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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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

aggregate functions 187, 188, 219-221

AVG 188, 201, 202, 219

case scenario 214, 215

COUNT 188, 192, 193, 219

MAX 188, 206, 207, 219

MIN 188, 206, 207, 219

SUM 188, 196, 197, 219

alternate key 12

Alteryx 48

analytical workflow

SQL to business intelligence (BI) 153

analytic function 222

approximate data types

DOUBLE 64

FLOAT 63

average 272

AVG() function 188, 201, 202, 229, 230

case scenario 202-205

scenario 230, 231

B

box plot method 312

used, for outlier detection 306, 307

Boyce-Codd Normal Form (BCNF) 25

super key 25, 26

super key, fixing 26, 27

C

candidate key 11

cascading actions 15

DELETE CASCADE 15

UPDATE CASCADE 15, 16

CAST() function 78, 120, 121

arguments 78

benefits 122

case scenarios...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
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Data Wrangling with SQL
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