rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Introduction to Data Systems
    sub title: Building from Python
  name:
    name part: Bressoud, Thomas.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: White, David.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2020
    edition: 1st ed. 2020.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XXIX, 828 pages 81 illustrations, 65 illustrations in color.)
  abstract: 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.
  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.
  note: by Thomas Bressoud, David White.
  subject:
    topic: Data mining
  subject:
    topic: Data structures (Computer science)
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computers
  subject:
    topic: Big data
  subject:
    topic: Python (Computer program language)
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Data Structures and Information Theory
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Information Systems and Communication Service
  subject:
    topic: Big Data
  subject:
    topic: Python
  classification: QA76.9.D343
  classification: 006.312
  related item:
    title info:
      title: Springer Nature eBook
  identifier: 9783030543716
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 201204
    record change date: 20230330185218.4
    record identifier: in00004428236