Data Storytelling Project - Guideline

DANL 310: Data Visualization and Presentation

Author

Affiliation

Byeong-Hak Choe

SUNY Geneseo

Published

April 8, 2026

Overview

For the final project in DANL 310: Data Visualization and Presentation, you will create a data storytelling project and present it in class.

You may work alone or form a group with one other student.
The size of a group must be either one or two.
You may choose any dataset for the project.
If your story involves technical concepts, scientific knowledge, or other background information that may not be familiar to a general audience, you should provide enough background explanation so that your audience can understand the story.
Your project should tell an interesting and coherent story using:
- descriptive statistics,
- data transformation, and
- data visualization.
A literature review is not required.
A statistical model is not required.

The main goal of this project is to show that you can take raw data, transform it thoughtfully, visualize it clearly, and build a meaningful narrative that helps an audience understand something interesting, surprising, or important.

Presentation

Presentation Dates

Presentations will take place during class time on:

May 4, or
May 6.

The presentation order will be determined randomly.

Presentation Length

If you work alone, you will have 7 minutes.
If you work in a two-person group, your group will have 14 minutes total.
- Each member should speak for roughly 7 minutes.

To ensure fairness and equal participation, each student is expected to contribute meaningfully to the presentation.

What to Present

Your presentation should focus on the story you are telling with the data.

A strong presentation will usually include the following:

Title and Topic Introduction
- Introduce your topic clearly.
- Explain why the topic is interesting or worth exploring.
Data Source and Context
- Explain where the data come from.
- Briefly describe what the dataset contains and why it is suitable for your story.
Data Transformation and Descriptive Statistics
- Show how you cleaned, filtered, reshaped, summarized, or otherwise transformed the data.
- Highlight descriptive statistics that help the audience understand the data.
Data Visualization and Storytelling
- Present figures that support a clear narrative.
- Explain what each figure shows and how it advances the story.
- Do not just display charts; interpret them.
Main Takeaways
- Conclude with the key insight or message of your project.
- Explain why the story matters in a broader business, social, economic, cultural, or practical context.

Presentation Slides

You may prepare presentation slides for class. If you use slides:

keep them visually clean and readable,
avoid overcrowding with too much text,
use figures that are easy to interpret,
and make sure the slides support your oral explanation rather than replace it.

If your project uses techniques beyond what we covered in class, you should be prepared to explain them briefly and clearly.

Website Requirement

Each project must be published on the personal GitHub website of each member.

The required published components are:

a project webpage,
a Shiny app using the project data, and
a dashboard using the project data.
If you work alone, all required components must be posted on your own personal GitHub website.
If you work in a two-person group, the same project webpage, Shiny app, and dashboard should be posted on each member’s personal GitHub website.

Your project webpage should be written as a Quarto document (.qmd) and rendered as HTML.

Important Notes

The webpage is a central part of the project.
The webpage should be more complete than your in-class presentation.
The webpage must clearly show your code, narrative, descriptive statistics, and visualizations.
The Shiny app and dashboard must use the same project data and should help users interact with or explore the story in a clear way.
Since this is a data storytelling project, the emphasis is on clarity, coherence, transformation, and visualization rather than on formal statistical modeling.

Website Due

The personal GitHub website is due by May 14, 2026, at 11:59 PM.

Structure of the Project Webpage

Your webpage does not need to follow a rigid template, but a strong project will usually include the following sections. In addition to the webpage, your published Shiny app and dashboard should be built from the same project data and posted on each member’s personal GitHub website.

1. Introduction

Introduce the topic.
Explain what makes the topic interesting.
State the main question, theme, or story of the project.

2. Data

Describe the source of the data.
Explain the unit of observation and key variables.
Provide enough context so a reader can understand the dataset.

3. Data Transformation and Descriptive Statistics

Show how the data were cleaned or prepared.
Explain important transformations such as filtering, grouping, reshaping, recoding, joining, or creating new variables.
Include descriptive statistics that help set up the story.

4. Data Visualization and Storytelling

Present visualizations in a logical order.
Use each figure to develop the overall story.
Explain the meaning of each chart clearly.
Connect the figures together so the webpage feels like one coherent narrative rather than a disconnected collection of plots.

5. Conclusion

Summarize the key findings.
State the main insight of the story.
Briefly discuss why the result matters.

6. References (Optional)

You may include references if helpful.
A formal literature review is not required.

General Expectations

What You Should Aim For

Your project should demonstrate that you can:

find or choose a dataset that supports an interesting story,
transform data appropriately,
summarize data using descriptive statistics,
create effective visualizations,
and communicate insights clearly to an audience.

What Is Not Required

The following are not required for this project:

literature review,
hypothesis testing,
regression modeling,
machine learning models,
or other formal statistical modeling.

You may include additional analysis if it helps your story, but it is not necessary.

Technical Expectations

Your Quarto document should be well organized.
Code should run without major errors.
Figures should be readable and appropriately labeled.
The webpage should be polished enough for a public-facing GitHub website.
Use color, annotation, and layout carefully to improve readability.

Submission

In-Class Presentation

You will present in class on May 4 or May 6.

