Data Wrangling with SQL A Hands-On Guide to Manipulating, Wrangling, and Engineering Data Using SQL /

Become a data wrangling expert and make well-informed decisions by effectively utilizing and analyzing raw unstructured data in a systematic manner Purchase of the print or Kindle book includes a free PDF eBook Key Features Implement query optimization during data wrangling using the SQL language wi...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Kandarpa, Raghav
Otros Autores: Saxena, Shivangi
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, Limited, 2023.
Edición:1st edition.
Temas:
SQL (Computer program language)
Data mining.
SQL (Langage de programmation)
Exploration de données (Informatique)
Electronic books.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Dedication
  • Contributors
  • Table of Contents
  • Preface
  • Part 1:Data Wrangling Introduction
  • Chapter 1: Database Introduction
  • Getting started
  • Establishing the foundation
  • Efficient data organization
  • Data integrity and consistency
  • Technical requirements
  • Decoding database structures
  • relational and non-relational
  • What is a database?
  • Types of databases
  • Tables and relationships
  • The SQL CREATE DATABASE statement
  • The SQL CREATE TABLE statement
  • SQL DROP TABLE versus TRUNCATE TABLE
  • SQL ALTER TABLE
  • SQL constraints
  • SQL keys
  • Database relationships
  • Comparing database normalization and denormalization
  • Normalization
  • Types of normalization
  • Denormalization
  • When to apply denormalization
  • Disadvantages of denormalization
  • Summary
  • Practical exercises
  • Practical exercise 1
  • Practical exercise 2
  • Practical exercise 3
  • Practical exercise 4
  • Chapter 2: Data Profiling and Preparation before Data Wrangling
  • What is data wrangling?
  • Data wrangling steps
  • The importance of data wrangling
  • Benefits of data wrangling
  • Data wrangling use cases
  • Business use cases
  • Data capture
  • How does data get captured?
  • Data-capturing techniques
  • Web scraping
  • Structured versus unstructured data
  • Paid-for versus free data-wrangling tools
  • Data profiling
  • Data profiling types
  • Data profiling techniques
  • Practical exercise
  • Step 1
  • Discovery
  • Step 2
  • Structuring
  • Step 3
  • Cleaning
  • Step 4
  • Enriching
  • Step 5
  • Validating
  • Step 6
  • Publishing
  • Summary
  • Part 2:Data Wrangling Techniques Using SQL
  • Chapter 3: Data Wrangling on String Data Types
  • SQL data types
  • Numeric data types
  • Date and time data types
  • String data type
  • SQL string functions
  • RIGHT()
  • LEFT()
  • LEN()
  • TRIM()
  • RTRIM()
  • LTRIM()
  • RPAD()
  • LPAD()
  • REPLACE()
  • REVERSE()
  • SUBSTRING()
  • CAST()
  • CONCATENATE()
  • CONCATENATE_WS()
  • UPPER function
  • LOWER function
  • INITCAP function
  • INSTR function
  • Summary
  • Practical exercises
  • Practical exercise 1
  • Practical exercise 2
  • Practical exercise 3
  • Practical exercise 4
  • Chapter 4: Data Wrangling on the DATE Data Type
  • SQL DATE data type functions
  • EXTRACT
  • DATEDIFF()
  • TIMEDIFF()
  • DATE_ADD()
  • DATE_SUB()
  • DATE_FORMAT()
  • STR_TO_DATE()
  • Extracting the current date and time
  • Summary
  • Chapter 5: Handling NULL Values
  • The impact of missing data and NULL values on data analysis
  • Understanding the importance of data validation and cleaning before analyzing data
  • Identifying NULL/missing values
  • NULL values versus zero values
  • Using the IS NULL and IS NOT NULL operators to filter and select data with NULL values
  • IS NULL() and IS NOT NULL()
  • scenario
  • Using the COALESCE and IFNULL functions to replace NULL values with a default value
  • IFNULL()
  • COALESCE()
  • IS NULL versus = NULL
  • Summary

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