Data Wrangling with SQL

Data Wrangling with SQL

eBook Details:

  • Paperback: 350 pages
  • Publisher: WOW! eBook (July 31, 2023)
  • Language: English
  • ISBN-10: 183763002X
  • ISBN-13: 978-1837630028

eBook Description:

Data Wrangling with SQL: Become a data wrangling expert and make well-informed decisions by effectively utilizing and analyzing raw unstructured data in a systematic manner

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.

  • Build time series models using data wrangling
  • Discover data wrangling best practices as well as tips and tricks
  • Find out how to use subqueries, window functions, CTEs, and aggregate functions
  • Handle missing data, data types, date formats, and redundant data
  • Build clean and efficient data models using data wrangling techniques
  • Remove outliers and calculate standard deviation to gauge the skewness of data

By the end of this Data Wrangling with SQL 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.

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