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  2. Data wrangling - Wikipedia

    en.wikipedia.org/wiki/Data_wrangling

    Data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. The goal of data wrangling is to assure quality and useful data.

  3. Data preprocessing - Wikipedia

    en.wikipedia.org/wiki/Data_Preprocessing

    Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process.Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing ...

  4. Cognitive pretesting - Wikipedia

    en.wikipedia.org/wiki/Cognitive_pretesting

    Whatever method used in the actual interview or test should be used in this method of pretesting. [1] [8] Cognitive pretesting (cognitive interviewing)- very similar to conventional pretesting. However, the participants are actively being asked about the questions as they take the test. It's conducted during the interview or test. [1] [6]

  5. Data transformation (computing) - Wikipedia

    en.wikipedia.org/wiki/Data_transformation...

    Data review; These steps are often the focus of developers or technical data analysts who may use multiple specialized tools to perform their tasks. The steps can be described as follows: Data discovery is the first step in the data transformation process. Typically the data is profiled using profiling tools or sometimes using manually written ...

  6. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  7. Data cleansing - Wikipedia

    en.wikipedia.org/wiki/Data_cleansing

    Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [2] and transforming it into one cohesive data set; a simple example is the expansion of abbreviations ("st, rd, etc." to "street, road, etcetera").

  8. Mortgage prequalification vs. preapproval: How to time these ...

    www.aol.com/finance/mortgage-prequalification-vs...

    Let's talk about two important steps you’ll need to take if you’re planning on getting a mortgage: prequalification and preapproval. These processes help you determine how much house you can ...

  9. Data preparation - Wikipedia

    en.wikipedia.org/wiki/Data_preparation

    Given the variety of data sources (e.g. databases, business applications) that provide data and formats that data can arrive in, data preparation can be quite involved and complex. There are many tools and technologies [5] that are used for data preparation. The cost of cleaning the data should always be balanced against the value of the ...