Professor: Eric Dunford
Teaching Assistant: Madeline (“Maddie”) Pickens
Teaching Assistant: Chandler Dawson
Class Website: www.ericdunford.com/ppol564
This first course in the core data science sequence teaches Data Science for Public Policy (DSPP) students how to synthesize disparate, possibly unstructured data in order to draw meaningful insights. Topics covered include the fundamentals of object-oriented programming in Python; literate programming; an introduction to algorithms and data types; data wrangling, visualization, and extraction; and an introduction to machine learning methods. In addition, students will be exposed to Git and Github for version control and reproducible research. The objective of the course is to teach students how incorporate data into their decision-making and analysis. No prior programming experience is assumed or required.
Classes will be held on Wednesdays from 6:30pm to 9:00pm in Car Barn 204:
The lecture will be broken up into synchronous and asynchronous components.
Note that this class is scheduled to meet weekly for 2.5 hours. I will do my best to ensure that the asynchronous and synchronous material in combination does not exceed 2.5 hours weekly. Put differently, students will not be required to commit more than 2.5 hours to lecture (on average). This does not include readings, homework and/or coding discussions; rather, bifurcating lecture materials into synchronous and asynchronous components is necessary when learning technical material. Lectures that exceed an 1.5 hours are not effective. Moreover, to best absorb technical concepts, it helps to be able to return to them. The recorded asynchronous lecture material allows students this opportunity to pause, ponder, and repeat.
Five minute breaks will be taken approximately every 40 minutes during the synchronous lecture.
All synchronous lecture material will be recorded and stored on the class Canvas site. Students who are unable to attend the synchronous lecture will be able to review the materials covered in class at a future date. It is the students responsibility to review all lecture materials and to keep pace with the course.
In case we must switch to a virtual classroom due to a COVID-19 outbreak, we will use Zoom (a web-conferencing platform) to hold class each week. Class will meet at its regularly scheduled time each week for synchronous lectures. If you do not have Zoom, you can download it here prior to the start of class.
A link for the synchronous component of the weekly lecture along with a link for virtual office hours is posted on the course website and Canvas. Students will use this link to access the live Zoom call for lecture.
If the link breaks or does not function properly, please check the #general channel on Slack for information regarding the new link. If there is no message regarding a new link, please contact the professor and/or TA via Slack. All synchronous lecture material will be recorded.
The course aims to provide students with the following competencies:
General understanding of python’s object oriented programming syntax and data structures.
Competency using version control (Git/Github).
Learn to manipulate and explore data with Pandas and other tools.
General understanding of analyzing algorithms and data structures.
Learn to extract and process data from structured and unstructured sources.
Learn to use statistical learning approaches to effectively explore and ask questions from data.
Readings: We will rely primarily on the following text for this course.
Vanderplas, J.T., 2016. “Python data science handbook: tools and techniques for developers.” O’Reilly. (Online version: https://jakevdp.github.io/PythonDataScienceHandbook/)
Miller, B. and Ranum, D., 2013. “Problem Solving with Algorithms and Data Structures using Python.” (Online version: https://runestone.academy/runestone/books/published/pythonds/index.html)
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). “An Introduction to Statistical Learning: with Applications in R”. New York: springer.
Additional readings will be posted for each class and can be found on the course website. Most reading material is open source and available via a link on the reading list, otherwise it can be found on Canvas.
Class Website: A class website (www.ericdunford.com/ppol564) will be used throughout the course and should be checked on a regular basis for lecture materials and required readings.
Class Slack Channel: The class also has a dedicated slack channel (ppol-564-fall-2021.slack.com). The channel serves as an open forum to discuss, collaborate, pose problems/questions, and offer solutions. Students are encouraged to pose any questions they have there as this will provide the professor and TA the means of answering the question so that all can see the response. If you’re unfamiliar with, please consult the following start-up tutorial (https://get.slack.help/hc/en-us/articles/218080037-Getting-started-for-new-members). Please follow the invite link to be added to the Slack channel.
