syllabus
Capstone in Data Science
DS190, Fall 2026
Jo Hardin 2351 Estella jo.hardin@pomona.edu
Class: Tuesdays, 2:45-4pm, Estella 2141
Office Hours: (Estella 2351)
Monday: 1:30-4:00pm
Tuesday: 9-11am
Thursday: 1:30-3pm
The Course.
Capstone in Data Science exists as a way to support the data science minor capstone courses. The capstone project is outside of statistics, mathematics, and computer science, emphasizing work in aligned disciplines. Students will bring together the work they have done in data science with problems in other disciplines, and they will consider the ethical implications of their questions and their work. Prerequisite: all DS minor core courses including statistics, linear algebra, computer science, data science, and ethics in data science.
Anonymous Feedback As someone who is constantly learning and growing in many ways, I welcome your feedback about the course, the classroom dynamics, or anything else you’d like me to know. There is a link to an anonymous feedback form on the landing page of our Canvas webpage. Please provide me with feedback at any time!
Student Learning Outcomes.
- Statistical & Computational Proficiency: Students will develop expertise and apply their knowledge in statistical analysis, probability, and computational concepts relevant to data science, including efficiency and data structures.
- Application of Computational Methods: Students will demonstrate the ability to apply algorithmic, mathematical, and scientific reasoning to computational problems using a programming language.
- Ethical Decision-Making: Students will understand and practice ethical philosophies and frameworks for data-driven decision-making and evaluating scientific claims.
- Effective Communication: Students will clearly and persuasively communicate data-driven analyses using literate programming and collaborate effectively with stakeholders across disciplines.
The Data Science capstone project should:
- carry out a study and communicate results from an extensive data-driven project that is related to domain specific challenges; and
- demonstrate competency in applying at least one type of advanced data-analytic method such as (not limited to):
- modeling a process (e.g., generalized linear models, Bayesian analysis, advanced probability theory and stochastic processes, non-linear models, machine learning, big data analysis, econometrics, or statistical computing)
- advanced study-design (e.g., creating a computational online study with sophisticated design to deal with non-independence)
- advanced data visualization (e.g., creating a dashboard)
- advanced computational data curation (e.g., scraping multiple websites and using regular expressions); and
- be written with scripting code (i.e., not pull-down menus) using literate programming (data + code + results + narrative) and version control (e.g., GitHub);
- include a discussion of ethical issues that came up along with any solutions you used to address the ethical issues; and
- focus on a question originating from or responding to a domain outside of statistics, mathematics, and computer science.
Diversity and Inclusion Statement.
(adapted from Monica Linden, Brown University):
In an ideal world, science would be objective. However, much of science is subjective and is historically built on a small subset of privileged voices. In this class, we will make an effort to recognize how science (and statistics!) has played a role in both understanding diversity as well as in promoting systems of power and privilege. I acknowledge that it is possible that there may be both overt and covert biases in the material due to the lens with which it was written, even though the material is primarily of a scientific nature. Integrating a diverse set of experiences is important for a more comprehensive understanding of science. I would like to discuss issues of diversity in statistics as part of the course from time to time.
Please contact me if you have any suggestions to improve the quality of the course materials.
Furthermore, I would like to create a learning environment for my students that supports a diversity of thoughts, perspectives and experiences, and honors your identities (including race, gender, class, sexuality, religion, ability, etc.) To help accomplish this:
- If you have a name and/or set of pronouns that differ from those that appear in your official records, please let me know!
- If you feel like your performance in the class is being impacted by your experiences outside of class, please don’t hesitate to come and talk with me. You can also relay information to me via your mentors. I want to be a resource for you. If you prefer to speak with someone outside of the course, the math liaisons, Dean of Students, or QSC staff are all excellent resources. I (like many people) am still in the process of learning about diverse perspectives and identities. If something was said in class (by anyone) that made you feel uncomfortable, please talk to me about it. As a participant in course discussions, you should also strive to honor the diversity of your classmates.
Daily.
In class: no phones or computers.
