Syllabus for PSC 4175: Introduction to Data Science
Head Instructor
Prof. Ryan Weldzius
Email: ryan.weldzius@villanova.edu
Web: ryanweldzius.com
Office Hours
- Tuesdays, 2:30pm-4:30pm Villanova time
- You must make an appointment for office hours here: https://calendly.com/weldzius/officehours
- If you cannot make my office hours, please email me your availability at least 24-hours in advance and we can try to find a time that works.
Course Description
The use of large, quantitative data sets is increasingly central in social science. Whether one seeks to understand political behavior, economic outcomes, or violent conflict, the availability of large quantities of data has changed the study of social phenomena. In this course, students will learn about data acquisition, management, and visualization — what we call data science — to answer exciting questions in the social sciences. Whereas most data-related courses focus exclusively on probability theory, matrix algebra, and/or statistical estimation, our main focus will be on the computational tools of data science. Students will leave the course with the ability to acquire, clean, visualize, and analyze various types of political data using the statistical programming language R, which will set them up for success in future statistical courses (as well as the post-graduation job market). No prior background in statistics is required, but students should be familiar with how to use a computer and have a willingness to learn a variety of data science tools.
The contents of this repository represent a work-in-progress and revisions and edits are likely frequent.
The main text for the course is “R For Data Science” which can be assessed free online here. Note that there are no assigned readings from this book; the material is synthesized in the daily homework assignments. However, should you need another source with more detail, you’ll find most of the topics we cover in this course in this book.
Villanova has an enterprise site license for Microsoft’s Copilot chat application, which is built off of Open A.I. Copilot is available to all faculty, staff, and students here.
Class time and location: Asynchronous (online) Tuesdays; Synchronous (online) Thursdays 2:30-3:45pm.
Required Applications
R and R Studio
This course uses the open-source program R and its user interface R Studio. In order to use this program for all graded assignments (See Week 1), you will need a personal computer (laptop or desktop) capable of running this software. Notebooks, Chromebooks, and most tablets will not suffice. Falvey Library has computers on loan if needed.
Blackboard
This is the course management software used at Villanova University to support course learning. It is clunky, not user-friendly, and is thankfully on its way out soon. For these reasons, I will only utilize Blackboard to post course materials (e.g., additional readings), for you to submit your assignments, and to see your grades.
Campuswire
I have set up a Campuswire workspace for our use this semester to help us better communicate with each other. You will need to create an account and join our workspace by following this link. The Code/PIN can be found on the first email I sent to the class. You are encouraged to adopt these Slack etiquette tips. Most likely, you will utilize a similar communication system at a future job, so use this time wisely as you adopt best practices.
Here is the list of channels you should see upon joining the Campuswire workspace:
Class feed: A space to post questions and respond to other posts.
#announcements: A space for all course announcements.
#general: A space for you to share and discuss stories you’ve seen in the news or on social media that are relevant to our class.
Calendar: Not used. See Schedule.
Files: Not used. See Resources.
Grades: Not used. See Blackboard.
Evaluation & Responsibilities
As with learning any new topic or language, the best strategy is to put in a little effort every day. To this end, you will be assigned homework assignments for each class. These are your primary readings, so take your time working through them. You must submit each homework to Blackboard by Tuesday at 11:59pm on the date they are assigned.
You will be assigned bi-weekly problem sets that will test your ability to apply what you’ve learned in the lectures. These problem sets are assigned on the Monday of each week and are due by 11:59PM Villanova time the following Friday. You are welcome to collaborate on these problem sets, and are encouraged to ask questions on the Class feed on Campuswire.
The final grade is calculated as a weighted average of these components with the following weights:
Homeworks (35 points): There are 14 homeworks over the semester. Each will be worth between 1 and 3 points depending on their length/difficulty. Due on Tuesdays by 11:59pm.
