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  2. Listwise deletion - Wikipedia

    en.wikipedia.org/wiki/Listwise_deletion

    Listwise deletion is also problematic when the reason for missing data may not be random (i.e., questions in questionnaires aiming to extract sensitive information. [3] Due to the method, much of the subjects' data will be excluded from analysis, leaving a bias in data findings. For instance, a questionnaire may include questions about ...

  3. Microsoft Power BI - Wikipedia

    en.wikipedia.org/wiki/Microsoft_Power_BI

    A dataset can be connected to and get its source data through one or more dataflows. Power BI Datamart Within Power BI, the datamart is a container that combines Power BI Dataflows, datasets, and a type of data mart or data warehouse (in the form of an Azure SQL Database) into the same interface.

  4. Business intelligence software - Wikipedia

    en.wikipedia.org/wiki/Business_intelligence_software

    Business intelligence software is a type of application software designed to retrieve, analyze, transform and report data for business intelligence (BI). The applications generally read data that has been previously stored, often - though not necessarily - in a data warehouse or data mart.

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    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!

  6. Comma-separated values - Wikipedia

    en.wikipedia.org/wiki/Comma-separated_values

    Comma-separated values is a data format that predates personal computers by more than a decade: the IBM Fortran (level H extended) compiler under OS/360 supported CSV in 1972. [14] List-directed ("free form") input/output was defined in FORTRAN 77, approved in 1978. List-directed input used commas or spaces for delimiters, so unquoted character ...

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  8. Data lineage - Wikipedia

    en.wikipedia.org/wiki/Data_lineage

    The data flow helps the data scientist or a developer to look deeply into the actors and their transformations. This step allows the data scientist to figure out the part of the algorithm that is generating the unexpected output. A Big Data pipeline can go wrong in two broad ways. The first is a presence of a suspicious actor in the dataflow.

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