BDI 513 - Data Storytelling
Program-level details: See program/curriculum.md
Draft syllabus submitted 2026-05-01 for T&L review (Brook Corwin / Emily Ziegler). Ron confirmed textbook-first approach (Mar 25, 2026).
⚠️ Known drift (2026-08-15 audit): Ron’s live Box roster (
bdi513/sync/activity-roster.md, synced 2026-08-13) shows a materially different design from the narrative below: the final project is introduced as a personal data story for the portfolio (individual, submitted Module 7, 20 pts), Module 8 presentations are ungraded feedback sessions (no graded oral defense on the roster), grading runs through McGraw-Hill Connect quizzes/labs + group critique videos (“Graded by CAs”, some “Graded by Bongo”), and the point structure differs from the Assessment Summary below. Treat the sync roster as the fresher source for structure and points; the assessment/rubric/milestone sections below are the earlier program-side design pending reconciliation with Ron. The absent oral defense also needs checking against the program’s oral-component commitment (assessment_strategy §0) — flagged in the 2026-08-15 audit, not auto-resolved.
Live LD team data (module items, item types, point values): see
bdi513/sync/— auto-synced from Box ~4x/day (scripts/box-autosync.py). Source:Instructional Activity Roster.xlsx(edited by Ron Guymon; formerlyVideo Roster.xlsx). ACourse Map.xlsxnow also exists in Box — its CLOs/assessment will sync once populated.
Credits: 4 Semester: Fall 2026, Weeks 5-12 (straddles both halves of the semester) Instructor: Ron Guymon
Course Catalog Description
Once a researcher or a practitioner has completed the analyses of their data, they may assume that it is a simple process to communicate their findings to relevant stakeholders, but this is almost always an incorrect assumption. Proper data communication and storytelling begins even before data are analyzed and there are proven strategies to better connect the story behind and from the data to relevant stakeholders, especially within the context of business practice. This course will focus on helping students better position themselves to successfully tell the persuasive story flowing from their data.
Course Vision
Students master data visualization and narrative storytelling to communicate insights from business and public datasets. Students learn to ask questions of data, explore answers visually, and craft compelling narratives that drive decision-making. AI tools are used throughout as co-authors for accessing data, refining plots, creating dashboards, and polishing narratives.
Content Delivery Approach
Ron’s co-authored McGraw-Hill textbook is the primary conceptual content delivery mechanism for this course. The textbook covers visualization theory, design principles, and storytelling frameworks.
Textbook: Richardson, V., Guymon, R., Richardson, J. (2026) Data Visualization: Storytelling with Impact. McGraw-Hill. Platform: McGraw-Hill Connect (eBook + practice problems, labs, video tutorials, datasets). Student cost: ~$70 via Affordable Access (cost included in tuition) or $102 standalone. Affordable Access enrollment requires official BDI 513 course number (blocked on Lorena providing it).
This means:
- Conceptual videos are overview/transition only — introducing each week’s topic and connecting it to prior courses. No need for full-length lecture recordings on concepts already covered in the textbook.
- 2-3 screen capture videos per week — demonstrating how to use AI + Python together for visualization tasks (studio prep material). ~16-24 total across 8 weeks.
- McGraw-Hill online platform integrated with Canvas for reading assignments and exercises.
- Total recorded content target: well under the 60-90 min/week program standard, since the textbook carries the conceptual load.