In class time will consist of two types of class sessions. Some of our class sessions will consist of working through the logistics surrounding your projects. For example, some days we will talk about the projects themselves, some days we will talk about how to turn in assignments, and some days you will be presenting. During 5 of our class sessions, we will talk about ethics in data science through the lens of a discipline (engineering, philosophy, linguistics, and ecology).
There is an expectation that you attend every class meeting and stay engaged in our discussions. On the ethical data science discussion days, you will have reading that you should do before coming to class.
Writing.
The final data science capstone project is a written paper. During the semester, you will have parts of the paper due along the way. Ideally, when you get to the end of the semester, the written part will come naturally by putting together the parts from earlier in the semester. You should not leave all the writing to the end.
Reading.
There will be expected reading on each of the five days when we discuss ethical data science. The readings will be posted on the front page of the course webpage. Some of the readings are hyper-linked. Others are available at the Claremont Colleges library, and you will need to login to the library to access the reading.
Presenting.
Each of the presentations will be given by way of you describing pre-prepared slides. You may not read from your phone or any papers during your presentation. Your slides will act as your notes, you should not need additional notes.
Academic Honesty:
Throughout the semester, you will be challenged, and you may find yourself stuck. Every single one of us has been there, I promise. Below, I’ve provided Pomona’s academic honesty policy. But before the policy, I’ve given some thoughts on cheating which I have taken from Nick Ball’s CHEM 147 Collective (thank you, Prof Ball!). Prof Ball gives us all something to think about when we are learning in a classroom as well as on our journey to become scientists and professionals:
If you find yourself in a situation where “cheating” seems like the only option, please come talk to me. We will figure this out together.
Pomona’s Academic Honesty Policy
The College expects students to understand and adhere to basic standards of honesty and academic integrity. These standards include but are not limited to the following:
- In projects and assignments (including homework) prepared independently, students never intentionally represent the ideas or the language of others as their own, examples include but are not limited to plagiarism, failing to use citations, unapproved use of artificial intelligence and resubmitted personal work for another course.
- Students do not destroy or alter either the work of other students or the educational resources and materials of the College.
- Students neither give nor receive assistance with examinations.
- Students do not represent work completed for one course as original work for another or deliberately disregard course rules and regulations.
- In laboratory or research projects involving the collection of data, students accurately report data observed and do not alter or fabricate data for any reason
Advice:
Please email and / or set up a time to talk if you have any questions about or difficulty with the material, the computing, or the course. Talk to me as soon as possible if you find yourself struggling. The material will build on itself, so it will be much easier to catch up if the concepts get clarified earlier rather than later. This semester is going to be fun. Let’s do it.
Philosophy on AI use
The goals of the course including learning core content and becoming skilled at using analysis tools accurately and effectively. While sometimes quite helpful, there are ways that AI can get in the way of the learning, for example:
- Learning takes struggle, and generative AI usually removes the struggle.
- Generative AI will often be wrong, maybe because it doesn’t understand the prompt or maybe because it is hallucinating.
- Learning takes practice, particularly by building muscles related to creative and independent thinking. Generative AI removes the practice.
There are also ways that AI can be ethically problematic, for example:
- Generative AI queries use large amounts of resources.
- Generative AI is based on intellectual property (often illegally) taken from scholars, artists, and journalists.
- The rise of generative AI impacts the number of jobs available to people like you.
I encourage you to reflect on your use of AI in this class and elsewhere. Is your use consistent with the learning that brings you to a place like Pomona College? Are you considering the ethical ramifications associated with using AI? See the academic honesty part of the syllabus for generative AI policy.
Grades.
Your final grade will be calculated using the following points system below. There are 120 total points. 90% of 120 is 108 points. Assignments turned in within one week of the original due date can earn up to half of the points.
topic: 5 pts
annotated bibliography: 5 pts
1st outline: 5 pts
section draft: 5 pts
introduction or 2nd section: 5 pts
ethics component: 5 pts
project draft: 10 pts
final write-up: 25 pts
1st presentation: 10 pts
final presentation: 25 pts
active participation in ethics conversations: 10 pts attendence: 10 pts
Footnotes
Many of the ideas below are from https://provost.tufts.edu/celt/online-resources/artificial-intelligence/ai-syllabus-statements/↩︎