on String Data Types Chapter 4: Data Wrangling on the DATE Data Type Chapter 5: Handling NULL Values Chapter 6: Pivoting Data Using SQL Part 3:SQL Subqueries, Aggregate And Window Functions Chapter 7: Subqueries and CTEs Chapter 8: Aggregate Functions Chapter 9: SQL Window Functions Part 4:Optimizing Query Performance Chapter 10: Optimizing Query Performance Part 5:Data Science And Wrangling Chapter 11: Descriptive Statistics with SQL Chapter 12: Time Series with SQL Chapter 13: Outlier Detection Index Other Books You May Enjoy
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