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Data Wrangling with SQL

Data Wrangling with SQL

By : Raghav Kandarpa, Shivangi Saxena
4.6 (25)
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Data Wrangling with SQL

Data Wrangling with SQL

4.6 (25)
By: Raghav Kandarpa, Shivangi Saxena

Overview of this book

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.
Table of Contents (21 chapters)
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1
Part 1:Data Wrangling Introduction
4
Part 2:Data Wrangling Techniques Using SQL
9
Part 3:SQL Subqueries, Aggregate And Window Functions
13
Part 4:Optimizing Query Performance
15
Part 5:Data Science And Wrangling

Descriptive Statistics with SQL

Descriptive statistics is a fundamental aspect of data analysis that helps us to summarize and describe the main characteristics of a dataset. With the increasing availability of large datasets, it has become more important than ever to have tools and techniques to help us understand the data we are working with.

In this chapter, we will explore how to use SQL to calculate various descriptive statistics measures, such as mean, median, mode, standard deviation, and variance. We will also demonstrate how to generate visualizations, such as histograms and box plots, to gain insights into the distribution of data.

Throughout the chapter, we will use real-world examples to demonstrate the application of SQL in descriptive statistics. We will assume that you have a basic understanding of SQL and statistical concepts such as mean, median, and standard deviation.

By the end of this chapter, you will have a solid understanding of how to use SQL to calculate...

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Data Wrangling with SQL
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