| 000 -LEADER |
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| fixed length control field |
a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
211026b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783030575915 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
005.74 |
| Item number |
BAD |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Badia, Antonio |
| 245 ## - TITLE STATEMENT |
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| Title |
SQL for data science : data cleaning, wrangling and analytics with relational databases |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Springer, |
| Date of publication, distribution, etc |
2020 |
| Place of publication, distribution, etc |
Cham : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xi, 285 p. ; |
| Other physical details |
ill., |
| Dimensions |
24 cm |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
49.99 |
| Price type code |
EUR |
| Unit of pricing |
90.50 |
| 490 ## - SERIES STATEMENT |
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| Series statement |
Data-centric systems and applications, |
| Volume number/sequential designation |
2197-9723 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
This textbook explains SQL within the context of data science and introduces the different parts of SQL as they are needed for the tasks usually carried out during data analysis. Using the framework of the data life cycle, it focuses on the steps that are very often given the short shift in traditional textbooks, like data loading, cleaning and pre-processing. The book is organized as follows. Chapter 1 describes the data life cycle, i.e. the sequence of stages from data acquisition to archiving, that data goes through as it is prepared and then actually analyzed, together with the different activities that take place at each stage. Chapter 2 gets into databases proper, explaining how relational databases organize data. Non-traditional data, like XML and text, are also covered. Chapter 3 introduces SQL queries, but unlike traditional textbooks, queries and their parts are described around typical data analysis tasks like data exploration, cleaning and transformation. Chapter 4 introduces some basic techniques for data analysis and shows how SQL can be used for some simple analyses without too much complication. Chapter 5 introduces additional SQL constructs that are important in a variety of situations and thus completes the coverage of SQL queries. Lastly, chapter 6 briefly explains how to use SQL from within R and from within Python programs. It focuses on how these languages can interact with a database, and how what has been learned about SQL can be leveraged to make life easier when using R or Python. All chapters contain a lot of examples and exercises on the way, and readers are encouraged to install the two open-source database systems (MySQL and Postgres) that are used throughout the book in order to practice and work on the exercises, because simply reading the book is much less useful than actually using it. This book is for anyone interested in data science and/or databases. It just demands a bit of computer fluency, but no specific background on databases or data analysis. All concepts are introduced intuitively and with a minimum of specialized jargon. After going through this book, readers should be able to profitably learn more about data mining, machine learning, and database management from more advanced textbooks and courses. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Database Management |
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| Topical term or geographic name as entry element |
Big Data Analytics |
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| Topical term or geographic name as entry element |
SQL |
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| Topical term or geographic name as entry element |
Computer program language |
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| Topical term or geographic name as entry element |
Python |
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| Topical term or geographic name as entry element |
Association Rule |
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| Topical term or geographic name as entry element |
Binning |
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| Topical term or geographic name as entry element |
Duplicate data |
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| Topical term or geographic name as entry element |
Foreign Key |
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| Topical term or geographic name as entry element |
Outliers |
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| Topical term or geographic name as entry element |
Subquery |
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| Topical term or geographic name as entry element |
Unstructured data |
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| Topical term or geographic name as entry element |
Big data |
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| Topical term or geographic name as entry element |
Meta data |
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| Topical term or geographic name as entry element |
Data cleaning |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Item type |
Books |