Learning Outcomes (L-C-E Framework)
Literacy (Foundational Awareness)
- L1: Explain principles of effective data visualization and narrative communication
- L2: Identify the story in a dataset (trends, anomalies, comparisons)
- L3: Recognize when storytelling is effective vs. misleading
Competency (Applied Skills)
- C1: Create compelling data stories that translate analysis into business insight
- C2: Use AI to refine narratives, generate chart captions, and summarize data
- C3: Build interactive dashboards and infographics using Python and Power BI
- C4: Present findings to a live audience with clear explanations and compelling visuals
Expertise (Advanced Application)
- E1: Lead data-driven decision-making through strategic storytelling
- E2: Critique visualization choices in published reports and suggest improvements
- E3: Design multi-part data stories with sub-narratives that reinforce the main message
Part 1 (Weeks 1-4, 2 credits) — Visualize the Data
| Week | Topic | Key Concepts | Learning Outcomes |
|---|---|---|---|
| 1 | Addressing business analytics questions using data visualizations (Ch 1) | Anscombe’s quartet, use of AI in visualization, types and purposes of data visualizations | LO 1.1 Explain why visualizations are important tools for performing and reporting business analytics results. LO 1.2 Evaluate software tools and assess their ability to acquire, prepare, analyze, and visualize data. LO 1.3 Describe how AI has and will affect the generation and use of visualizations. |
| 2 | Master the Data: Data qualities and types (Ch 2) | Data cleaning tasks using real market data, data quality assessment | Milestone: Dataset selection + initial exploration. LO 2.1 Outline data qualities needed for useful visualizations. LO 2.2 Identify common data-cleaning and preparation tasks. LO 2.3–2.4 Describe appropriate visualizations for categorical and numerical data types. |
| 3 | Master the Data: Data visualization design principles (Ch 3 & 8) | Gestalt principles, accessibility guidelines, design checklist, ethical visualizations | LO 3.1 Describe visualization fundamentals using Gestalt principles. LO 3.2 Define ethics in visualizations (persuasion vs. manipulation). LO 3.3 Describe data and charting considerations for ethical visualizations. LO 3.4 Explain how to make visualizations more accessible. LO 3.5 Apply a visualization checklist to evaluate effectiveness. |
| 4 | Perform the Analysis: Exploratory and explanatory visualizations (Ch 4 & 5) | Descriptive, diagnostic, predictive, and prescriptive analytics visualizations | Milestone: Exploratory visualizations + preliminary analysis. LO 4.1–4.4 Explain how visualizations serve as descriptive, diagnostic, predictive, and prescriptive analytic techniques. |
Part 2 (Weeks 5-8, 2 credits) — Tell the Story
| Week | Topic | Key Concepts | Learning Outcomes |
|---|---|---|---|
| 5 | Share the Story: The importance of storytelling (Ch 6) | Elements of a data story, refining visualizations for narrative | LO 5.1 Describe how explanatory visualizations fit into data stories. LO 5.2 Identify the elements of effective data stories. LO 5.3 Evaluate audience needs and expectations. LO 5.4 Refine an explanatory visualization. |
| 6 | Share the Story: Dashboards and infographics (Ch 7) | Dashboard design considerations, infographic best practices | Milestone: Draft narrative + dashboard prototype. LO 6.1 Explore the purposes of dashboards. LO 6.2 Identify common design decisions in dashboards. LO 6.3–6.4 Assess design decisions for exploratory and explanatory dashboards. |
| 7 | Alternative visualizations (Ch 9) | Visualizing quantities, ranges, observations, dimensions, and comparisons | LO 7.1 Recognize reasons why traditional visualizations can fall short. LO 7.2–7.5 Examine alternative plots for quantities, ranges, observations, dimensions, and comparisons. |
| 8 | Final team presentations (Ch 10) | Team video + written analysis, peer evaluation, oral defense | LO 8.1 Practice presenting a compelling data story. LO 8.2 Practice evaluating data stories. |
Team Project (threaded Weeks 1-8)
Each team of 3 identifies one or more datasets, analyzes the data, and creates a data story. Individual milestones build toward the final team deliverable. Students also submit an individual GitHub portfolio piece.
Project Milestones:
- Week 2: Dataset selection + initial exploration notebook (individual)
- Week 4: Exploratory visualizations + preliminary analysis (individual, Part 1 checkpoint)
- Week 6: Draft narrative + dashboard prototype (individual)
- Week 8: Final team presentation (video + written analysis + oral defense) + individual GitHub portfolio submission
Rubric (5 dimensions):
| Dimension | Excellent (A) | Proficient (B) | Developing (C) |
|---|---|---|---|
| Data Exploration | Systematic, creative questions, discovers non-obvious insights | Explores key areas, some surface-level | Shallow exploration |
| Visualizations | Compelling, precise, story-driven, accessible | Clear and correct | Confusing or cluttered |
| Narrative Arc | Coherent story with beginning/middle/end, business context | Logical flow, some disconnects | Disjointed or missing context |
| Dashboard/Infographic | Professional, intuitive, interactive | Functional, mostly clear | Cluttered or static |
| Presentation | Clear, confident, handles questions well | Adequate, minor stumbles | Unclear or unprepared |
AI Integration
Throughout the course:
- Use AI to access and summarize data (e.g., earnings calls, reports)
- AI-generated chart captions and key insights
- AI as “editor” for narrative refinement (“Make this paragraph more compelling”)
- AI for making forecasts and generating key findings
- Use AI to create and refine dashboard layouts
AI Attribution: Students document all AI tool usage in project submissions (which tools, what prompts, how outputs were modified).
