DA 401 - Seminar in Data Analytics

Fall 2024

Your Professor:

Matt Lavin

My Email:


My Office:

Burton D. Morgan Center 411

Office Hours

9-10:30 a.m. W F by appointment, 2-3:30 T group drop-in

Our Classroom:


When We Meet:

3-4:20 p.m. M W

Course Description

DA 401 is a capstone seminar for the Data Analytics major in which students work on independent research projects in a collaborative seminar setting. The seminar is the culmination of the Data Analytics major, a showcase for the problem-driven display of analytic, statistical, and programming skills through an independent research project. It is heavily focused on and invested in communicating data anlytics research in the form of an oral presentation and a written paper that meets the standards of professional and scholarly audiences to a level expected of graduating seniors in data analytics. 

Students' research questions may originate from internship experiences, courses of study at Denison, or other sources, subject to the instructor's approval. In all cases, students’ individual projects will build upon their entire skill set and domain concentration in a complete research project that synthesizes and hones pre-existing skills, develops new project specific techniques, generates deeper domain knowledge, and professionally shares the results through written, visual, and oral communication. Since the topics will vary widely based on student choice, class sessions will resemble workshopping and brainstorming sessions commonplace in research hubs, where peers provide assistance and feedback as projects develop. Assigned readings (to be completed outside of class) will supplement this work by focusing on research design concepts and strategies for effective presentation of quantitaive information. 

Last but not least, significant feedback on writing is a core component of this course. Students must present well, but also engage with their peers in substantive, constructive ways which maximizes the collaborative nature of research. Students are expected to review instructor and peer feedback and incorporate that into their future work.

Prerequisites: For Senior Data Analytics majors only. DA 301, DA 350, CS 181, Math 220, a disciplinary research methods course, and completion of an approved DA summer experience.

Office Hours

I am always happy to see students during my office hours, whether it's to discuss this class, majoring in DA, how I can contribute to your learning at Denison, or your plans for life after graduation (career, graduate school, etc.). Like many professors, I offer mix of in-person appointments (via Google Calendar) and drop-in office hours.

For office hours by appointment, visit my appointment page, where you will see a real-time account of when I am available. You can book the appointment with one or two clicks by selecting any time when I'm listed as available. My standard appointment slots are divided into 15-minute blocks from 9 to 10:30 a.m. on Wednesdays and Fridays. Note that these appointment slots will disappear from my calendar once I've been booked. Please book appointments at least 24 hours in advance.

Drop-in office hours will be held in the DA lab from 2 to 3:30 p.m. on Tuesdays. For these, you will not need an appointment, and I encourage you to drop by or work in or near the lab space whether or not you have a specific question. If you are hoping to ask me a specific question, I will see students in the order they arrive, so there is no guarantee that I will have time for everyone on a given day. In other words, if your question is very specific or time sensitive, it's best to make an appointment. 

If I ever need to cancel by-appointment office hours on a given day (say, for example, if I'm ill), I will update the calendar and email anyone with an appointment. If I ever need to cancel office hours on a given drop-in day, I will post to Canvas or e-mail the entire class.

Additional Norms and Policies

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Here you will find information on required readings, import university policies, and course-specific policies like attendance and cell phone use.

Reading Materials

This class does not have a required textbook. Selected readings will be made available as html or pdf, linked from the course website, and shared via Canvas. You will also be expected to conduct independent research in your topic area and read relevant secondary materials thoroughly.

Computers and Software

Computers: Students are required to provide their own laptops and to install free and open source software on those laptops. Support will be provided by the instructor in the installation of any useful or required software. If at any time you don’t have access to a laptop please contact the instructor and the Data Analytics Program can provide you with a loan from the laptop cart. In class, please use eduroam to connect to the Internet instead of Denison Guest. Please be respectful with your use of laptops and technology in class. I request that you only use them for class related purposes, as I and others may find them distracting (For example, no email or social media should be open in your browser tabs!). Cell phones should be kept silent and put away.
Github, Programming Languages, Software: We will be using git and Github for version control and collaboration. Other than that, this course is largely software agnostic, which is to say that you can use any programming language (R, Python, Javascript, etc.), digital tool (tableau, excel, minitab, etc.), or combination of languages and tools. Your top priority should select tools and approaches you know how to use, and/or tools and approaches that best fit the task. That said, I am assuming that most of you will use R or Python for most of your work, and I've asked you say so in the course survey if you plan to reply heavily on anything else.

Grading and Feedback

Since there are multiple sections of DA 401 every semester, the various instructors work hard to make sure there is approximate parity in terms of content, workload, and expectations. However, it is also true that each professor has their strengths, areas of interest, and priorities, and I'm sure I'm no exception. We'll have to spend some time together for you to get a real sense of what I value, how I grade, etc., but I look forward to that process, and to getting to know you all better more generally.

