BDI 513 - Data Storytelling
Program-level details: See program/CURRICULUM.md
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.
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 |
|---|---|---|
| 1 | Addressing business analytics questions using data visualizations | Anscombe’s quartet, use of AI in visualization, types and purposes of data visualizations |
| 2 | Master the Data: Data qualities and types | Data cleaning tasks using real market data, data quality assessment |
| 3 | Master the Data: Data visualization design principles | Gestalt principles, accessibility guidelines, design checklist, ethical visualizations |
| 4 | Perform the Analysis: Exploratory and explanatory visualizations | Descriptive, diagnostic, predictive, and prescriptive analytics visualizations |
Part 2 (Weeks 5-8, 2 credits) — Tell the Story
| Week | Topic | Key Concepts |
|---|---|---|
| 5 | Share the Story: The importance of storytelling | Elements of a data story, refining visualizations for narrative |
| 6 | Share the Story: Dashboards and infographics | Dashboard design considerations, infographic best practices |
| 7 | Alternative visualizations | Visualizing quantities, ranges, observations, dimensions, and comparisons |
| 8 | Capstone case presentations (team) | Team video + written analysis, peer evaluation, oral defense |
Semester-Long Project
Each student identifies one or more datasets, analyzes the data, and creates a data story that they can share in their portfolio. The Week 8 capstone presentation is a team project (teams of 3-4).
Progressive Deliverables:
- 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 + live 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 | Notes |
|---|---|---|
| Semester-long project (progressive) | 60% | Individual: dataset analysis → visualizations → narrative |
| Capstone team presentation (Week 8) | 30% | Team: video + written analysis + oral defense (20% of capstone weight is oral defense), peer evaluation |
| Studio participation | 10% | Weekly attendance + peer feedback |
No traditional exam. Assessment through progressive project deliverables and final presentation.
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)
- Notebook: Jupyter, Google Colab
- 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)
Prerequisites
- Comfortable with Python (or willing to learn alongside course — BADM 554 runs concurrently in Weeks 1-8)
- Interest in business data (no specific domain expertise required)
- Comfortable presenting to an audience
Last Updated: February 2026