According To Kirk 2016, Most Of Your Time Will Be Spe 897017

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 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. Remember your initial post on the main topic should be posted by Thursday 11:59 PM (EST). Your 3 following posts should be commenting on your classmates’ posts on different days by Sunday 11:59 PM (EST). You should end the week with 4 total discussion posts.

A quality post is more than stating, “I agree with you.” Maybe you should state why you agree with. You must do the following: 1) Create a new thread to write your initial response to the discussion prompt. 2) Select AT LEAST 2 other students' threads and post substantive comments on those threads. Your comments should extend the conversation started with the thread. 3) Provide a thoughtful research-based response and include references to support your thoughts. 4) Exclude attachments and cover page in your submission.

Type your response in the message box. When responding to your peer, address your peer by name. 5) ALL original posts (two to three paragraphs supported by resources) and comments must be substantive. (I'm looking for about a paragraph — not just "I agree.") 6) Paraphrase text from resources used and cite. If quoting text, use double-quotes and cite. Reference: Kirk, A. (2016). Data Visualisation: A Handbook for Data Driven Design (p. 50). SAGE Publications.

Paper For Above instruction

Data transformation is a critical phase in the data analysis process, as highlighted by Kirk (2016). This step involves modifying and consolidating raw data to improve its quality, consistency, and usability. Data transformation encompasses various activities such as cleaning the data by handling missing or inconsistent values, normalizing data to standardize formats or scales, and aggregating data to facilitate analysis at different levels of detail. These processes are essential for preparing data for meaningful analysis and visualization, ensuring that insights derived are accurate and reliable.

One of the primary actions within data transformation is data cleaning, which eliminates inaccuracies and inconsistencies that can distort analysis outcomes. For instance, resolving duplicate entries, correcting formatting errors, and imputing missing values help create a cleaner dataset that accurately reflects the underlying phenomena. Data normalization is another crucial activity, involving transforming data into a common scale, especially when combining multiple datasets or variables with diverse units. Standardizing data ensures that comparisons are meaningful and unbiased, which is vital for producing valid insights in data visualization.

Data aggregation consolidates data points into summarized measures, such as totals or averages, facilitating easier interpretation and highlighting overarching trends. For example, transforming daily sales data into monthly or quarterly summaries allows analysts to recognize broader patterns and seasonality effects. Throughout the transformation process, maintaining data integrity and documenting changes are essential to ensure transparency and reproducibility. Overall, data transformation enhances the quality and usability of data, enabling analysts to derive accurate, insightful, and actionable conclusions from their datasets.

References

  • Kirk, A. (2016). Data Visualisation: A Handbook for Data Driven Design. SAGE Publications.
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