Assessment Summary
| Component | Weight | v3 Bucket | Notes |
|---|---|---|---|
| Weekly assignments | 30% | ICA | Practice exercises, visualization critiques, peer feedback on drafts. Canvas Live Session engagement folded in. |
| Project milestones (Weeks 2, 4, 6) | 25% | Project — Team Deliverables | Progressive deliverables toward final project |
| Final project deliverable (Week 8) | 15% | Project — Team Deliverables | Team video + written analysis + GitHub portfolio submission |
| Oral defense (Week 8) | 20% | Project — Individual Deliverables | Live Q&A on team project — individual grade, no AI permitted |
| Peer evaluation (Peerceptiv) | 10% | Engagement | Wk 4 formative 2% + Wk 8 summative 8% |
No traditional exam. Assessment through weekly practice, progressive project milestones, and final presentation with oral defense.
AI Usage Levels (AIAS)
| Assessment | AIAS Level | AI Permitted |
|---|---|---|
| Weekly assignments | 1 | AI for exploration during exercises and critiques |
| Project milestones (Weeks 2, 4, 6) | 2 | AI for data exploration, chart captions, narrative drafting — with attribution |
| Final project deliverable (Week 8) | 3 | AI as collaborator for narrative refinement, polish, slide generation — with full disclosure |
| Oral defense (Week 8) | 0 | No AI |
| Studio sessions (ungraded — output folds into milestones) | 1 | AI for exploration during exercises |
Technology Stack
- Visualization: Python (matplotlib, seaborn, plotly, streamlit, dash); Power BI Desktop (academic license or via Citrix) (instructor-confirmed 2026-05-01)
- Data: pandas, numpy for exploration and cleaning
- APIs: Data access via public APIs (specific APIs TBD based on student datasets)
- IDE: VS Code with GitHub Copilot; Google Colab (browser alternative)
- Notebooks: Jupyter Notebooks (via Colab or VS Code)
- AI: Claude, ChatGPT, Gemini, or similar for data access, narrative refinement, chart captions, summarization
- Presentation: Streamlit/Dash dashboards, Power BI dashboards, video recording tools, Zoom (for live oral defense)
- Version Control: GitHub
Prerequisites
- Basic Python familiarity (plotting with matplotlib, loading data with pandas) — BADM 554 runs concurrently in Weeks 1-8 and covers these skills
- Interest in business data (no specific domain expertise required — students are encouraged to bring datasets from their current or prior industry)
- Willingness to develop presentation skills (no prior presentation experience assumed — the course builds this progressively)
Pedagogical Notes for Faculty
Design suggestions grounded in program research — not requirements. Adapt to your course and teaching style. Full references in reference/articles/.