Project Deliverables on GitHub Website

The final published project materials must include all of the following on each member’s personal GitHub website:

a project webpage based on a Quarto (.qmd) document rendered to HTML,
a Shiny app using the project data, and
a dashboard using the project data.

Detailed submission logistics may be announced separately on Brightspace.

Rubric

Presentation Rubric

Attribute	Very Deficient (1)	Somewhat Deficient (2)	Acceptable (3)	Very Good (4)	Outstanding (5)
1. Quality of Data Transformation and Descriptive Statistics	- No transformation or cleaning applied - Very poor data transformation - Contains significant errors	- Minimal transformation or cleaning - Basic data transformation with errors - Contains several errors	- Basic transformation applied - Adequate data transformation - Contains minor errors	- Effective transformation - Thorough data transformation - Data is accurate	- Advanced transformation - Exceptional data transformation - Data is impeccable
2. Quality of Data Visualization	- Visualizations are missing or unclear - Misrepresents data	- Visualizations are basic and lack clarity - Some misrepresentation	- Visualizations are clear and accurate - Data is appropriately represented	- Visualizations are insightful and enhance understanding - Data is accurately represented	- Visualizations are highly creative and compelling - Data representation is impeccable
3. Effectiveness of Data Storytelling	- No narrative or storyline - Insights are absent or irrelevant - Fails to engage the audience	- Weak narrative structure - Insights are superficial - Minimal audience engagement	- Clear narrative present - Insights are relevant - Audience is adequately engaged	- Compelling narrative - Insights are significant - Engages audience effectively	- Exceptional and captivating narrative - Insights are profound and impactful - Audience is highly engaged
4. Quality of Slides and Visual Materials	- Very poorly organized - Difficult to read and understand - Numerous errors present	- Somewhat disorganized - Some slides are unclear - Several errors present	- Well organized - Mostly clear and understandable - Few errors present	- Very well organized - Clear and visually appealing - Very few errors	- Exceptionally well organized - Highly clear and visually compelling - No errors
5. Quality of Team Presentation	- Presentation is disjointed - Poor team coordination - Unable to address questions	- Lacks flow - Some coordination issues - Difficulty with several questions	- Cohesive presentation - Team works well together - Addresses most questions adequately	- Engaging presentation - Team is well-coordinated - Addresses almost all questions professionally	- Highly engaging and polished presentation - Excellent team coordination - Addresses all questions expertly

Webpage Rubric

For the webpage, the following criteria from the project rubric will be emphasized:

quality of the project question or story,
quality of data visualization,
quality of proposed business/economic or practical interpretation,
quality of writing, and
quality of Quarto usage.

Because this is a data storytelling project, a formal literature review and a formal modeling section are not required.

Attribute	Very Deficient (1)	Somewhat Deficient (2)	Acceptable (3)	Very Good (4)	Outstanding (5)
1. Quality of project question or story	Not stated, or very unclear Entirely derivative No meaningful story	Stated somewhat confusingly Slightly interesting, but underdeveloped Weak story	Stated explicitly Somewhat interesting and coherent Story is understandable	Stated explicitly and clearly Clearly interesting and creative Story is well developed	Articulated very clearly Highly interesting and creative Story is compelling and memorable
2. Quality of data visualization	Very poorly visualized Unclear Figures are difficult to interpret	Somewhat visualized Somewhat unclear Reader has difficulty interpreting figures	Mostly well visualized Mostly clear visualization Acceptably interpretable	Well organized Well thought-out visualization Almost all figures are clearly interpretable	Very well visualized Outstanding visualization All figures are clearly interpretable
3. Quality of interpretation and significance	Demonstrates little or no critical thinking Little understanding of why the results matter Interpretation is weak or missing	Rudimentary critical thinking Somewhat shaky interpretation Misses important implications	Average critical thinking Adequate interpretation of the findings Discusses why results matter	Mature critical thinking Clear understanding of the meaning of the findings Strong discussion of implications	Sophisticated critical thinking Superior interpretation of the findings Highly compelling discussion of implications
4. Quality of writing	Very poorly organized Very difficult to read and understand Teems with typos and grammatical errors	Somewhat disorganized Somewhat difficult to read and understand Numerous typos and grammatical errors	Mostly well organized Mostly easy to read and understand Some typos and grammatical errors	Well organized Easy to read and understand Very few typos or grammatical errors	Very well organized Very easy to read and understand No typos or grammatical errors
5. Quality of Quarto usages	Very poorly organized Teems with redundant warning/error messages from running code Provides inappropriate programming codes	Somewhat disorganized Numerous warning/error messages from running code Misses some important programming codes	Mostly well organized Some warning/error messages from running code Provides appropriate programming codes	Well organized Very few warning/error messages from running code Provides advanced programming codes	Very well organized No warning/error messages from running code Provides highly advanced programming codes

Project Grade

Peer evaluation on group presentation (5%)
Instructor evaluation (95%)
- Descriptive statistics (5%)
- Data transformation (5%)
- Data visualization (20%)
- Data storytelling (20%)
- Presentation slides (10%)
- Presentation (30%)
- Code (10%)

Final Advice

Choose a topic that you genuinely care about. A successful data storytelling project is not just a collection of charts. It is a clear and engaging explanation of something interesting that becomes easier to understand because of thoughtful data transformation, descriptive statistics, and visualization.