As a general rule, the expectations in this course are high, and I'm confident you can all do great work. The feedback I provide on assignments is designed to help you get there. My goal is to provide specific, relevant, and honest feedback when I grade your work. This will include constructive criticism, strategies for improvement, and guidance on how students can achieve success. I will not do "compliment sandwiches" just to begin and end on a positive remark, but this means that, when I praise your work, it's an honest (and I think more meaningful) act of praise.

Regarding the major assignment rubric, it is adapted from the standards that the data analytics program uses for all its majors.

Major Assignment Rubric

Item Description
Assignment Process: All materials are turned in on time and in the right place. Assignment directions are followed. Required components are all present and submitted on time.
Attention to Detail: The project is well organized, flows logically, and follows the all formatting guidelines, including attention to proofreading, proper citations, and language that is appropriate to a well-informed, non-technical reader.
Research Question and Research Design: The project has a focused and well defined research question that can be addressed with computational, data-driven analysis. The focal data set and method(s) are appropriate for the research question.
Data, Visuals, and Code: The data are fully described, properly sourced, and presented in appropriate ways. Visuals (tables, charts, graphs) are used effectively to describe multiple aspects of the research project (data, methods, or results). The paper provides sufficient details and/or points to supplementary materials that make the research reproducible by a technical reader (i.e, detailed footnotes, appendices, GitHub, code, etc.)
Data Analysis Methods: The method(s) used to test the research question is justified, validated, and applied appropriately; the student appropriately describes the strengths and weaknesses of the methods used; outside sources are used to justify how the methods are used and interpreted.
Reporting and Interpretation of Results: The results are interpreted correctly and clearly address the research question; the project discusses its limitations, the extent to which it can be generalized, and expansion to further research.
Ethical Considerations: The writing thoughtfully engages any ethical considerations of using the data, methods, and implications of communicating the findings.

Grade Breakdown

Item Percentage Comments
Project Proposal 5  
Domain knowledge assignment 10  
Early results 10  
First draft 15  
Final presentation 10  
Final draft 20  
Scaffolding assignments 15 Includes research design, replication, peer review, and executive summary (full list of assignments on Canvas)
Seminar participation 15 In addition to this 15% of the grade, regular participation and attendance are prerequisites to passing this course. See full attendance policy below.

Late Work

If you have a legitimate emergency such as a serious illness, a mental health emergency, or a death in the family, I will grant an appropriate extension with a new due date. The trade off is that work turned in this way is probably not going end up in my hand when I grade everything else, so it's going to get very sparse feedback. If you miss a deadline entirely without getting an extension, you will automatically lose 10 points off the top of your grade for each day it is late, in addition to any points you lose for the quality of the work. Retroactive and last-minute extensions will not be granted.


Cell phones should be off and put away. Laptops are okay for notes and such, but you should not be messaging, using Facebook, etc. I’ll check screens regularly give you a verbal warning on your first offense. After that, I reserve the right to ask you to leave class and mark you absent if you are creating a distraction.


Attendance is particularly important in a course like this: we often learn as much from the generative discussions we have as from the texts we read. Therefore, it is particularly important that students arrive on time and come prepared to engage with the community we will be building in this classroom. I expect you to attend class every class meeting and I expect you to arrive on time. If you cannot attend class, it is your responsibility to get (from a classmate) all written notes about what we discussed in class, including in-class announcements. As stated above, participation counts for 15% of your final grade for this course, but regular attendance is also a prerequisite to passing this course.

I will allow each student two unexcused absences without penalty. Late arrivals will count as half an absence. Missing class beyond these two free cuts will result in a participation score zero for that day (or a 50 for coming late). If you miss five classes, each unexcused absence thereafter will result in a 5% deduction from your final grade for the course. Note that you may not use a free cut to miss final presentations. If you cut on any other day when a graded, in-class activity is taking place, you will not be permitted to make up that assignment. If you miss class on a day when written work is due, that work must still be turned in on time.

Here's an example of how these policies will work. A student misses class six time without an excuse. He gets two free cuts but, for four of the six absences, he gets a zero for participation. On two of the days, he missed scaffolding assignments, so he gets zeros on both those assignments. Six absences is one absence above the five-absence limit so, in addition to these penalties, his final grade will be lowered by 5 points (out of 100) after all other grading has been tallied.

Being Prepared for Class

Coming to class prepared means that you have the day's reading in hand (printed or digital) and have come to class with a way to take notes (printed or digital). If you are not prepared for class, I reserve the right to grade as if you were absent for that day. Anything due on a given class day is due at the start of class on that day unless otherwise noted on Canvas. 

