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 preformed in that action group. Remember your initial post on the main topic should be posted by Wednesday 11:59 PM (EST). Your 2 following posts should be commenting on your classmates’ post on different days by Sunday 11:59 PM (EST). You should end the week with 3 total discussion posts. A quality post is more than stating, “I agree with you.” Maybe you should state why you agree with your classmate’s post. Additionally, post some examples or find a related topic on the internet or University’s library and comment on it in the discussion post. Reference: Kirk, A. (2016). Data Visualisation: A Handbook for Data Driven Design (p. 50). SAGE Publications.

In data visualization, one of the key actions involved in working with data is data transformation. According to Kirk (2016), data transformation refers to the process of enhancing the raw data through modification and consolidation.

In this action group, the focus is on manipulating the data to make it more meaningful and suitable for analysis and visualization. This involves various techniques and methods to transform the data in a way that best represents the underlying patterns and relationships.

One important aspect of data transformation is data cleaning. This involves identifying and correcting any errors or inconsistencies in the data. Errors can occur due to various reasons such as human input errors, data entry errors, or technical issues. In this step, the data is carefully examined, and any discrepancies or outliers are identified and rectified. For example, if there are missing values or outliers that can significantly affect the analysis, appropriate actions are taken to handle them, such as imputation or removal.

Once the data is cleaned, the next step is data integration or consolidation. This involves combining data from multiple sources or datasets to create a unified and comprehensive dataset. Often, data relevant to a particular analysis might be scattered across different sources, and consolidating them into a single dataset allows for more efficient analysis and visualization. For example, if we are analyzing sales data for a company, we might need to merge data from different regions or departments to get a complete picture.

In addition to cleaning and consolidation, data transformation also includes methods to derive new variables or attributes from the existing data. This process is known as data derivation or feature engineering. It involves creating new variables that may provide additional insights or capture important aspects of the data. For example, in a dataset containing information about customers’ purchase history, we might derive new variables such as total amount spent, average purchase frequency, or customer loyalty levels based on specific calculations.

Another important aspect of data transformation is data normalization or scaling. This involves transforming the data to a common scale or range to facilitate meaningful comparisons and analysis. For example, if we have variables measured in different units or with different scales, such as temperature in Celsius and height in meters, normalizing the data ensures that they are on the same scale for appropriate analysis and visualization.

Overall, data transformation plays a crucial role in the data visualization process. It involves cleaning the data, consolidating it from multiple sources, deriving new variables, and normalizing the data. These actions help to enhance the quality and usability of the data and enable more accurate and insightful visualizations.