← MSBAi Home

BDI 513 - Data Storytelling

Program-level details: See program/curriculum.md

Status: In Development

Instructor actively building content; structure stable. Ron confirmed textbook-first approach (Mar 25, 2026).

Credits: 4 Semester: Fall 2026, Weeks 5-12 (straddles both halves of the semester) Instructor: Ron

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. 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

Part 2 (Weeks 5-8, 2 credits) — Tell the Story

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):

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

No traditional exam. Assessment through weekly practice, progressive project milestones, and final presentation with oral defense.

AI Usage Levels (AIAS)

Technology Stack

• Visualization: Python (matplotlib, seaborn, plotly, streamlit, dash), Power BI Desktop (academic license)
• 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, or similar for data access, narrative refinement, chart captions, summarization
• Presentation: 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)