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  2. Data engineering - Wikipedia

    en.wikipedia.org/wiki/Data_engineering

    Around the 1970s/1980s the term information engineering methodology (IEM) was created to describe database design and the use of software for data analysis and processing. [3] [4] These techniques were intended to be used by database administrators (DBAs) and by systems analysts based upon an understanding of the operational processing needs of organizations for the 1980s.

  3. Data science - Wikipedia

    en.wikipedia.org/wiki/Data_science

    Example for the usefulness of exploratory data analysis as demonstrated using the Datasaurus dozen data set Data science is at the intersection of mathematics, computer science and domain expertise. Data science and data analysis are both important disciplines in the field of data management and analysis, but they differ in several key ways.

  4. Data modeling - Wikipedia

    en.wikipedia.org/wiki/Data_modeling

    Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, the process of data modeling involves professional data modelers working closely with business stakeholders, as well as potential users of the ...

  5. Data analysis - Wikipedia

    en.wikipedia.org/wiki/Data_analysis

    Data analysis is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. [1] Data is collected and analyzed to answer questions, test hypotheses, or disprove theories. [11] Statistician John Tukey, defined data analysis in 1961, as:

  6. Artificial intelligence engineering - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence...

    This involves cleansing, normalizing, and augmenting the data as needed. Creating data pipelines and addressing issues like imbalanced datasets or missing values are also essential to maintain model integrity during training. [27] In the case of using pre-existing models, the dataset requirements often differ.

  7. 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. It is, however, not similar to the ability to read text since it requires certain skills involving reading and understanding data. [1]

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  9. Data mining - Wikipedia

    en.wikipedia.org/wiki/Data_mining

    The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to ...

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