# 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, and `VIDEO_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:** 1. **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) 2. **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: 1. **Context & Question** (1-2 sentences) - What analytical question does this address? - Why is this question important? 2. **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? 3. **The Visualization Itself** - Must render and work properly - Must meet quality checklist (or explain exceptions) 4. **Key Insights** (2-3 sentences) - What patterns, trends, or outliers did you discover? - What should viewers take away from this visualization? 5. **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:** 1. **Introduction** (30-60 seconds): Your portfolio theme and approach 2. **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 3. **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.html` file 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_Documentation` with 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!