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Portfolio Project: Data Visualization Portfolio
Total Points: 35 + 5 bonus
Submission: ZIP archive with index.html, relevant data sources + Video presentation (5-7 minutes)
Optionally: a website published on github pages (not graded)
TL;DR - Quick Overview
Portfolio with minimum 5 visualizations exploring a unified theme
Submit by Week 8:
- ZIP file with
index.html, datasets, andVIDEO_LINK.txt - 5-7 minute video presentation
Must demonstrate:
- Clear analytical questions (data/task abstraction)
- Justified chart choices and visual encodings
- Professional documentation (titles, labels, legends, sources)
- Sophisticated techniques (interactivity OR layers OR multiple views)
- Coherent narrative connecting all visualizations
Grade: 35 points across 5 categories + optional 5-point live presentation bonus
Tools: Any visualization tool (Tableau, Vega-Lite, Python, R, D3.js, etc.)
Data: Use your own datasets or public data - NOT instructor-provided assignment datasets
Key checklist: Each visualization needs meaningful title, source citation, proper labels, appropriate color use, and at least one sophisticated element (interactive/layered/concatenated)
📖 Read below for complete requirements, grading rubric, and submission details
Assignment Overview
Create a portfolio of data visualizations that demonstrates your mastery of the principles learned throughout this course. Your portfolio should showcase your ability to transform data into clear, effective visual communication.
Core Philosophy: We value well-crafted, sophisticated visualizations that clearly convey useful information. Quality and depth matter more than quantity.
What You Need To Do
Create a portfolio containing minimum 5 visualizations that demonstrate your understanding of:
- Data and task abstraction (Munzner's framework from Week 2)
- Visual encoding principles
- Chart selection and design rationale
- Color theory and accessibility
- Technical implementation skills
Submit:
-
ZIP archive (
Portfolio_[LastName]_[FirstName].zip) containing:index.html(your portfolio - template provided)- All datasets referenced in your visualizations
- AI usage documentation (if applicable - see policy below)
-
Video presentation (5-7 minutes):
- Walkthrough of your portfolio
- Highlight key visualizations and design decisions
- Discuss what you learned
- Upload to YouTube/Google Drive and embed link in your index.html
Technical Requirements
Dataset Requirements
❌ Prohibited:
- Instructor-provided datasets from assignments (Bikeshare, World Energy, OWID Population)
- Default tutorial datasets (vega-datasets, Tableau samples, Iris, Titanic, etc.)
✅ Allowed:
- Datasets YOU brought to previous assignments (reuse is fine)
- Makeover Monday datasets not used in class
- Your own research/work data
- Government open data portals
- Kaggle datasets (non-tutorial)
- Domain-specific professional datasets
You may reuse your own previous assignment work IF you brought your own data.
All data sources must be cited with links where possible.
Tools & Technologies
You have complete freedom to use any visualization tool(s):
- Vega-Lite (learned in class)
- Tableau Public (learned in class)
- Python libraries (Altair, Matplotlib, Plotly, etc.)
- R (ggplot2, etc.)
- D3.js, Observable notebooks
- Any other professional visualization tools
Tool choice does NOT affect grading. Mastery of one tool is equally valid as using multiple tools. Use what works best for your visualizations.
Critical requirement: Source code must be available. For Tableau, visualizations must be published in YOUR Tableau Public account (not mine, not a classmate's).
Refining Previous Work
You may include enhanced work from Assignments 2-4, IF:
- You brought your own dataset (not instructor-provided data)
- You've significantly improved it: new analytical question, different chart type, added interactivity/layers, substantially better documentation
Simply resubmitting assignment work with minor label changes will not earn full credit.
Visualization Quality Checklist
Every visualization must include (or explicitly explain why not applicable):
✅ Meaningful title - Not "Chart 1" but "GDP Growth Rates in Eastern Europe, 2010-2020"
✅ Source citation - Where the data came from, with link if possible
✅ Appropriate labels - Axis labels, value labels where needed, units specified
✅ Legends when necessary - If using color/size/shape encoding, provide legend
✅ Appropriate use of color - Color is used meaningfully and corresponds to the data type (categorical / sequential / diverging / highlight)
✅ At least ONE of these:
- Interactive elements (filters, tooltips, brushing/linking)
- Layered marks (multiple mark types composed together)
- Concatenated views (small multiples, hconcat/vconcat, or dashboard composition)
If any checklist item doesn't apply to your visualization, you must explain why in your documentation.
Example: "No legend needed because color encodes the same information shown on the x-axis labels."
Portfolio Structure
We've provided a template HTML file (see course materials). Your portfolio should include:
Introduction Section (3-5 sentences)
Explain your portfolio theme - the common question or domain you're exploring. This is required for full points.
A "theme" means: A common question or domain explored through multiple visualizations.
Good themes:
- "Economic inequality in Ukraine" - 5 visualizations exploring different dimensions
- "My company's user behavior" - 5 visualizations answering related business questions
- "Climate change impacts" - 5 visualizations showing different evidence
Not a theme:
- 5 random visualizations on unrelated topics
- "Visualizations I made for class" without connecting narrative
For EACH Visualization:
- Context & Question (1-2 sentences)
- What analytical question does this address?
- Why is this question important?
- Design Decisions (2-3 sentences)
- Chart type selection and rationale
- Visual encoding choices (position, color, size, etc.)
- What alternatives did you consider and why did you choose this approach?
- What are the strengths and tradeoffs of your choice?