The scenic route (cognitive friction) Data storytelling is where the “scenic route” principle matters most. When students draft their own narrative before asking AI to refine it (the pre-AI → AI-mediated → post-AI sequence), they build the prediction that makes the AI’s alternative framings surprising and educational. If AI writes the first draft, students skip the struggle that makes the revision meaningful. The Week 6 milestone (draft narrative + dashboard prototype) is a natural pre-AI moment: students should have their story before AI polishes it. → Machulla (2026), Vendrell & Johnston (2026)
The IKEA effect (completion matters) The project milestone pipeline (Weeks 2→4→6→8) is a series of small completions building toward a big one. Each milestone should feel like a finished thing — a working notebook, a viewable dashboard, a presentable story — not just a progress report. The oral defense at Week 8 is the final completion signal where effort converts to demonstrated competence. Students who build the full arc value the result more than those who assembled pieces. → Norton, Mochon & Ariely (2012)
The Push-Back Protocol for AI-refined narratives When students use AI as a narrative “editor” (AIAS 2-3), there’s a risk they accept AI’s framing uncritically — especially when AI produces fluent, confident prose. Consider teaching the Push-Back Protocol: demand evidence → surface assumptions → request alternatives → stress-test → synthesize. A structured challenge to AI’s narrative suggestions builds the critical evaluation skill that distinguishes a data storyteller from someone who lets AI tell the story. → Means (2025, “Push-Back Protocol”)
Provenance as storytelling skill In a world of AI-generated content, the ability to show where data came from, what choices were made, and what was left out becomes a professional differentiator. Data lineage and good storytelling are the same skill: both create prediction errors by revealing something the audience didn’t expect. Students who document their data sourcing, cleaning decisions, and visualization trade-offs are building a provenance habit that will matter in their careers. → Machulla (2026), Furze (2026)
Scaffolding critical thinking through visualization critique The weekly visualization critiques (assignments) are a natural application of the scaffolding framework: students analyze independently (what’s wrong with this chart?), then consult AI (what does AI say is wrong?), then compare and reflect (where did my critique and AI’s critique diverge, and why?). This three-phase pattern turns a routine assignment into a metacognitive exercise. → Vendrell & Johnston (2026), Means (2026, “Scaffold That Teaches”)
Attack your assessments Before the semester starts, have a confident AI user attempt each major assignment using current AI tools. AI is already very good at generating plausible data narratives and polished visualizations. Where can AI produce a convincing story without genuine data understanding? Those are the spots to add pre-AI phases or shift weight toward the oral defense. → Furze (2026)
Session-by-Session Outline (Ron Guymon, 2026-08-31)
Source: BDI 513 Outline.html (attached to rnguymon@illinois.edu email 2026-08-31; Ron requested this be added to course documentation). Details may change. Cross-reference with the activity-roster sync for the most current item list.
Overall approach: Live Sessions use discussion questions, guest speakers, and Python lab demonstrations. Each Live Session will have a companion Python demo video. Project Studios are hands-on with AI, each with a companion AI demo video.
Module 1 — Addressing Business Analytic Questions with Data Visualizations
Live Session: 2026-09-22 (Tuesday)
- Course intro and AMPS model; value of data visualizations (Groove sankey diagram)
- Lab 1.3: Scatterplot to identify fixed, variable, and mixed costs (with Python)
Videos to create (M1 Live): Installing Python and setting up VS Code; Lab 1.1 Anscombe’s quartet/datasaurus dozen
Assignment: Lab 1.2 Python — Column charts, line charts, and pie charts for Amazon sales and earnings (AIAS Level 2)
Project Studio: 2026-09-24 (Thursday)
- Introduce the final project and related assignments
- Activity: Explore examples of good data visualizations and portfolios
Videos to create (M1 Studio): Getting started with AI in VS Code; Getting started with Claude Code, Gemini CLI, Codex
Module 2 — Data Qualities and Types
Live Session: 2026-09-29 (Tuesday)
- Data prep techniques in Python; data sources
- Activity: Baked Apple sample data from MBA 561 Module 3 live session
Videos to create (M2 Live): Lab 2.1 identifying and working with different data types
Assignment: Lab 2.1 Python — How to clean and preprocess data (AIAS Level 2)
Project Studio: 2026-10-01 (Thursday)
- Demo: Ron’s racing line chart visualization of tool usage
- Activity: Use EDA tools to identify a dataset for the final project
Videos to create (M2 Studio): Pygwalker; AutoEDA
Assignment: Project Milestone 1 — Dataset selection and initial exploration (5 pts, AIAS Level 3)
Module 3 — Data Viz Design Principles and Ethics
Live Session: 2026-10-06 (Tuesday) — Guest Speaker: Drake Loud (ML Engineering)
- Guest: using analytics to create value, communicating findings to non-analysts, converting results into persuasive findings, pros/cons of AI
- Gestalt principles; accessibility (Matplotlib styles); ethics
- Labs 3.5 (rating charts using a checklist) and 3.3 (stacked bar/donut charts)
Videos to create (M3 Live): Matplotlib intro; stacked and grouped bar charts in Matplotlib
Assignment: Lab 8.1 — Cherry Picking (AIAS Level 2)
Project Studio: 2026-10-08 (Thursday)
- Activity: Remix static data visualizations (three options provided: Newsweek approval rating map; Anthropic capability/usage chart; NVIDIA earnings chart)
Videos to create (M3 Studio): Openstreet data maps with rayshading
Group Assignment Video: Remix a data visualization and record a video (9 pts, AIAS Level 3) — Three remixes, each reproducible with Python; ~6-8 min video evaluating each remix + data/IPYNB/HTML files.