Disability Resources

If you are a student who feels you may need an accommodation based on the impact of a disability, you should contact me privately as soon as possible to discuss your specific needs. I rely on the Academic Resource Center in 020 Higley Hall to verify the need for reasonable accommodations based on documentation on file in that office.

Academic Integrity

Proposed and developed by Denison students, passed unanimously by DCGA and Denison’s faculty, the Code of Academic Integrity requires that instructors notify the Associate Provost of cases of academic dishonesty. Cases are typically heard by the Academic Integrity Board, which determines whether a violation has occurred, and, if so, its severity and the sanctions. In some circumstances the case may be handled through an Administrative Resolution Procedure. Further, the code makes students responsible for promoting a culture of integrity on campus and acting in instances in which integrity is violated.

Academic honesty, the cornerstone of teaching and learning, lays the foundation for lifelong integrity. Academic dishonesty is intellectual theft. It includes, but is not limited to, providing or receiving assistance in a manner not authorized by the instructor in the creation of work to be submitted for evaluation. This standard applies to all work ranging from daily homework assignments to major exams. Students must clearly cite any sources consulted--not merely for quoted phrases, but also for ideas and information that are not common knowledge. Neither ignorance nor carelessness is an acceptable defense incases of plagiarism. It is the student’s responsibility to follow the appropriate format for citations. Students should ask their instructors for assistance in determining what sorts of materials and assistance are appropriate for assignments and for guidance in citing such materials clearly.

Note on Technology: Unauthorized use of technology (including, but not limited to, artificial intelligence sites and translation programs) in the preparation or submission of academic work can be considered a form of cheating and/or plagiarism. Instructors may at their discretion create assignments that incorporate the use of supporting technologies and will inform students of acceptable uses of technology in their courses. It is the responsibility of the student to ask the instructor for clarification whenever they are unclear about the parameters of a specific assignment and to understand that presenting the work of artificial intelligence as your own constitutes a violation of Denison's Code. Cases of suspected inappropriate use of technology may be submitted to the Academic Integrity Board to initiate an investigation of academic dishonesty. For further information about the Code of Academic Integrity, see https://denison.edu/academics/curriculum/integrity.

Our Commitment to Liberal Arts Education

Denison's mission statement articulates an explicit commitment to liberal arts education. It emphasizes active learning, which defines students as active participants in the leaning process, not passive recipients. Denison seeks to foster self-determination and to demonstrate the transformative power of education. A crucial aspect of this approach is what Denison's mission statement refers to as "a concern for the whole person," which is why the university provides a "living-learning environment" based on individual needs and an overriding concern for community. This community is based on "a firm belief in human dignity and compassion unlimited by cultural, racial, sexual, religious or economic barriers, and directed toward an engagement with the central issues of our time."

In this class, we will discuss inequality directly. In many cases, you will asked to apply quantitative reasoning skills to these subject, which can be difficult because there is always the potential for the available data to complicate or contradict something you may feel very passionate about. In these cases, you should aspire to adopt an attitude of critical skepticism, i.e. wary of claims that are not supported by evidence but potentially willing to be persuaded by evidence if you find it compelling, and willing to give that evidence a fair hearing.

How we treat one another will be a cornerstone of these conversations. Denison's "Guiding Principles" speak of "a community in which individuals respect one another and their environment." Further, "each member of the community possesses a full range of rights and responsibilities. Foremost among these is a commitment to treat each other and the environment with mutual respect, tolerance, and civility." It's easy to treat someone this way when you like them and agree with their ideas, but the real challenge is treating those who differ from us with the same compassion and respect. However, I consider disruptive, deceitful, or hateful behavior to be breaches of these responsibilities. Bullying, trolling, hate speech, and harassment of any kind will not be tolerated.

Discrimination, Sexual Misconduct, and Sexual Assault

Essays, journals, and other coursework submitted for this class are generally considered confidential pursuant to the University’s student record policies. However, students should be aware that University employees are required by University policy to report allegations of discrimination based on sex, gender, gender identity, gender expression, sexual orientation or pregnancy to the Title IX Coordinator or a Deputy Title IX Coordinator. This includes reporting all incidents of sexual misconduct, sexual assault and suspected abuse/neglect of a minor. Further, employees are to report these incidents that occur on campus and/or that involve students at Denison University whenever the employee becomes aware of a possible incident in the course of their employment, including via coursework or advising conversations. There are others on campus to whom you may speak in confidence, including clergy and medical staff and counselors at the Wellness Center. More information on Title IX and the University’s Policy prohibiting sex discrimination, including sexual harassment, sexual misconduct, stalking and retaliation, including support resources, how to report, and prevention and education efforts, can be found at: https://denison.edu/campus/title-ix.


Proposal (5% of total grade)

An early assignment designed to help you establish your research topic, articulate your research question, and refine your research intervention.

