According to Kirk (2016), most of your time will be spent wo…

According to Kirk (2016), most of your time will be spent working with your data.  The four following group actions were mentioned by Kirk (2016): Select 1 data action and elaborate on the actions preformed in that action group. Purchase the answer to view it


One of the data action groups mentioned by Kirk (2016) is data transformation. Data transformation refers to the process of changing the structure or format of data to make it more suitable for analysis and interpretation. This action group includes a variety of actions that can be performed on the data.

One of the actions in the data transformation group is data cleaning. Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies that may exist in the data. This action involves various tasks, such as removing duplicate entries, handling missing values, and correcting formatting issues. Data cleaning is essential to ensure the accuracy and reliability of the data before further analysis.

Another action in the data transformation group is data integration. Data integration involves combining data from different sources into a single dataset. This action is often necessary when working with data from multiple databases or systems. It may involve merging datasets with similar variables or combining datasets with different variables but a common identifier. Data integration enables a comprehensive analysis by providing a unified view of the data.

Data aggregation is another action in the data transformation group. Aggregation involves combining individual data points into groups or summary measures. This action is useful for reducing the complexity and size of the dataset, especially when dealing with large datasets. Aggregated data allows for high-level analysis and the identification of patterns or trends. Common aggregation techniques include summing, averaging, or counting data values within specific categories or time periods.

Data transformation also includes actions such as data normalization and data discretization. Data normalization involves scaling data to a common range or format to facilitate robust comparisons and analysis. It ensures that variables with different units or scales are comparable. Data discretization, on the other hand, involves converting continuous data into discrete categories or intervals. This action is useful for simplifying analyses or handling data that is too granular or detailed.

In summary, the data transformation action group includes various actions that are crucial for preparing data for analysis. These actions, such as data cleaning, data integration, data aggregation, data normalization, and data discretization, help ensure data accuracy, improve data quality, and enable efficient and effective analysis. Each action serves a specific purpose in transforming raw data into a format suitable for analysis and interpretation.

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