- The Visualization Itself
- Must render and work properly
- Must meet quality checklist (or explain exceptions)
- Key Insights (2-3 sentences)
- What patterns, trends, or outliers did you discover?
- What should viewers take away from this visualization?
- Data Source Citation
- Where did the data come from?
- Include links if possible
Grading Rubric (35 points)
Technical Execution (10 points)
All visualizations render correctly, proper tool usage, source code available
- 10 pts: Perfect execution, no errors
- 8 pts: Minor issues (one viz loads slowly, small formatting glitch)
- 6 pts: Multiple problems affecting experience
- <6 pts: Significant technical failures
Source code requirement: For Tableau, visualizations must be in YOUR Tableau Public account. For code-based tools, include specifications in your HTML. (you can use LLMs to aid if not sure about how to format code in HTML)
Theoretical Understanding (10 points)
Data/Task Abstraction (5 pts): Clear analytical questions, appropriate data transformations
Design Rationale (5 pts): Justified chart selections, discussion of alternatives and tradeoffs, addresses "why this chart type?"
Visual Communication (5 points)
Clarity & professional documentation
- Visualizations clearly convey useful information
- Proper titles, labels, legends (per checklist)
- All sources cited
- Clean, professional presentation
Complexity & Polish (5 points)
Majority of visualizations meet the quality checklist
- 5 pts: All 5+ visualizations meet checklist requirements
- 4 pts: 4 visualizations meet full checklist
- 3 pts: 3 visualizations meet full checklist
- <3 pts: Fewer than 3 meet requirements
Coherent Portfolio Theme (5 points)
Portfolio tells a unified story
- 5 pts: Clear theme, explicit connections between visualizations, coherent narrative in introduction
- 3-4 pts: Weak theme or connections not clearly articulated
- 0-2 pts: No discernible theme, random collection of visualizations
Additional Deductions:
- Video >7 minutes: -2 points
- Video <4 minutes: -2 points
- Missing or inaccessible video: -5 points
- Missing AI documentation (if AI was used): -5 points
- Fewer than 5 visualizations: Up to -10 points depending on severity
Bonus: Live Portfolio Presentation (+5 points)
Optional: Present your portfolio live during Week 8 classes.
Requirements:
- 10-minute presentation + 5-minute Q&A
- Must opt-in by end of Week 7 via Slack
- Present live (no pre-recorded videos)
- Demonstrate your portfolio and discuss design choices
This is separate from the theme requirement. The live presentation is a bonus opportunity for students who want to showcase their work and potentially recover points from earlier assignments.
Limited slots available - first come, first served after opt-in.
Video Presentation Requirements
Purpose: The video serves as documentation of your work and ensures we can evaluate your portfolio even if technical rendering issues occur. Duration: 5-7 minutes (videos under 4 minutes or over 7 minutes will be penalized)
Content to cover:
- Introduction (30-60 seconds): Your portfolio theme and approach
- Visualization walkthrough (3-5 minutes):
- You may highlight 2-3 key visualizations in depth, OR
- Briefly cover all 5+ visualizations
- Focus on design decisions and key insights
- Reflection (30-60 seconds): What you learned
Technical: Upload to YouTube or Google Drive storage and include the link prominently in your index.html (for example, in the Introduction section or create a dedicated "Video Presentation" section at the top). If uploading to Google Drive, make sure to enable external access to your video!
Deployment to GitHub Pages (Optional)
We provide instructions for deploying your portfolio to GitHub Pages, making it publicly accessible. This is completely optional and NOT graded - I recognize this is a data visualization course, not a web development course.
Benefits of deploying:
- Shareable portfolio link for your CV/LinkedIn
- Professional online presence
- Easy to show potential employers
You can also run everything locally if you prefer. Deployment guide is provided separately in the repository.
Submission Checklist
Before submitting, verify:
Portfolio Content:
- Minimum 5 visualizations included
- All visualizations render and work properly
- Each visualization meets quality checklist (or explains why not)
- Introduction section explains portfolio theme
- Each visualization includes: Context, Design Decisions, Insights, Data Source
- All data sources cited with links where possible
Technical Files:
index.htmlfile at root of archive- All referenced datasets included in archive
- File named correctly:
Portfolio_[LastName]_[FirstName].zip
Video Presentation:
- 5-7 minutes duration
- Covers portfolio theme, key visualizations, and learnings
- Link included in index.html
Documentation:
- AI usage documented if applicable (shared conversation links or PDFs)
Optional:
- Opted in for live presentation (by end of Week 7)
- Deployed to GitHub Pages
Academic Integrity & AI Usage
AI Tools Policy
All AI usage must be documented:
- Include links to shared LLM conversations, OR
- Attach PDF exports of your conversations
- Create a folder in your ZIP called
AI_Documentationwith these files
Make it trivial for graders to access your AI usage history.
Remember: AI can help with code syntax or brainstorming, but design decisions and analytical insights must reflect YOUR understanding of visualization principles.
Originality
- Do not copy visualizations from online sources
- Do not submit someone else's work
- If reusing existing visualizations from previous assignments, document this clearly
- Your analysis and design decisions must be your own
Final Reminder
The goal isn't to impress with technical wizardry (though that's welcome).
The goal is to demonstrate that you can:
- Transform data into clear, effective visual communication
- Apply systematic design principles
- Make justified choices about visual encodings
- Reveal insights that tell compelling stories with evidence
Your portfolio should show that you understand WHY certain visualizations work better than others, and that you can create professional-quality work that communicates effectively.
Good luck, and we look forward to seeing your portfolios!