國立虎尾科技大學 |

Introduction to Data Systems = Building from Python /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Introduction to Data Systems/ by Thomas Bressoud, David White.
其他題名:	Building from Python /
作者:	Bressoud, Thomas.
其他作者:	White, David.
面頁冊數:	XXIX, 828 p. 81 illus., 65 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Python. -
電子資源:	https://doi.org/10.1007/978-3-030-54371-6
ISBN:	9783030543716

Introduction to Data Systems = Building from Python /
Bressoud, Thomas.

Introduction to Data SystemsBuilding from Python /[electronic resource] :by Thomas Bressoud, David White. - 1st ed. 2020. - XXIX, 828 p. 81 illus., 65 illus. in color.online resource.

Part I Foundation -- 1. Introduction -- 2. File Systems and File Processing -- 3. Python Native Data Structures -- 4. Regular Expressions -- Part II Data Systems: The Data Models -- 5. Data Systems Models -- 6. Tabular Model: Structure and Formats -- 7. Tabular Model: Access Operations and pandas -- 8. Tabular Model: Advanced Operations and pandas -- 9. Tabular Model: Transformations and Constraints -- 10. Relational Model: Structure and Architecture -- 11. Relational Operations: Single Table -- 12. Relational Operations: Multiple Tables -- 13. Relational Database Programming -- 14. Relational Model: Design, Constraints, and Creation -- 15. Hierarchical Model: Structure and Formats -- 16. Hierarchical Model: Operations and Programming -- 17. Hierarchical Model: Constraints -- Part III Data Systems: The Data Sources -- 18. Overview of Data Systems Sources -- 19. Networking and Client-Server -- 20. The HyperText Transfer Protocol -- 21. Interlude: Client Data Acquisition -- 22. Web Scraping -- 23. RESTful Application Programming Interfaces -- 24. Authentication and Authorization.

Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form. The starting point of the book is a foundation in Python programming found in introductory computer science classes or short courses on the language, and so does not require prerequisites of data structures, algorithms, or other courses. This makes the material accessible to students early in their educational career and equips them with understanding and skills that can be applied in computer science, data science/data analytics, and information technology programs as well as for internships and research experiences. This book is accessible to a wide variety of students. By drawing together content normally spread across upper level computer science courses, it offers a single source providing the essentials for data science practitioners. In our increasingly data-centric world, students from all domains will benefit from the “data-aptitude” built by the material in this book.

ISBN: 9783030543716

Standard No.: 10.1007/978-3-030-54371-6doiSubjects--Topical Terms:

1115944
Python.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Introduction to Data Systems = Building from Python /
LDR:03925nam a22004095i 4500 001 1030430
003 DE-He213
005 20201204163027.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030543716 $9 978-3-030-54371-6
024 7 $a 10.1007/978-3-030-54371-6 $2 doi
035 $a 978-3-030-54371-6
050 4 $a QA76.9.D343
072 7 $a UNF $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.312 $2 23
100 1 $a Bressoud, Thomas. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1327340
245 1 0 $a Introduction to Data Systems $h [electronic resource] : $b Building from Python / $c by Thomas Bressoud, David White.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XXIX, 828 p. 81 illus., 65 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Part I Foundation -- 1. Introduction -- 2. File Systems and File Processing -- 3. Python Native Data Structures -- 4. Regular Expressions -- Part II Data Systems: The Data Models -- 5. Data Systems Models -- 6. Tabular Model: Structure and Formats -- 7. Tabular Model: Access Operations and pandas -- 8. Tabular Model: Advanced Operations and pandas -- 9. Tabular Model: Transformations and Constraints -- 10. Relational Model: Structure and Architecture -- 11. Relational Operations: Single Table -- 12. Relational Operations: Multiple Tables -- 13. Relational Database Programming -- 14. Relational Model: Design, Constraints, and Creation -- 15. Hierarchical Model: Structure and Formats -- 16. Hierarchical Model: Operations and Programming -- 17. Hierarchical Model: Constraints -- Part III Data Systems: The Data Sources -- 18. Overview of Data Systems Sources -- 19. Networking and Client-Server -- 20. The HyperText Transfer Protocol -- 21. Interlude: Client Data Acquisition -- 22. Web Scraping -- 23. RESTful Application Programming Interfaces -- 24. Authentication and Authorization.
520 $a Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form. The starting point of the book is a foundation in Python programming found in introductory computer science classes or short courses on the language, and so does not require prerequisites of data structures, algorithms, or other courses. This makes the material accessible to students early in their educational career and equips them with understanding and skills that can be applied in computer science, data science/data analytics, and information technology programs as well as for internships and research experiences. This book is accessible to a wide variety of students. By drawing together content normally spread across upper level computer science courses, it offers a single source providing the essentials for data science practitioners. In our increasingly data-centric world, students from all domains will benefit from the “data-aptitude” built by the material in this book.
650 2 4 $a Python. $3 1115944
650 2 4 $a Big Data. $3 1017136
650 2 4 $a Information Systems and Communication Service. $3 669203
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Data Structures and Information Theory. $3 1211601
650 1 4 $a Data Mining and Knowledge Discovery. $3 677765
650 0 $a Python (Computer program language). $3 1127623
650 0 $a Big data. $3 981821
650 0 $a Computers. $3 565115
650 0 $a Artificial intelligence. $3 559380
650 0 $a Data structures (Computer science). $3 680370
650 0 $a Data mining. $3 528622
700 1 $a White, David. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1327341
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030543709
776 0 8 $i Printed edition: $z 9783030543723
776 0 8 $i Printed edition: $z 9783030543730
856 4 0 $u https://doi.org/10.1007/978-3-030-54371-6
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)