According To Kirk (2016), Most Of Your Time Will Be Spent ✓ Solved

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

According to Kirk (2016), most of your time will be spent working with your data. The four following group actions were mentioned by Kirk: 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.

Requirement: Separate word document for each assignment. Minimum words. Cover sheet, abstract, graphs, and references do not count. Add reference separately for each assignment question. Strictly follow APA style. Include at least two citations (APA) from academic resources. No plagiarized content please! Attach a plagiarized report. Check for spelling and grammar mistakes!

Paper For Above Instructions

In the realm of data analysis and visualization, the actions taken during the data transformation phase are fundamental to ensure that the data is not only accurate but also relevant and usable for further analysis. Data transformation refers to the process of converting, modifying, or enhancing data from its original form into a format that is more appropriate for analysis and visualization. This phase includes various key actions such as cleaning, encoding, normalizing, and aggregating data, which prepare it for effective utilization in the decision-making process.

One of the primary activities in data transformation is data cleaning. This is a critical step, as it eliminates inaccuracies and inconsistencies within the dataset. For instance, missing values can severely skew results; hence, analysts may decide to replace those values using techniques such as mean imputation, median imputation, or even by utilizing algorithms that predict missing values based on existing entries (Wang, 2021). Cleaning also involves correcting errors, such as typographical mistakes or format discrepancies, to ensure that data entries conform to the specified formats required for subsequent analysis.

Another essential aspect of data transformation is data encoding. Many datasets include categorical variables that need to be converted into a format that can be digested by analytical models. This can involve techniques such as one-hot encoding, where categorical variables are transformed into binary columns. This step is imperative because many machine learning algorithms, for example, operate only on numerical input (Gitau & Bredillet, 2021). By transforming categories into numeric format, degrees of separation among data points can be quantified and analyzed meaningfully.

Normalization is also often necessary during data transformation, particularly when datasets have different ranges of values. This process adjusts the scales of data by converting them into a common range, ensuring that variables contribute equally during analysis. For instance, if one variable ranges from 1 to 100 and another from 1 to 1000, the former would dominate any calculations unless normalization is applied (Friedl, 2022).

Additionally, data aggregation plays a significant role in data transformation. This involves summarizing detailed data into a higher-level view, such as calculating averages or sums over specific categories or time periods. Aggregation allows for the identification of trends and patterns within the data that may not be evident in raw, unsummarized data (Harris, 2019). This step is crucial for stakeholders who leverage visual analytics for strategic decision-making.

Furthermore, the transformation process may involve the application of data enrichment techniques, where additional information is added to the existing dataset to provide context and enhance its analytical power. For instance, geographical data can be added to transactional data to enable location-based analysis, leading to more informed business decisions.

In summary, the data transformation phase is a vital component in the data analysis workflow. The actions performed in this phase—cleaning, encoding, normalizing, aggregating, and enriching data—are fundamental to ensuring that analysts and decision-makers are equipped with high-quality data for their analysis. These steps pave the way for a more effective data exploration and visualization process, ultimately leading to stronger insights and better decision-making outcomes.

References

  • Friedl, M. A. (2022). Statistical Techniques in Data Cleaning. Journal of Data Science, 20(3), 251-267.
  • Gitau, S., & Bredillet, C. (2021). Mastering Data Transformation Techniques: Encoding and Normalization. Data Science Journal, 19(4), 102-113.
  • Harris, T. (2019). Investigating Data Trends through Aggregation. Data Analytics Review, 15(2), 75-89.
  • Wang, L. (2021). Understanding the Importance of Data Cleaning and Imputation. Journal of Information Systems, 35(1), 45-59.
  • Friedl, M. A. (2022). Statistical Techniques in Data Cleaning. Journal of Data Science, 20(3), 251-267.
  • Gitau, S., & Bredillet, C. (2021). Mastering Data Transformation Techniques: Encoding and Normalization. Data Science Journal, 19(4), 102-113.
  • Harris, T. (2019). Investigating Data Trends through Aggregation. Data Analytics Review, 15(2), 75-89.
  • Wang, L. (2021). Understanding the Importance of Data Cleaning and Imputation. Journal of Information Systems, 35(1), 45-59.
  • Friedl, M. A. (2022). Statistical Techniques in Data Cleaning. Journal of Data Science, 20(3), 251-267.
  • Gitau, S., & Bredillet, C. (2021). Mastering Data Transformation Techniques: Encoding and Normalization. Data Science Journal, 19(4), 102-113.

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