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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").
Data mining is the process of extracting and ... that might be interesting or data errors that require ... It is common for data mining algorithms to find patterns in ...
Data mining involves six common classes of tasks: [1] Anomaly detection (Outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation. Association rule learning (Dependency modelling) – Searches for relationships between variables. For example ...
There are other common habits and oversights that could leave your financial data wide open to cybercriminals. Mistakes like these don’t just jeopardize your bank account, they can lead to ...
If the data collection or analysis were to stop at a point where the p-value happened to fall below the significance level, a spurious statistically significant difference could be reported. Optional stopping is a practice where one collects data until some stopping criteria is reached. While it is a valid procedure, it is easily misused.
Data corruption refers to errors in computer data that occur during writing, reading, storage, transmission, or processing, which introduce unintended changes to the original data. Computer, transmission, and storage systems use a number of measures to provide end-to-end data integrity , or lack of errors.
Sometimes missing values are caused by the researcher—for example, when data collection is done improperly or mistakes are made in data entry. [ 2 ] These forms of missingness take different types, with different impacts on the validity of conclusions from research: Missing completely at random, missing at random, and missing not at random.
Here are seven common banking mistakes to avoid both now and throughout retirement to save money and earn more on your savings. Banking mistake 1: Keeping too much in checking