Problem sets (35 points): 5 in total worth 7 points each. There will be 1 extra credit point available on each of the problem sets (occassionaly I’ll include an extra hard question for another potential extra credit point).
Final Project: Worth 20 points (write-up 15 points, presentation and comments 5 points). The final project for the course will be a data analysis project where students will find a dataset of interest, state an interesting research question about that data, and answer this question using that data. Students may work individually, or can work in groups of up to 3 students. Your proposal is due by Week 7 (a short description of your research question, argument, and potential data sources). In the final week of classes, you will present your work to the class in a short 5 minute presentation (recorded). Each student will watch your video and provide one positive comment, one looming question, and one helpful tip to improve the project. Final papers due by Tuesday, December 16 at 5:00pm.
Attendance & Participation: Worth 10 points. You are required to show up to our Thursday classes and participate.
See the table below for a breakdown of the percentages, points, and extra credit.
| Item | Percent | Points | EC | Max |
|---|---|---|---|---|
| pset0 | 0% | 0 | 0 | 0 |
| pset1 | 7% | 7 | 1 | 8 |
| pset2 | 7% | 7 | 1 | 8 |
| pset3 | 7% | 7 | 1 | 8 |
| pset4 | 7% | 7 | 1 | 8 |
| pset5 | 7% | 7 | 1 | 8 |
| Homeworks | 35% | 35 | 0 | 35 |
| Final Project | 20% | 20 | 0 | 20 |
| Attendance/Participation | 10% | 10 | 0 | 10 |
| Totals | 100% | 100 | 5 | 105 |
Letter grades are determined as per the standard Villanova grading system:
| Letter Grade | Score Range |
|---|---|
| A | 94+ |
| A− | 90–93 |
| B+ | 87–89 |
| B | 84–86 |
| B− | 80–83 |
| C+ | 77–79 |
| C | 74–76 |
| C− | 70–73 |
| D+ | 67–69 |
| D | 64–66 |
| D− | 60–63 |
| F | <60 |
Course Policies
Attendance
This course is a mix of asynchronous (Tuesdays) and synchronous (Thursdays, 2:30-3:45pm). You are required to attend all synchronous sessions and participate in the discussion.
Late Assignments
Late homeworks will not be accepted. Every problem set is assigned on a Monday and due on Blackboard by 11:59PM Villanova time on the following Friday. All graded work should be submitted via Blackboard. The problem sets are designed to require no more than a few hours to complete. Late submissions will be penalized 1 point off for each day late. After three days, problem sets will no longer be accepted and will be scored 0.
Academic Honor Code
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. You may view the University’s Academic Integrity Policy and Code for a detailed description.
If a student is found responsible for an academic integrity violation, which results in a grade penalty, they may not WX the course unless they are approved to WX for significant medical reasons. Students applying for a WX based on significant medical reasons, must submit documentation and their request for an exception will be considered.
Collaboration is the heart of data science, but your work on your assignments should be your own. Please be careful not to plagiarize. The above link is a very helpful guide to understanding plagiarism. In particular, while students are invited to work on problem sets together, collaboration is prohibited on the midterm and final exams.
Copilot and related Large Language Models (LLMs) are essential tools in the data scientist’s toolkit, and acceptable resources for completing the assignments and learning concepts at a deeper level. However, graded assignments cannot be generated purely by these tools. All assignments must include a log of the Copilot (or other AI programs) prompts and resulting output used to complete the assignment. If you are found to have used AI to complete an assignment with no citation and printed output, you will receive a zero for that assignment. A second infraction will lead to a failure of the class.
Office for Access & Disability Services (ADS) and Learning Support Services (LSS)
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 ADS 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 ADS at 610-519-3209 or ods@villanova.edu.
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.
Acknowledgements
The contents of this course are influenced by Prof. James H. Bisbee who teaches a similar course at Vanderbilt University. I am indebted to him for making his course materials available. The design of the webpage is inspired by Matthew Blackwell.