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Explaining data patterns in natural language with language models Chandan SinghJohn X. Morris Jyoti Aneja Alexander M. Rush Microsoft Research Cornell UniversityJianfeng Gaochansingh@microsoft.com
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Identify the type of data you are working with (e.g. numerical, categorical, etc)
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Visualize the data using plots such as histograms, scatter plots, and box plots to identify any patterns
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Use statistical measures such as mean, median, and mode to understand the central tendency of the data
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Apply machine learning algorithms or data mining techniques to uncover hidden patterns in the data
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Explaining patterns in data refers to the process of analyzing and interpreting data sets to identify trends, correlations, or anomalies that can provide insights or inform decision-making.
Entities or individuals involved in data analysis, such as researchers, data scientists, or organizations collecting data, may be required to file reports that explain patterns in their data, depending on regulatory or institutional guidelines.
Filling out explaining patterns in data typically involves collecting relevant data, conducting analysis to identify patterns, and compiling the findings into a structured report that addresses specific questions or criteria set by a governing body.
The purpose of explaining patterns in data is to provide clarity, enhance understanding, and facilitate informed decision-making based on the insights derived from data analysis.
Information that must be reported includes the methodology used for analysis, findings or patterns identified, supporting data, context for interpretation, and any conclusions or recommendations.
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