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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. Curriculum vitae - Wikipedia

    en.wikipedia.org/wiki/Curriculum_vitae

    Example of the type of extensive CV used in academia, in this case 69 pages long. In English, a curriculum vitae (English: / ... ˈ v iː t aɪ,-ˈ w iː t aɪ,-ˈ v aɪ t iː /, [a] [1] [2] [3] Latin for 'course of life', often shortened to CV) is a short written summary of a person's career, qualifications, and education.

  4. Résumé - Wikipedia

    en.wikipedia.org/wiki/Résumé

    A résumé or resume (or alternatively resumé), [a] [1] is a document created and used by a person to present their background, skills, and accomplishments. Résumés can be used for a variety of reasons, but most often are used to secure new jobs, whether in the same organization or another.

  5. Template (word processing) - Wikipedia

    en.wikipedia.org/wiki/Template_(word_processing)

    The term template, when used in the context of word processing software, refers to a sample document that has already some details in place; those can (that is added/completed, removed or changed, differently from a fill-in-the-blank of the approach as in a form) either by hand or through an automated iterative process, such as with a software assistant.

  6. AOL Mail

    mail.aol.com

    Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!

  7. Synthetic data - Wikipedia

    en.wikipedia.org/wiki/Synthetic_data

    Synthetic data is generated to meet specific needs or certain conditions that may not be found in the original, real data. One of the hurdles in applying up-to-date machine learning approaches for complex scientific tasks is the scarcity of labeled data, a gap effectively bridged by the use of synthetic data, which closely replicates real experimental data. [3]

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