According To Kirk 2016, Most Of Your Time Will Be Spe 528841

According To Kirk 2016 Most Of Your Time Will Be Spent Working With

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): Data acquisition: Gathering the raw material Data examination: Identifying physical properties and meaning Data transformation: Enhancing your data through modification and consolidation Data exploration: Using exploratory analysis and research techniques to learn Select 1 data action and elaborate on the actions performed in that action group.

Paper For Above instruction

Data Transformation is a critical phase in the data analysis process that involves modifying and consolidating data to enhance its quality and usability for subsequent analysis. As outlined by Kirk (2016), data transformation is crucial because raw data often contains inconsistencies, errors, or formatting issues that can hamper effective analysis. The actions performed during data transformation include cleaning, standardizing, integrating, and creating new variables, all of which aim to prepare the data for meaningful insights.

One of the primary actions in data transformation is data cleaning. This step involves identifying and correcting errors or inconsistencies in the data. For instance, missing values, duplicate records, or incorrect entries can distort analytical results. Utilizing tools such as filters, conditional formatting, or data validation functions, analysts can detect anomalies and either rectify or remove problematic records. For example, if sales data contain missing entries, the analyst might choose to impute missing values using statistical methods such as mean or median substitution to maintain dataset integrity.

Standardization and normalization are further actions in data transformation that ensure data comparability across different units or scales. For example, converting dates into a consistent format or transforming measurements from miles to kilometers facilitates seamless analysis and interpretation. These steps reduce data variability due to formatting differences, ensuring that subsequent analytical techniques operate on uniform data inputs.

Data integration is another key aspect of transformation, involving combining data from multiple sources to create a comprehensive dataset. This might include merging customer information stored in different databases or aggregating transactional data across various periods. Proper integration requires careful matching of keys and attributes to prevent duplication or misalignment. Techniques such as SQL joins, lookup functions, or data blending tools are often employed to achieve reliable data merges.

Creating new variables, or feature engineering, is an advanced action that adds value to the dataset. Analysts can derive new insights by transforming existing data into more meaningful indicators. For instance, combining date and time fields to calculate customer engagement duration, or generating categorical variables from continuous data, can reveal hidden patterns. This process heightened the predictive power of models and facilitated more nuanced analysis.

Effective data transformation demands meticulous attention to detail, a thorough understanding of the data context, and proficiency with data manipulation tools in software such as Excel, R, or Python. These actions ultimately lead to cleaner, more consistent, and insightful datasets, enabling organizations to make data-driven decisions with confidence.

References

  • Kirk, R. (2016). Data Preparation for Data Mining Using SAS. SAS Institute.
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  • Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22.

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