About this course

Click here to download a PDF copy of the official course syllabus.

Course Info

*held on Zoom (link available on Blackboard)

**held in SAC 370

Course Learning Objectives

By the end of the semester, you will…

• learn to explore, visualize, and analyze data in a reproducible and shareable manner
• gain experience in data wrangling and cleaning, exploratory data analysis, predictive modeling, and data visualization
• work on problems and case studies inspired by and based on real-world questions and data
• learn to effectively communicate results through written assignments and final project presentation

Course Materials

Texts

This course utilizes three texts that are freely available online or through Villanova’s Library. Hardcopies are also available for purchase.

* Available as an eBook through Villanova’s Library

Software

This course will utilize the statistical software R and RStudio. Students will receive instructions in the first week of class for how download it onto their personal computer.

Hardware

Students are expected to bring a laptop to all class sessions. If access to a laptop is an issue, then please contact the course instructor and an accommodation will be made. This requirement will not prevent students from taking this course.

Course Communication

• Course website: All lecture notes, assignment instructions, an up-to-date schedule, and other course materials may be found on this course website, nova-stat-4380.netlify.app.
• Blackboard will be used to submit assignments and view grades
• Piazza will be used as a help forum to ask questions outside of class (access from Blackboard homepage)
• Email: If you have a personal question that is not suitable for the public HELP forum (e.g. about your grade or a personal accommodation), you are welcome to email me directly. I do my best to respond to emails within 24 hours Monday - Friday. Response time may be slower for emails sent Friday evening through Sunday.

Course design

Much of the course design, activities, and assessments are adapted from Mine Çetinkaya-Rundel’s Data Science in a Box curriculum and Duke University’s open source Introduction to Data Science course under the Creative Commons Attribution Share Alike 4.0 International.

Modern data analysis is inherently tied to coding and computational tools, and coding is learned by doing. To facilitate this type of learning, this course utilizes a flipped learning design. Each week, you will engage with a series of lecture videos [before]{.underline} class on Tuesdays. Then class time will be dedicated to hands-on application exercises and group lab assignments. The activities and assessments are designed to help you develop the foundational skills of a modern data scientist. You will also engage with current events and issues of ethics in the data science community.

Preparation Quizzes

At the beginning of each week, you will engage with a lecture video in Perusall, no later than class-time on Tuesdays.

Application Exercises

The majority of class-time on Tuesdays will be dedicated to working on Application Exercises (AEs) in RStudio, designed to help you practice the new skills, code, and concepts introduced in that week’s lecture videos. AEs are due at the end of class on Tuesdays and serve as your “exit ticket”. AEs are graded on completion; demonstrating that a good faith effort has been made on the assignment will earn full credit. If you are unable to attend class on a Tuesday, you may still submit your AE for half credit. The two lowest AE scores will be dropped at the end of the semester to accommodate occasional absences.

Quizzes

Once per week (usually Thursdays), class time will begin with a brief quiz that assesses your understanding of the code and data science tools learned via the lecture videos and previous assignments. There are no make-up quizzes if you are late or absent, but the two lowest scores are dropped at the end of the semester.

Labs

The majority of class-time on Thursdays will be dedicated to lab assignments. The labs are a more in-depth application of the week’s material that will have you complete scaffolded analyses of a real dataset using RStudio. You will collaborate in randomly assigned teams of ~3, and teams will rotate approximately every 2 weeks. Labs are due by class-time the following Thursday. The lowest lab grade will be dropped at the end of the semester.

Project

The purpose of the final project is to apply what you’ve learned throughout the semester to investigate a real-world social good problem by analyzing a relevant dataset of your choosing. You will identify an issue you care about (e.g. mental health, food insecurity, animal adoption, mass incarceration) and find a dataset that allows you to better understand the problem and/or proposed solutions. The project will be completed in self-assigned teams of 2-3. You will be asked to present your findings in a written report and a poster presentation. See Project tab of course website for more details.

Data Ethics Readings & Community Annotations

Each week, you will read an article or book excerpt that engages with issues related to data ethics and/or data communication. You will engage with the reading via the community annotation tool Perusall. Annotations are due by class-time on Thursdays.