Module 4 — Exploratory and Explanatory Visualizations
Live Session: 2026-10-13 (Tuesday)
- Exploratory for descriptive/diagnostic/predictive/prescriptive purposes
- Lab 4.6: Scatter plots (advertising vs. sales); Lab 4.3: Looking for fraud in accounts payable
Videos to create (M4 Live): Lab 5.6 comparing sensitivity of profits using conditional formatting
Assignment: Lab 5.1 — Showing earnings predictions using line charts and time series (AIAS Level 2)
Project Studio: 2026-10-15 (Thursday)
- Activity: ChoiceCoach remix using Google AI Studio
Videos to create (M4 Studio): Introduction to Google AI Studio
Assignment: Project Milestone 2 — Exploratory visualizations and preliminary analyses (5 pts, AIAS Level 3)
Module 5 — Storytelling
Live Session: 2026-10-20 (Tuesday) — Guest Speaker: Pablo Smith (Graphic Artist)
- Guest: background, communicating findings
- Elements of a data story; audience considerations; refining a visualization
- Lab 6.2: Creating a data story with multiple line charts
Videos to create (M5 Live): Lab 6.1 adding story elements to a visualization of sales performance
Assignment: Lab 6.3 — Sharing a story with a bubble chart (AIAS Level 2)
Project Studio: 2026-10-22 (Thursday)
- Watch Hans Rosling videos; recreate animated Rosling plots using Gapminder data
Videos to create (M5 Studio): Animated plots with Plotly; animated plots with Matplotlib
Group Assignment Video: Extended Hans Rosling data story (10 pts, AIAS Level 3) — Teams tell an updated/extended Rosling bubble chart story (life expectancy, GDP, fertility through most recent year), extended with an additional variable, country annotation, or alternative chart type. ~6-8 min video + data/code files.
Module 6 — Dashboards
Live Session: 2026-10-27 (Tuesday) — Guest Speaker: Shangruff Raina (Tableau Ambassador)
- Guest: creating dashboards, guiding principles
- Purposes of exploratory vs. explanatory dashboards
- Lab 7.1: Exploratory analysis dashboard for where to add a data center
Videos to create (M6 Live): Introduction to Streamlit; Introduction to Dash
Assignment: Lab 7.3 — Exploratory evaluation dashboard for evaluating performance (AIAS Level 2)
Project Studio: 2026-10-29 (Thursday)
- Evaluate scrollytelling dashboards; identify a topic for a scrollytelling dashboard
Videos to create (M6 Studio): D3 interactive scrollytelling dashboard demos
Group Assignment Video: Create a dashboard (10 pts, AIAS Level 3) — Exploratory dashboard or infographic, built with AI assistance. ~6-8 min video + code/data files.
Module 7 — Alternative Visualizations
Live Session: 2026-11-03 (Tuesday) — Guest Speaker: Abby Kaplan (Data Scientist)
- Guest: communicating findings to broad audiences, recommendations
- Reasons for alternative visualizations
- Lab 9.1: Animated plot of cell phone adoption
Videos to create (M7 Live): Explaining results of logistic regression
Assignment: Lab 9.3 — Pair plot of NFTs (AIAS Level 2)
Project Studio: 2026-11-05 (Thursday)
- Lab 9.2: Treemap; S&P 500 FinViz treemap recreation; Problem 9.3: network plot exploration
Videos to create (M7 Studio): Using an interactive treemap for variance analysis
Assignment: Project — Individual Video (20 pts, AIAS Level 3)
Assignment: Peer Evaluation — of one group assignment in M6 (5 pts, AIAS Level 1)
Module 8 — Presentations
Live Session: 2026-11-10 (Tuesday) — Guest Speaker: Aaron Becker
- Guest: favorite data stories/movies, what makes a good data story, process for converting data to effective story, pros/cons of AI
- Presentations
Assignment: Peer Evaluation — of final projects for three peers (5 pts, AIAS Level 1)
Project Studio: 2026-11-12 (Thursday)
- Presentations
Guest speakers confirmed: Drake Loud (M3), Pablo Smith (M5), Shangruff Raina (M6), Abby Kaplan (M7), Aaron Becker (M8). As of 2026-08-31, consent forms sent to all five; two have confirmed they signed.
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