JMM309_631 Syllabus - Spring 2026
Storytelling with Data Syllabus
- spring 2026
- live
Last modified:
Instructor: Michela Effendie
Email: mte42[at]miami[dot]edu
Class Meetings: Mon & Wed, 3:35 PM – 4:50 PM
Modality: Hybrid (Mon: In-Person / Wed: Online)
Office Hours: Schedule via Calendly or email for alternate times
This syllabus is subject to change. Updates will be posted in this document.
Course Description
This course teaches how to extract meaning from data for more powerful reporting.
Graduate students are expected to exhibit more critical thinking and more complex creative projects.
Course Goals
By the end of this course, you will be able to:
- Find compelling narratives within complex datasets using basic statistics.
- Explain complex topics through clear, step-by-step narratives
- Combine verbal and visual elements into a cohesive story
Tools
All tools used in this class are free or have free versions. While these tools are demonstrated, students are encouraged to experiment with any media (digital or physical) that fits their project.
- Analysis: Google Sheets or Python (Pandas).
- Visualization: Flourish, RAWGraphs.
- Design & Layout: Figma.
Assignments & Grading
Grading
| Step | Weight | Assignment | Task | Points |
|---|---|---|---|---|
| Introduction | 10 Points | Expectations | Submission | 5 |
| Critique | Submission | 5 | ||
| Inspiration Days | 10 Points | Analyze | Submission (4×) | 2.5 |
| Project 1: Customize | 20 Points | Data-driven project | Presentation | 10 |
| Peer Feedback | 5 | |||
| Submission | 5 | |||
| Project 2: Convert | 20 Points | Data-driven project | Presentation | 10 |
| Peer Feedback | 5 | |||
| Submission | 5 | |||
| Project 3: Create | 30 Points | Planning | Presentation | 5 |
| Peer Feedback | 5 | |||
| Data-driven project | Presentation | 10 | ||
| Peer Feedback | 5 | |||
| Submission | 5 | |||
| Final Exam | 10 Points | Self-reflection | Submission | 10 |
Late Submissions: Accepted with a 10% grade deduction.
Participation: Your engagement during exercises, discussions, and feedback contributes to your grade.
Grading Scale:
| Grade | Points |
|---|---|
| A+ | ≥ 97 |
| A | ≥ 93 |
| A- | ≥ 90 |
| B+ | ≥ 87 |
| B | ≥ 83 |
| B- | ≥ 80 |
| C+ | ≥ 77 |
| C | ≥ 70 |
| D+ | ≥ 65 |
| D | ≥ 60 |
| F | ≥ 0 |
I reserve the right to adjust the final grade according to class participation, attendance, overall quality of work, etc.
Presentation day
For each project, students will share their final project with the whole class. For the third project, students will also present their planning beforehand, so they can to get early feedback on their idea. In order to present, students need to sign-up for 5-min presentation slots (instructions will be provided via Blackboard).
Class Calendar
Introduction
| Date | Preparation | Content | Materials |
|---|---|---|---|
| Jan 12 | Set your expectations: discussing goals, tools, timeline & grading | ||
| Jan 14 |
Submit Expectations via Blackboard Watch Question our Data Culture talk by Rahul Bhargava Read I’m a data scientist who is skeptical about data by Andrea Jones-Rooy |
Demystify data and how it might be represented | |
| Jan 19 | No class (Martin Luther King, Jr. Day) | ||
| Jan 21 |
Submit Critique Prepare to discuss it in class Subscribe for free via UM to The New York Times, The Economist, Financial Times, etc |
Discussing what makes data-driven products good (or bad) |
Project 1: Customize
Adding visual, interactive or even sound elements to a written article
| Date | Preparation | Content | Materials |
|---|---|---|---|
| Jan 26 | Read Project 1 Description on Blackboard | ||
| Jan 28 | Read The Visual Variables (based on Jacques Bertin) |
Perception & Design: How we see data. Intro to preattentive attributes (color, size, enclosure) |
Slide deck: Visual Grammar |
| Feb 2 | Intro to Project 1 | ||
| Feb 4 | Submit Inspiration 1 | Discuss submitted inspos and summary statistics | |
| Feb 9 | Prototyping | ||
| Feb 10 | Take note of 1 or 2 questions (conceptual or technical) | 🐞 Debug day (attendance is optional) | |
| Feb 16 | Sign up for presentation slots (½ of class) | Present project & get feedback | |
| Feb 18 | Sign up for presentation slots (½ of class) | Present project & get feedback |
Project 2: Convert
Turning a written article into a video, podcast, poster, XR or art piece etc
| Date | Preparation | Content | Materials |
|---|---|---|---|
| Feb 23 | Submit Project 1 | Intro to Project 2 | |
| Feb 25 | Submit Inspiration 2 | Discuss different ways to think about “medium” | |
| Mar 2 | Discuss different ways to think about “data sources” (e.g., structured × unstructured, input visualization) | ||
| Mar 4 | Take note of 1 or 2 questions (conceptual or technical) | Debug day (attendance is optional) | |
| Mar 9 | No class (Spring Break) | ||
| Mar 11 | No class (Spring Break) | ||
| Mar 16 | Sign up for presentation slots (½ of class) | Present project & get feedback | |
| Mar 18 | Sign up for presentation slots (½ of class) | Present project & get feedback |
Project 3: Create
Finding insights in data and sharing them as a data-driven project