Grading

The final course grade will be calculated as follows:

The final letter grade will be determined based on the following thresholds:

Late work & extension policies

Here is how deadlines work in the real world: they exist and they’re important. However, there’s a certain amount of flexibility with them. If you need a little longer on something, you communicate with whoever has set you the deadline and ask if you can have a few more days. This is usually not a big deal, but if it happens a lot, people will start asking you if everything is all right. That is also how deadlines work in this class. You may communicate with me (via an extension request form, available on the course website) to ask for an extension on anything you need, and that’s mostly fine. If you ask for lots of extensions, we’ll work together to find ways to help you adjust and better keep up with the work in the course. If there are life circumstances that are having a longer-term impact on your academic performance or well-being, come talk to me, and we can work towards a solution and connect you to the support you need.

Important dates

See Project tab for important due dates associated with the course project

Click here for the full Villanova academic calendar.

Course community and policies

Inclusive community

It is my intent that this course models and fosters justice, equity, diversity, and inclusion. We will engage with these values both in content and in practice. Data and statistics can be tools to tell diverse stories and help us learn about the state of the world from a perspective beyond our own lived experience. When used responsibly and with integrity, they can amplify the experiences of vulnerable and historically excluded populations. For example, they can be used to shed light on disparities in our schools, healthcare system, and criminal justice system.

The research questions we ask, the data we collect, and the way we use that data are infused with (often hidden) values about who and what matters in the world. For example, we should examine if and when marginalized people and their experiences are being excluded from our data, particularly when that data is used in countless ways to drive decision-making and inform society about the state of the world.

You will be asked to continually and critically engage with these ideas with each dataset and analysis you encounter. You are expected to engage your peers and new perspectives with curiosity, empathy, and intellectual humility. It is my intent that all students be well-served by this course, that your learning needs are met inside and outside the classroom, and that the diversity that you bring to this class be valued and utilized as a resource and strength.

I (like many people) am continually learning how to honor diverse perspectives and identities. If something was said in class (by me or a peer) that made you feel uncomfortable, please let me know. You will also have the opportunity to express concerns anonymously via check-in surveys. Villanova also encourages community members to submit any campus climate concerns at the following website: https://www1.villanova.edu/university/diversity-inclusion/report-climate-concern.html

Villanova affirms that diversity, equity and inclusion are integral components of the teaching and learning experience and an essential element of the ongoing intellectual, social and spiritual development of every member of the Villanova community. We believe that an inclusive community fosters an understanding and appreciation for diversity among our students, faculty, staff and administrators. We are committed to cultivating an academic environment that is marked by genuine curiosity about different perspectives, ardent receptivity to knowledge generated through intercultural connections, and a genuine sensitivity to the variety of human experiences.

Absences for Religious Holidays

Villanova University makes every reasonable effort to allow members of the community to observe their religious holidays, consistent with the University’s obligations, responsibilities, and policies. Students who expect to miss a class or assignment due to the observance of a religious holiday should discuss the matter with their professors as soon as possible, normally at least two weeks in advance. Absence from classes or examinations for religious reasons does not relieve students from responsibility for any part of the course work required during the absence. https://www1.villanova.edu/villanova/provost/resources/student/policies/religiousholidays.html

Academic integrity

TL;DR: Don’t cheat! And use AI responsibly and within guidelines provided

See full AI / LLM policy at the end of this page. Additionally, please abide by the following as you work on assignments in this course:

• You may discuss individual homework and lab assignments with other students; however, you may not directly share (or copy) code or write-ups with other students.
• Citing code/solutions: Unless explicitly stated otherwise, you may make use of online resources for coding on assignments. However, the work must be primarily your own and may not be completed, in whole or in substantial part, by other humans or chatbots, AI, etc. If you directly use code from an outside source (or use it as inspiration), you must explicitly cite where you obtained the code. Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism.

All students are expected to uphold Villanova’s Academic Integrity Policy and Code. Any incident of academic dishonesty will be reported to the Dean of the College of Liberal Arts and Sciences for disciplinary action. For the College’s statement on Academic Integrity, you should consult the Student Guide to Policies and Procedures. You may view the University’s Academic Integrity Policy and Code, as well as other useful information related to writing papers, at the Academic Integrity Gateway web site: https://library.villanova.edu/research/subject-guides/academicintegrity

Accessibility & Support Services

If there is any portion of this class that is not accessible to you due to course format or challenges with technology, please let me know so we can make appropriate accommodations. It is the policy of Villanova to make reasonable academic accommodations for qualified individuals with disabilities. All students who need accommodations should go to Clockwork for Students via myNOVA to complete the Online Intake or to send accommodation letters to professors. Go to the LSS website http://learningsupportservices.villanova.edu or the ODS website https://www1.villanova.edu/university/student-life/ods.html for registration guidelines and instructions. If you have any questions please contact LSS at 610- 519-5176 or learning.support.services@villanova.edu, or ODS at 610-519-3209 or ods@villanova.edu.

