TITLE

Springer Series in the Data Sciences

ISSN

2365-5682

ISSN ELECTRONIC

2365-5674

CATEGORY

Mathematics and Statistics

DESCRIPTION

Springer Series in the Data Sciences focuses primarily on monographs and graduate level textbooks. The target audience includes students and researchers working in and across the fields of mathematics, theoretical computer science, and statistics.Data Analysis and Interpretation is a broad field encompassing some of the fastest-growing subjects in interdisciplinary statistics, mathematics and computer science. It encompasses a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, including diverse techniques under a variety of names, in different business, science, and social science domains. Springer Series in the Data Sciences addresses the needs of a broad spectrum of scientists and students who are utilizing quantitative methods in their daily research. The series is broad but structured, including topics within all core areas of the data sciences. The breadth of the series reflects the variation of scholarly projects currently underway in the field of machine learning.

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BOOK (manifestation)

  • Book: 978-3-319-25386-2 (Book)

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    13 TRIPLES      13 PREDICATES      13 URIs      9 LITERALS

    Subject Predicate Object
    1 book-series:605c8bb6b31535990b194a59387e5f4e sg:category Mathematics and Statistics
    2 sg:ddsId 13852
    3 sg:description Springer Series in the Data Sciences focuses primarily on monographs and graduate level textbooks. The target audience includes students and researchers working in and across the fields of mathematics, theoretical computer science, and statistics.Data Analysis and Interpretation is a broad field encompassing some of the fastest-growing subjects in interdisciplinary statistics, mathematics and computer science. It encompasses a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, including diverse techniques under a variety of names, in different business, science, and social science domains. Springer Series in the Data Sciences addresses the needs of a broad spectrum of scientists and students who are utilizing quantitative methods in their daily research. The series is broad but structured, including topics within all core areas of the data sciences. The breadth of the series reflects the variation of scholarly projects currently underway in the field of machine learning.
    4 sg:issnElectronic 2365-5674
    5 sg:issnPrint 2365-5682
    6 sg:language En
    7 sg:license http://scigraph.springernature.com/explorer/license/
    8 sg:scigraphId 605c8bb6b31535990b194a59387e5f4e
    9 sg:shortTitle Springer Series in the Data Sciences
    10 sg:title Springer Series in the Data Sciences
    11 sg:webpage https://link.springer.com/13852
    12 rdf:type sg:BookSeries
    13 rdfs:label BookSeries: Springer Series in the Data Sciences
    HOW TO GET THIS DATA PROGRAMMATICALLY:

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    curl -H 'Accept: application/ld+json' 'http://scigraph.springernature.com/things/book-series/605c8bb6b31535990b194a59387e5f4e'

    N-Triples is a line-based linked data format ideal for batch operations .

    curl -H 'Accept: application/n-triples' 'http://scigraph.springernature.com/things/book-series/605c8bb6b31535990b194a59387e5f4e'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'http://scigraph.springernature.com/things/book-series/605c8bb6b31535990b194a59387e5f4e'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'http://scigraph.springernature.com/things/book-series/605c8bb6b31535990b194a59387e5f4e'






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