Data Visualization Project: Insightful Analysis Using Python/Power BI on Real-World Dataset with Design Reflection & Team Presentation
| University | City Colleges (CC) |
| Subject | Data Visualisation |
Assessment Task [100 Marks]
- Form groups of 3. Every group selects a dataset different from other project groups in the class. Datasets can be in CSV, Excel etc formats. Selected dataset should reflect a domain your group has collective interest in. Sources could be: Kaggle, UCI ML repository, government websites etc.
- Google spreadsheets: write group members and dataset link. Dataset must be different for every group. I have put the link for the spreadsheet on the module page.
- Think of 10 data analysis questions that you want to answer using your dataset.
- Think about data preparation, data cleaning, data transformation needed to explore your dataset. Create visualisations which help in answering the questions. Think in terms of data visualisation techniques that you have studied in this module such as:
- What relationship in the data was most interesting?
- What encodings did you consider?
- Successful design decisions (why?)
- Design decisions that can be improved (why?)
- Things you all discovered was hard (be specific)
- Tools that you can use: Python, Microsoft Power BI.
- Prepare slides for presentation in the last class. 14th May (tentative.)
- Presentations are 15 mins each. 5 mins for every group member.
Total Marks: 100
Marking Scheme: Group Data Visualisation Project
• Dataset Selection & Project Setup (10%)
* Dataset Appropriateness (5%):
– Dataset successfully selected and distinct from other groups.
– Dataset source documented (link provided in spreadsheet).
– Relevance to group interest is clear.
– Dataset complexity is suitable for the scope of the assignment (not too simple, not overwhelmingly complex).
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* Analysis Questions (5%):
– 10 distinct and relevant data analysis questions formulated.
– Questions are clear, specific, and potentially answerable with the chosen dataset.
– Questions demonstrate curiosity and a clear goal for the analysis.
• Data Preparation & Cleaning (15%)
* Identification of Needs (5%):
– Clear identification of necessary data cleaning steps (e.g., handling missing values, correcting errors, standardizing formats).
– Clear identification of necessary data transformation steps (e.g., creating new variables, aggregation, reshaping data).
* Execution (10%):
– Appropriate techniques applied for cleaning and transformation.
– Process is logical and well-justified (can be explained in presentation/report).
– Evidence of cleaned/prepared data being used for analysis and visualization (e.g., code shown, steps described).
• Data Visualization (30%)
* Appropriateness & Effectiveness (15%):
– Visualizations chosen are appropriate for the type of data and the specific question being addressed.
– Visualizations effectively communicate insights and patterns related to the analysis questions.
– Variety of relevant visualization techniques demonstrated (beyond basic charts if appropriate).
* Technical Quality & Clarity (10%):
– Visualizations are technically correct and well-executed (using Python/Power BI).
– Charts are clearly labelled (titles, axes, legends).
– Visualizations are easy to read and interpret; good aesthetic choices (colour, layout) aid understanding without misleading.
* Encoding Choices (5%):
– Demonstrated understanding of visual encodings (position, colour, size, shape, etc.).
– Justification for encoding choices is sound (even if discussed briefly in presentation).
• Analysis & Interpretation (25%)
* Answering Questions (10%):
– Clear attempt to answer the formulated analysis questions using the visualizations and underlying data.
– Conclusions drawn are logical and supported by evidence from the analysis.
* Depth of Insight (10%):
– Analysis goes beyond surface-level descriptions.
– Identification of interesting relationships, trends, outliers, or patterns (addresses Q4a).
– Demonstrates critical thinking about the data and its meaning.
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* Reflection on Design & Process (5%): (Addresses Q4a-e)
– Discussion of successful design decisions and why they worked.
– Acknowledgement of design decisions that could be improved and why.
– Identification of specific challenges encountered during the process (data prep, visualization choice, tool limitations etc.).
• Presentation & Communication (20%)
* Slide Quality (10%):
– Slides are clear, well-organized, and visually appealing.
– Key findings, visualizations, and reflections are effectively summarized.
– Appropriate balance between text and visuals; not overly cluttered.
– Covers all key aspects of the project (questions, prep, viz, analysis, reflection).
* Delivery & Teamwork (10%):
– Presentation delivered within the time limit (approx. 15 mins total).
– Each member contributes meaningfully (approx. 5 mins each).
– Clear and confident explanation of the group’s work.
– Smooth transitions between speakers.
– Ability to articulate findings and respond to potential questions (if Q&A occurs).
Notes for Markers:
- Tool Usage: Proficiency in Python or Power BI will be evident in the quality of data preparation, visualization, and analysis. No separate mark is needed, but lack of proficiency will negatively impact those sections.
- Group Work: While the mark is primarily for the group output, the presentation delivery component allows for some assessment of individual contribution to communication. Significant disparities in contribution during presentation might be noted.
- Adherence to Instructions: Ensure the group followed all instructions (unique dataset, Google Sheet update, number of questions, tool usage, time limits). Minor deviations might incur small penalties at the marker’s discretion, while major ones (e.g., wrong tools, duplicate dataset) could impact relevant sections more significantly.
- Focus: The core of the assessment lies in the visualization, analysis, and the critical reflection upon the process and design choices.
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