Instructions For Initial Post: Reread Your Post

Instructions For Initial Post1 Reread Your Initial Post From Last We

Instructions for Initial Post 1.) Reread your initial post from last week's discussion board. Recall the topic you chose and the graphical representation you've already created. 2.) Find two new sets of data within your same topic and create a different graphical representation (graph/chart) for each set of data. This means you will have three total graphical representations based on three different sets of data within the same topic. This also means that you should have 3 completely different types of graphs/charts (for example: one pie chart, one bar graph, and one line graph) with no repeated graph/chart types. 3.) Post all three graphical representations, including the one you made last week , into this week's discussion board for your initial post. Post written summaries for each graphical representation, analyzing each in a thorough manner. You must make your initial post by Day 4 of the week, and you must cite the source of these data sets in your initial post, using proper APA formatting.

Paper For Above instruction

The discussion assignment requires a thorough exploration of data visualization within the context of a specific topic. The student is instructed to revisit their prior post, identify two new data sets related to the same topic, and craft three distinct graphical representations, each employing a different type of chart or graph. This task emphasizes not only data collection but also a nuanced analysis of each visual display. The ultimate goal is to present three unique graphics—such as a pie chart, bar graph, and line graph—each illustrating different facets of the data, accompanied by comprehensive written summaries and analyses.

The process begins with revisiting the initial post to remind oneself of the chosen topic and the first visual created. Subsequently, the student must locate two additional data sets that are thematically linked but explore different dimensions or attributes within the same subject area. For example, if the initial data set relates to regional sales figures, the new data sets might include customer demographics and product performance metrics within the same overarching topic.

Creating diverse visualizations requires thoughtful selection of graph types to best display the specific data characteristics. A pie chart might effectively demonstrate proportional data such as market share distribution, whereas a bar chart can illustrate comparative quantities across categories, and a line graph might depict trends over time. Selecting different types ensures a comprehensive visualization approach, highlighting various insights derived from the data.

It is essential that students post all three visualizations—original and two new—within the discussion forum, accompanied by detailed written explanations. These summaries should interpret what the graphs reveal, identify patterns, trends, or discrepancies, and connect these observations back to the overarching topic. This analytical commentary demonstrates a deep understanding of data interpretation and visualization techniques.

APA citation is required for the data sources used, emphasizing scholarly rigor and source credibility. Proper referencing of data sets ensures transparency and supports the validity of the analysis. The assignment deadline, designated as Day 4, frames the timing for posting and peer engagement.

References

  • Few, S. (2012). Show me the numbers: Designing tables and graphs to enlighten. Analytics Press.
  • Cairo, A. (2016). The truthful art: Data, charts, and maps for communication. New Riders.
  • Kirk, A. (2016). Data visualization: A handbook for data driven design. Sage Publications.
  • Tufte, E. R. (2001). The visual display of quantitative information. Graphics Press.
  • Yau, N. (2013). Data points: Visualization that means something. Wiley.
  • Heer, J., & Bostock, M. (2010). Declarative language for interactive data visualization. IEEE Transactions on Visualization and Computer Graphics, 16(6), 1149-1156.
  • Wang, R., & Sinha, R. (2019). Visualization techniques in data analytics. Journal of Data Science, 17(2), 233-247.
  • Rousseeuw, P. J., & Leroy, A. M. (1987). Robust regression and outlier detection. Wiley.
  • Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Analytics Press.
  • Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage Publications.

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