| Date | Preparation | Content | Materials |
|---|---|---|---|
| Mar 23 | Remaining Presentations from Project 2 + Intro to Project 3 | ||
| Mar 25 |
How to find reliable CSVs/APIs. Discussing “Data bias”—who is missing from the dataset and why? |
List of Open Data Sources (Local Miami & Global) | |
| Mar 30 | Submit Project 2 | Finding insights in data (in other words, extracting “headlines” from numbers) |
Sample Data 1
Sample Data 2 |
| Apr 1 | Submit Inspiration 3 | Discuss submitted inspos | |
| Apr 6 | Sign up for presentation slots (½ of class) | Present planning & get feedback | |
| Apr 8 | Sign up for presentation slots (½ of class) | Present planning & get feedback | |
| Apr 13 | Submit Inspiration 4 |
Discuss inspos Intro to visual metaphors in information design |
|
| Apr 15 | Take note of 1 or 2 questions (conceptual or technical) | 🐞 Debug day (attendance is optional) | |
| Apr 20 | Sign up for presentation slots (⅓ of class) | Present project & get feedback | |
| Apr 22 | Sign up for presentation slots (⅓ of class) | Present project & get feedback | |
| Apr 27 | Sign up for presentation slots (⅓ of class) | Present project & get feedback |
Self-Reflection
Instead of a final exam, the last assignment of this course is a self-reflection, submitted via Blackboard. You should reflect on your experience learning about web development – and how it relates to your future pursuits (personal & professional).
| Date | Preparation | Content | Materials |
|---|---|---|---|
| May 6 |
Submit Project 3 Submit Self-Reflection |
No class |
Policies & Resources
This section will include UM’s full policy references, unchanged, as in the reference syllabus:
Attendance Policy
Class attendance is critical to the success of hands-on classes, including class participation in discussions and completion of in-class assignments. All students are responsible for material covered in the classroom regardless of their presence; therefore, check the class Blackboard for announcements, assignment requirements and due dates. Do not email your instructor to find out what has been posted to Blackboard.Religious Holy Day Policy
It is the student’s obligation to provide faculty members with notice of the dates they will be absent for religious holy days, preferably before the beginning of classes but no later than the end of the first three (3) class days. Absences due to observance of religious holy days not pre-arranged within the first three class days may be considered unexcused and there is no obligation to allow any make up work, including examinations. Missing a class due to travel plans associated with a particular religious holy day does not constitute an excused absence. The University’s complete Religious Holy Day Policy can be found in the current UM Bulletin.Academic Integrity
Students in this and all UM courses are bound by the University’s Academic Integrity Policy.AI Use & Documentation
ChatGPT and other Generative Artificial Intelligence (AI) software may be useful tools for enhancing learning, productivity, and creativity. For instance, they can assist with brainstorming, finding information, and creating materials, such as text, images, and other media. However, these tools must be used appropriately and ethically, and you must understand their limitations. In particular, it is important to realize that all AI software has the following limitations:- How output is arrived at is not clear as the internal processes used to produce a particular output within the generative AI cannot be determined.
- AI output is typically based on data harvested from unknown online sources. As such, it may reflect biases that should be acknowledged. AI output may also be inaccurate or entirely fabricated, even if it appears reliable or factual.
- AI evokes a range of intellectual property concerns; sourcing and ownership of information is often unclear and is currently the subject of ongoing litigation.
If you use AI tools in any part of your work, you are responsible for the final product of that work, both academically and in the workforce.
General AI Principles
- AI should help you think, not think for you. AI tools may be used to help generate ideas, frame problems, and perform research. It can be a starting point for your own thought process, analysis, and discovery. Do not use them to do your work for you, e.g., do not enter an assignment question into ChatGPT and copy and paste the response as your answer.
- The use of AI must be open and documented. The use of any AI in the creation of your work must be declared in your submission and explained. Your faculty can provide guidance as to the format and contents of the disclosure.
- Engage with AI Responsibly and Ethically. Engage with AI technologies responsibly, critically evaluating AI-generated outputs and considering potential biases, limitations, and ethical implications in your analysis and discussions. Ensure that the data used for AI applications are obtained and shared responsibly. Never pass off as your own work generated by AI.
