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02_activities/assignments/assignment_2.md

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     - You can find data visualizations at https://public.tableau.com/app/discover or https://datavizproject.com/, or anywhere else you like! 
 - For each visualization (good and bad):  
     - Explain (with reference to material covered up to date, along with readings and other scholarly sources, as needed) why you classified that visualization the way you did.
+    - How could this data visualization have been improved? 
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+- Good Example
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       Your answer...
+        A good example of data visualization can be found in the *Stock Market Trends* chart on Tableau Public. This visualization tracks daily price movements and trading volume for a company’s stock, and it works well because it is clear, accurate, and easy to understand. The data is directly tied to actual market performance, with transparent axes and scales that make it trustworthy. The use of line charts and volume bars is familiar to most viewers, which keeps the cognitive load low and allows people to quickly see volatility and trends without confusion (Sweller, 1994). The layout is clean, the colors are simple, and interactive features like hovering for details make the visualization engaging without being overwhelming. Since financial data is inherently temporal, the choice of a time-series line chart is appropriate and aligns with best practices for showing change over time. This reflects Tufte’s (1997) principle that good design should focus on clarity and integrity rather than unnecessary decoration.  
+        The visualization could be improved by adding annotations to highlight major events, so viewers can connect data trends to real-world causes. Accessibility could also be enhanced by using color palettes like Viridis, which are designed to be distinguishable for people with colorblindness (Lundgard & Satyanarayan, 2022). Finally, simplifying the trading volume bars by smoothing or aggregating them would reduce visual noise and make the chart easier to interpret. Overall, this visualization demonstrates how effective design can support decision-making and communication in finance.  
 
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+        References
+        Sweller, J. (1994). *Cognitive Load Theory, Learning Difficulty, and Instructional Design*. Learning and Instruction, 4(4), 295–312.  
+        Tufte, E. R. (1997). *Visual Explanations: Images and Quantities, Evidence and Narrative*. Graphics Press.  
+        Lundgard, A., & Satyanarayan, A. (2022). *Accessible Visualization Practices*. IEEE Computer Graphics and Applications.  
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-    - How could this data visualization have been improved?  
+- Bad Example  
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       Your answer...
 
+      A 3D bar chart from the Data Viz Project is a clear example of a bad visualization. The main problem is that the 3D effect distorts the data, making it hard to compare values accurately. Bars in the back look smaller or hidden even if they represent larger numbers, which misleads the viewer. This adds confusion and forces people to work harder to interpret the chart, increasing what Sweller (1994) calls extraneous cognitive load. The design also adds unnecessary clutter, which doesn’t help explain the data and instead distracts from it, violating Tufte’s principle of avoiding “chartjunk.” While the data itself may be correct, the way it is shown undermines substantive quality by exaggerating or minimizing differences. As Cairo (2016) and Few (2009) argue, 3D charts often obscure rather than clarify information. A simple 2D bar chart would have been much clearer and more effective.
+      To improve this visualization, the chart should be flattened into 2D so comparisons are accurate. Adding clear numeric labels would reduce reliance on visual estimation, and using consistent colors with good contrast would improve accessibility. Including proper axis titles, legends, and source citations would also reinforce transparency. By removing the 3D effects and focusing on clarity, the visualization could shift from being misleading to serving as a reliable tool for communication.
 
+        References
+        Cairo, A. (2016). The Truthful Art: Data, Charts, and Maps for Communication. New Riders.
+        Few, S. (2009). Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press.
+        Sweller, J. (1994). Cognitive Load Theory, Learning Difficulty, and Instructional Design. Learning and Instruction, 4(4), 295–312.
+        Tufte, E. R. (1997). Visual Explanations: Images and Quantities, Evidence and Narrative. Graphics Press.
 
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 - Word count should not exceed (as a maximum) 500 words for each visualization (i.e. 
 300 words for your good example and 500 for your bad example)