Introduction to data systems : building from Python /

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Bibliographic Details
Author / Creator:	Bressoud, Thomas, author.
Imprint:	Cham, Switzerland : Springer, 2020.
Description:	1 online resource (xxix, 828 pages) : illustrations (some color)
Language:	English
Subject:	Data mining. Python (Computer program language) Data structures (Computer science) Data mining. Data structures (Computer science) Python (Computer program language) Electronic books.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/12609796

Hidden Bibliographic Details
Other authors / contributors:	White, David, author.
ISBN:	9783030543716 3030543714 3030543706 9783030543709 9783030543723 3030543722 9783030543730 3030543730
Digital file characteristics:	text file PDF
Notes:	Includes bibliographical references and index. Online resource; title from PDF title page (SpringerLink, viewed February 23, 2021).
Summary:	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.
Other form:	Print version: 9783030543709
Standard no.:	10.1007/978-3-030-54371-6

Table of Contents:

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.

Introduction to data systems : building from Python /

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