Domain knowledge assignment (10% of total grade)

A mini review paper that can stand on its own (not a rundown of your specific study or planned methods), describing the landscape of the field to which your work should apply, or from which your method or idea originates. Includes a reflection on the skills you already have and need to develop in order to carry out your plan.

Early results (10% of total grade)

This short paper should summarize your early findings. The focus is on communicating an early concrete story, effectively translating your visuals and quantitative data and analysis. In order to succeed on this assignment, you will need to demonstrate significant progress on your project analysis, including completion of all data preparation and fully written, functional code.

First draft (15% of total grade)

This draft of your seminar paper should be a complete draft (i.e., all sections written out) but will inevtiably be revised based on peer and instructor feedback.

Final presentation (15% of total grade)

This is an oral presentation (with supprting visual material) that distills the essential ingredients of the research rather than incorporating every detail of it. These overviews should cover the research question, the logic of inquiry, your results, the implications of your work, and any appropriate qualifications to your conclusions.

Final draft (20% of total grade)

Submission of this assignment will include a detailed response to peer review comments (a scaffolding assignment), as well as a fully polished and in every possible sense completed version of your seminar paper.

Scaffolding assignments (15% of total grade)

This includes several assignments that begin with an in-class activity and conclude with a take-home assignment, or vice versa. They include but are not limited to a research design assignment, a written peer review assignment, an executive summary assignment, and a practice presentation. Scaffolding assignments are described in full on Canvas.

Weekly Calendar

Weekly Rhythm

Monday Wednesday Sunday
All weekly readings should be done by this day In-class activities, begin scaffolding assignments Turn in written assignments by midnight

Week 1: Research Questions
(Monday, September 2, 2024 - Wednesday, September 4, 2024)

Readings for Next Monday: "Note-Taking Habits ..." and "The Pen is Mightier" (pdfs on Canvas)

Week 2: Proposals
(Monday, September 9, 2024 - Wednesday, September 11, 2024)

Assignment Due Sunday, September 15: Project Proposal

Readings for Next Monday: Creswell, Chapter 1, "The Selection of a Research Approach"(pdf on Canvas)

Week 3: Project Planning
(Monday, September 16, 2024 - Wednesday, September 18, 2024)

Readings for Next Monday: Creswell Chapter 2, "Review of the Literature" and Chapter 7, "Research Questions and Hypotheses"(pdfs on Canvas)

Week 4: Domain Knowledge
(Monday, September 23, 2024 - Wednesday, September 25, 2024)

Assignment Due Sunday, September 29: Domain Knowledge

Readings for Next Monday: Example article to kick off student selections (I will choose this article and share a pdf on Canvas)

Week 5: Literature Reviews
(Monday, September 30, 2024 - Wednesday, October 2, 2024)

Readings for Next Monday: Student Selection 1

Week 6: Methods
(Monday, October 7, 2024 - Wednesday, October 9, 2024)

Assignment Due Sunday, October 13: Research Design (scaffolding)

Readings for Next Monday: Student Selection 2

Week 7: Data
(Monday, October 14, 2024 - Wednesday, October 16, 2024)

Readings for Next Monday: Student Selection 3

Week 8: Preliminary Results
(Monday, October 21, 2024 - Wednesday, October 23, 2024)

Assignment Due Sunday, October 27: Early Results

For Class on Monday: Bring to class a one-slide presentation with a summary of your preliminary results and at least one data visualization. Plan to present on this visualization for no more than three minutes.

Week 9: Data Visualization
(Monday, October 28, 2024 - Wednesday, October 30, 2024)

Readings for Next Monday: Student Selection 4


Week 10: Written Communication
(Monday, November 4, 2024 - Wednesday, November 6, 2024)

Assignment Due Sunday, November 10: First Draft

Week 11: Writing for an Audience
(Monday, November 11, 2024 - Wednesday, November 13, 2024)

Assignment Due Sunday, November 17: Peer Review (Scaffolding)

Readings for Next Monday: Student Selection 5

Week 12: Reproducibility
(Monday, November 18, 2024 - Wednesday, November 20, 2024)

Assignment Due Sunday, December 1: Replication (Scaffolding)

Week 13: Thanksgiving
(Monday, November 25, 2024 - Friday, November 29, 2024)

Reminder: No Class

Week 14: Big Picture
(Monday, December 2, 2024 - Wednesday, December 4, 2024)

Readings for Next Monday: Student Selection 6

Week 15: Finishing Touches
(Monday, December 9, 2024 - Wednesday, December 11, 2024)

Assignment Due Friday, December 13: Final Drafts 

Week 16: Exam Week
(Monday, December 16, 2024 - Friday, December 20, 2024)

During Our Scheduled Exam Block: Digital posters