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: Data Science and Visual Computing
  name:
    name part: Earnshaw, Rae.
    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: Dill, John.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Kasik, 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
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVIII, 108 p. 16 illus., 11 illus. in color. online resource.
  abstract: Data science addresses the need to extract knowledge and information from data volumes, often from real-time sources in a wide variety of disciplines such as astronomy, bioinformatics, engineering, science, medicine, social science, business, and the humanities. The range and volume of data sources has increased enormously over time, particularly those generating real-time data. This has posed additional challenges for data management and data analysis of the data and effective representation and display. A wide range of application areas are able to benefit from the latest visual tools and facilities. Rapid analysis is needed in areas where immediate decisions need to be made. Such areas include weather forecasting, the stock exchange, and security threats. In areas where the volume of data being produced far exceeds the current capacity to analyze all of it, attention is being focussed how best to address these challenges. Optimum ways of addressing large data sets across a variety of disciplines have led to the formation of national and institutional Data Science Institutes and Centers. Being driven by national priority, they are able to attract support for research and development within their organizations and institutions to bring together interdisciplinary expertise to address a wide variety of problems. Visual computing is a set of tools and methodologies that utilize 2D and 3D images to extract information from data. Such methods include data analysis, simulation, and interactive exploration. These are analyzed and discussed.
  table of contents: Data Science -- Big Data -- Visual Computing -- Visualization -- Geometric Visualization -- Visual Analytics -- Data Science Institutes and Data Centers.
  note: by Rae Earnshaw, John Dill, David Kasik.
  subject:
    topic: Data structures (Computer science)
  subject:
    topic: Computer graphics
  subject:
    topic: User interfaces (Computer systems)
  subject:
    topic: Data Storage Representation
  subject:
    topic: Computer Graphics
  subject:
    topic: User Interfaces and Human Computer Interaction
  classification: QA76.9.D35
  classification: 004.5
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Advanced Information and Knowledge Processing
  identifier: 9783030243678
  identifier: https://doi.org/10.1007/978-3-030-24367-8
  record info:
    record creation date: 190814
    record change date: 20191024061521.0
    record identifier: 978-3-030-24367-8