Villanova Resources for Student Wellness

• Villanova Resources and Services: Provides comprehensive services to identify and support students in managing all aspects of well being. If you or a peer are in need of support, visit the website for resources and assistance. Go to https://www1.villanova.edu/university/health-services/well-being/services.html

• University Counseling Center: Offers FREE mental health services to Villanova students. Seek support at https://www1.villanova.edu/university/health-services/counseling-center.html

• Nova Nook: Food & Personal Necessities Support: seeks to provide a safe and discreet space to distribute basic toiletries and food items to students in our community who may struggle to manage these costs on their own. See website for hours and location details.

• Academic & Support Resources: see website for info on comprehensive academic support on campus

If there are life circumstances that are having a long-term impact on your academic performance or well-being, come talk to me, and we can work towards a solution and connect you to the support you need.

AI / LLM Policy (i.e. usage of ChatGPT, Gemini, etc.)

TLDR: you’re responsible for understanding how to solve problems, cite any use of AI

In general, we treat AI-based assistance, such as ChatGPT, the same way we treat collaboration with other people; you are welcome to talk about your ideas with other people, both inside and outside the classroom, as well as with AI-based assistants.

However, all work you submit must be primarily your own, and may not be completed, in whole or in substantial part, by other humans or chatbots, AI, etc. You also must properly acknowledge (cite) any ideas / code / solutions that did not originate from you. In all cases, you are responsible for understanding all work that is turned in and may be periodically asked to orally explain your answers.

I expect you will use AI / LLMs periodically to assist you in this course. Responsible use of AI is not “against the rules” and you should not feel the need to hide it. If/when you use AI while working on an assignment, you are expected to provide the following with your submission:

• A statement acknowledging your use of AI and which tool you used
• A precise description of the prompt(s) you used on which problem(s)

Considerations for responsible AI / LLM usage AI / LLMs are likely to be used in your future workplace and can be an effective tool for the modern statistician / data scientist. However, there are both effective and detrimental ways that LLMs can be used in a learning context. Here are a few things to consider when choosing whether/how to use AI in your coursework:

• AI / LLMs can hallucinate and provide incorrect answers. You must develop your own foundational knowledge of a subject in order to effectively judge and verify whether an LLM’s output is trustworthy.
• AI / LLMs use an enormous amount of energy. In order to be climate-conscious, we need discerning use of AI and should be careful not to over-rely on it when other methods (e.g. Google search, non-AI computational tools, human effort) will suffice.
• Employers are interested in people who can, among other things, achieve Task X (with or without LLMs) correctly and efficiently and who can effectively document and communicate how they achieved Task X so that it can be verified and reproduced by someone else. This motivates the policy described above.
• One of the broader / more existential threats posed by AI is its potential to diminish human connection. Be mindful of how often you are turning to AI for help when you otherwise would be turning to a human. There is value in day-to-day interactions with classmates, tutors, and professors that go beyond efficiently completing an assignment.

Tips for when AI assistance can be especially useful and appropriate

• Debugging code or interpreting error messages
• Clarifying course concepts. For example:
  • “provide me with an intuitive explanation of XYZ”
  • “help me understand why ABC happens in this context”
  • “I’m confused about the difference between ABC and XYZ”
• Deepening understanding of working solutions
• Generating additional practice questions and step-by-step explanations when studying

(Non-exhaustive) scenarios when AI assistance would be inappropriate

• Copying / typing an assigned problem into ChatGPT and having it generate a full solution from start to finish. Assignments are intended to build your general coding and problem solving intuition, and you are responsible for coming up with the steps to solve the problem.
• Copying output from ChatGPT directly into your submission. Just as you should not let a classmate write content directly into your submission, so also should you avoid using AI assistance in such a way that directly adds content to your submission.
• Using AI on an open-ended problem or writing assignment that asks for your reflection, opinion, or meta-cognitive thought-processes. It is considered academic irresponsibility to use AI to generate an answer that does not reflect what you truly think and believe. I am interested in what you think, not what an LLM thinks.
• Submitting an assignment that has ideas, code, or solutions that originated from AI but is not properly cited. This is plagiarism.

If at any point you are unsure whether a particular use-case of AI is appropriate, please ask!