Assignment 1: Exploratory Data Analysis
Your team will select a topic of interest, identify a relevant dataset, and perform an exploratory data analysis to understand its structure, address initial questions, and generate preliminary insights and hypotheses.
Part 1: Data Description (Team)
As a team, select a topic of interest and find a relevant dataset for analysis.
Minimum Requirement: The data table you proposed for the project must have at least 10,000 rows and at least 10 columns.
The write-up of this part should include the following contents:
- Selected Topic: Describe the chosen topic of interest and explain why this topic is interesting/important. (3 points)
- Dataset Description: Provide the name and a description of the dataset, highlighting its relevance to the chosen topic. Also, briefly describe how the data was collected. (3 points)
- Data Attributes: What are the attributes in the dataset? What are the types of each variable? Is there any missing or outlier data? Do you need to transform the data before the analysis? (3 points)
- Data Access: Include the URL link or reference to access the dataset. (1 point)
This part count for a total of 10 points for the team.
Part 2: Exploratory Analysis (Individual)
Each team member will create two exploratory visualizations to answer specific research questions about the dataset. Each visualization, along with its description, must fit on one A4 page and include the following elements:
- Research Question: Clearly state a question the visualization addresses (each team member must select different questions).
- Indicate who is responsible in producing the visualization.
- Visualization Figure:
- Create a visualization that effectively answers that research question. Include a meaningful title, axis labels, and legends as necessary.
- The visualization should be interpretable without recourse to your write-up.
- Ensure the figure in the report is of high quality, i.e., all visual elements and text can be read easily.
- Figure Description: Explain the data aspects and the story conveyed. (1 paragraph)
- Design Rationale: Justify your design choices, including visual encodings, and explain how they enhance communication. (1 paragraph)
You are free to use any graphics or charting tool you are familiar with. To improve the quality of your visualization, you may need to transform the data, remove outliers, and incorporate additional external data sources.
This part counts for a total of 20 points.
Submission
Submit the team report (including both parts) on this Google Form. The deadline to submit this assignment is February 26th, 23:00.
Grading Criteria
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Component
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Excellent (3)
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Satisfactory (2)
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Poor (1)
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Data Question
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An interesting question (i.e., one without an immediately obvious answer) is posed. The visualization provides a clear answer.
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A reasonable question is posed, but it is unclear whether the visualization provides an answer to it.
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Missing or unclear question posed of the data.
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Mark and Encoding
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All design choices are effective. The visualization can be read and understood effortlessly.
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Design choices are largely effective, but minor errors hinder comprehension.
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Ineffective mark, encoding, or data transformation choices are distracting or potentially misleading.
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Title and Label
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Titles and labels helpfully describe and contextualize the visualization.
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Most necessary titles and labels are present, but they could provide more context.
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Many titles or labels are missing, or do not provide human-understandable information.
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Design Rationale
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Well-crafted write-up provides reasoned justification for all design choices and discussion of impact.
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Most design decisions are described, but rationale could be explained at a greater level of detail.
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Missing or incomplete. Several design choices or impacts are left unexplained.
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Creativity & Originality
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You exceeded the parameters of the assignment with original insights or a particularly engaging design.
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You met all the parameters of the assignment.
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You met most of the parameters of the assignment.
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Acknowledgment: This assignment is based on the assignment from CSE 442: Data Visualization course taught by Jeff Heer at the University of Washington and the Interactive Data Visualization course taught by Arvind Satyanarayan at MIT.