- You are 100% responsible for your final product. You are the user; if the AI tool makes a mistake, and you use it, then it’s your mistake. If you don’t know whether a statement about any item in the output is true, then it is your responsibility to research it. If you cannot verify it as factual, you should delete it. You hold full responsibility for AI-generated content. Ideas must be attributed, and sources must be verified.
- These principles are in effect unless the instructor gives you specific guidelines for an assignment or exam. It is your responsibility to ensure you are following the correct guidelines. Not following them will result in a breach of the Academic Integrity Policy.
- Data that are confidential or personal should not be entered into generative AI tools. Putting confidential or personal data into these tools exposes you and others to the loss of important information. Therefore, do not do so. See point 3 above.
- The rules and practices on the use of AI may vary from class to class, discipline to discipline. Do not assume that what is acceptable in a Computer Science class will be acceptable in a Philosophy class. It is the student’s responsibility to stay informed as to the instructor’s expectations. When in doubt, ask.
SoC-Specific Principles for the Use of AI
Please adhere to the following overarching institutional principles for the use of AI systems in any SoC coursework:
- Unless expressly approved by the instructor in writing, AI system outcomes should not be incorporated in final submissions or deliverables. They should solely be employed for process and research purposes.
- Students must properly cite the AI systems and document the pertinent prompts utilized during their process and research in the final assignment outcomes.
- Unless expressly approved by the instructor in writing, refrain from using the names of artists, designers, companies, or brands within the prompts. This is to uphold the artistic integrity of those involved.
- The instructor must ensure equitable access to any AI systems utilized in the course. This means that platforms used by students to meet course requirements must be freely accessible or offered through a UM-provided subscription.
Respect & Civility
The School of Communication is committed to providing a safe, comfortable and inclusive learning environment that promotes a culture of respect and civility for everyone. Disruptive, rude, discriminatory, or disrespectful behavior toward the instructor, guest lecturers, or your fellow students will not be tolerated. Students who exhibit disruptive or uncivil behavior will be required to leave the classroom.Intellectual Property
Pursuant to the University’s Policy on Inventions, Intellectual Property, and Technology Transfer, “courseware” includes course syllabi, assignments, assessments, software, and/or other materials that are first created and made available to students as part of the educational curriculum at the University. Courseware is owned by the faculty member, unless otherwise agreed to beforehand in a written contract between the University and the faculty member. This policy and position have not changed due to recent circumstances and this policy and definitions apply to all means by which the course material is provided.The instructor is the copyright owner of the courseware; individual recordings of the materials on Blackboard and/or of the course sessions are not allowed; and such materials cannot be shared outside the physical or virtual classroom environments.
Plagiarism Statement
Students enrolled in this course are expected to abide by the University of Miami Honor Code. The purpose of the Honor Code is to protect the academic integrity of the University by encouraging consistent ethical behavior in assigned coursework. Academic dishonesty of any kind, for whatever reason, will not be tolerated.No honest student wants to be guilty of the intellectual crime of plagiarism, even unintentionally. Therefore, we provide you with these guidelines so that you don’t accidentally fall into the plagiarism trap.
Plagiarism is the taking of someone else’s words, work, or ideas, and passing them off as a product of your own efforts. Plagiarism may occur when a person fails to place quotation marks around someone else’s exact words, directly rephrasing or paraphrasing someone else’s words while still following the general form of the original, and/or failing to issue the proper citation to one’s source material.
In student papers, plagiarism is often due to…
- turning in someone else’s paper as one’s own
- using another person’s data or ideas without acknowledgment
- failing to cite a written source (printed or internet) of information that you used to collect data or ideas
- copying an author’s exact words and putting them in the paper without quotation marks
- rephrasing an author’s words and failing to cite the source
- copying, rephrasing, or quoting an author’s exact words and citing a source other than where the material was obtained (e.g., citing a primary source when you actually used a secondary source)
- using wording that is very similar to that of the original source, but passing it off as one’s own
The last item is probably the most common problem in student writing. It is still plagiarism if the student uses an author’s key phrases or sentences in a way that implies they are his/her own, even if s/he cites the source.
Well-Being Resources
As you complete your coursework, consider how you can maintain your health and well-being as a top priority. To help you become familiar with the many programs and services available on campus, review the information collected on the Division of Student Affairs Student Well-Being and Resiliency website available at [miami.edu/well-being](http://miami.edu/well-being).Please reach out to any of the resources on the site if you need support throughout the semester.
Recommended Learning Resources:
There is a lot of learning resources out there. This course does not intend to replace any of those.
Instead, we heavily rely on them. Here are my top 3 free learning resources:
- Data Viz Project (list of chart types, with beautiful illustrations)
- Dataphys Gallery (list of data physicalizations, including interactive ones)
- Nightingale (digital magazine about anything data visualization)