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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2023.
|
| Edición: | 1st edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 049 | |a UAMI | ||
| 100 | 1 | |a Kandarpa, Raghav. | |
| 245 | 1 | 0 | |a Data Wrangling with SQL |h [electronic resource] : |b A Hands-On Guide to Manipulating, Wrangling, and Engineering Data Using SQL / |c Raghav Kandarpa, Shivangi Saxena. |
| 250 | |a 1st edition. | ||
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2023. | ||
| 300 | |a 1 online resource (351 p.) | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a computer |2 rdamedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 500 | |a Description based upon print version of record. | ||
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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() | |
| 505 | 8 | |a 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 | |
| 500 | |a Chapter 6: Pivoting Data Using SQL | ||
| 520 | |a 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 with practical use cases Master data cleaning, handle the date function and null value, and write subqueries and window functions Practice self-assessment questions for SQL-based interviews and real-world case study rounds Book Description 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. What you will learn 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 Who this book is for This book is for data analysts looking for effective hands-on methods to manage and analyze large volumes of data using SQL. The book will also benefit data scientists, product managers, and basically any role wherein you are expected to gather data insights and develop business strategies using SQL as a language. If you are new to or have basic knowledge of SQL and databases and an understanding of data cleaning practices, this book will give you further insights into how you can apply SQL concepts to build clean, standardized data models for accurate analysis. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a SQL (Computer program language) | |
| 650 | 0 | |a Data mining. | |
| 650 | 6 | |a SQL (Langage de programmation) | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 655 | 0 | |a Electronic books. | |
| 700 | 1 | |a Saxena, Shivangi. | |
| 776 | 0 | 8 | |i Print version: |a Kandarpa, Raghav |t Data Wrangling with SQL |d Birmingham : Packt Publishing, Limited,c2023 |z 9781837630028 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781837630028/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL30671945 | ||
| 938 | |a YBP Library Services |b YANK |n 305623711 | ||
| 994 | |a 92 |b IZTAP | ||