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  2. Data science - Wikipedia

    en.wikipedia.org/wiki/Data_science

    Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. [4] Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. [5]

  3. Society for Industrial and Applied Mathematics - Wikipedia

    en.wikipedia.org/wiki/Society_for_Industrial_and...

    For example, SIAM has hosted an annual conference on data mining since 2001. [22] The establishment of the SIAM Conferences on Discrete Mathematics, held every two years, has been regarded as a sign of the growth of graph theory as a prominent topic of study. [ 23 ]

  4. Scientific Data (journal) - Wikipedia

    en.wikipedia.org/wiki/Scientific_Data_(journal)

    Scientific Data is a peer-reviewed open access scientific journal published by Nature Research since 2014. [1] It focuses on descriptions of data sets relevant to the natural sciences , medicine , engineering and social sciences , [ 2 ] which are provided as machine-readable data , complemented with a human oriented narrative.

  5. Data architecture - Wikipedia

    en.wikipedia.org/wiki/Data_architecture

    Data architecture consist of models, policies, rules, and standards that govern which data is collected and how it is stored, arranged, integrated, and put to use in data systems and in organizations. [1] Data is usually one of several architecture domains that form the pillars of an enterprise architecture or solution architecture. [2]

  6. Research Organization for Life Sciences and Environment

    en.wikipedia.org/wiki/Research_Organization_for...

    The Research Organization for Life Sciences and Environment (Indonesian: Organisasi Riset Ilmu Pengetahuan Hayati dan Lingkungan, ORIPHL but also known as ORHL) is one of Research Organizations under the umbrella of the National Research and Innovation Agency (Badan Riset dan Inovasi Nasional, BRIN).

  7. Data-driven model - Wikipedia

    en.wikipedia.org/wiki/Data-driven_model

    Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, [3] neural networks for approximating functions, [4] global optimization and evolutionary computing, [5] statistical learning theory, [6] and Bayesian methods. [7]

  8. Data literacy - Wikipedia

    en.wikipedia.org/wiki/Data_literacy

    Data literacy is the ability to read, understand, create, and communicate data as information. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data. [1] It is, however, not similar to the ability to read text since it requires certain skills involving reading and understanding data. [2]

  9. Data as a service - Wikipedia

    en.wikipedia.org/wiki/Data_as_a_service

    Data as a service (DaaS) is a cloud-based software tool used for working with data, such as managing data in a data warehouse or analyzing data with business intelligence. It is enabled by software as a service (SaaS). [ 1 ]