Canvas: A Canvas site (http://canvas.georgetown.edu) will be used throughout the course and should be checked on a regular basis for announcements, readings, and assignments. All readings and assignments will be posted on Canvas; they will not be distributed in class or by e-mail. Support for Canvas is available at (202) 687-4949
NOTE: Students are encouraged to run lecture code on their own machines. If you do not have access to a laptop on which you can install python3, please contact the professor and/or TA for assistance. Only python3 will be used in this course.
| Assignment | Percentage of Grade |
|---|---|
| Participation/Attendance | 5% |
| Coding Discussion | 15% |
| Problem sets | 40% |
| Final Project | 40% |
Preparation, Participation and Attendance (5%): It is imperative that students keep up with the asynchronous materials and attend synchronous lectures on time. See the “Participation” section in the Course Policies section for more details.
Coding Discussions (15%): Some weeks there will be a coding problem/prompt/dataset to explore pushed to the class Github repository. Students will be required to submit an original response to the prompt. A point will be awarded for (1) submitting on time and (2) the quality of the submission. “Quality” is defined as an original, well-commented, and clear solution/analysis (i.e. the solution follows the guidelines required for assignments - see below).1
All submission must be posted Sunday 11:59 PM (EST). The schedule for the coding assignments are listed below.
| No. | Coding Discussion Week | Date Assigned |
|---|---|---|
| 1 | Week 2 | September 8 |
| 2 | Week 3 | September 15 |
| 3 | Week 6 | October 6 |
| 4 | Week 10 | November 3 |
| 5 | Week 11 | November 10 |
| 6 | Week 12 | November 17 |
The goal of the coding discussions is to apply a concept learned during the week in a way that helps build a greater level of programming fluency. Programming skills are honed through active usage and repetition. Learning to read other people’s code and detecting issues is vital to successful collaborations in applied work. The point is not to be “right” necessarily but rather to try, learn, and collaborate.
Problem Sets (40%): Students will be assigned four problem sets over the course of the semesters. While you are encouraged to discuss the problem sets with your peers and/or consult online resources, the finished product must be your own work. The goal of the assignment is to reinforce the student’s comprehension of the materials covered in each section. All assignments will be posted Wednesday evening by 9:00PM (i.e., by the scheduled end of class) on the class Canvas site for the weeks marked on the syllabus. Problem sets are due on the date and time posted on Canvas and must be submitted on Canvas. Generally, a week will be allotted to complete each assignment. Late assignments will be penalized a letter grade for every day they are overdue.
The assignments can be in the form of a Jupyter Notebook (.ipynb) or R Markdown (.Rmd). Student’s must submit completed assignments as a rendered .html file to Canvas on the assigned due date. All assignment submissions must adhere to the following guidelines:
The follow schedule lays out when each assignment will be assigned.
| Assignment | Date Assigned | Date Due |
|---|---|---|
| No. 1 | September 22 | September 29 |
| No. 2 | October 13 | October 20 |
| No. 3 | October 20 | October 27 |
| No. 4 | November 24 | December 1 |
Final Project (40%): Data science is an applied field and therefore, it is important that you understand how to conduct a complete analysis from collecting data, to cleaning and analyzing it, to presenting your findings. Toward the end of the semester, you will complete an independent data science project, applying concepts learned throughout the course. The project is composed of three parts: a 2 page project proposal, an in-class presentation, and a 12-page project report. Due dates and breakdowns for the project are as follows:
| Requirement | Due | Length | Percentage |
|---|---|---|---|
| Project Proposal | November 3 | 2 pages (500 words) | 5% |
| Presentation | December 1 | 7 minutes | 10% |
| Project Report | December 16 | 12 pages (3000 words) | 25% |
Students will use Git/Github for version control to track progress made on their analysis. Each student will be required to create a public Github repository and use it to track progress made on the project. Failure to version control one’s work on the project could result in a deduction in points on all components of the project.
Details regarding each aspect of the project will be posted on the course website leading up to the first due date (i.e. the Project Proposal). Until then, we will not discuss the project in class. The reason for this is that students need to reach a basic level of data competency before thinking through a project idea. Thus, discussion of the final project and the development of a project proposal will align with the final portion of the class.
Course grades will be determined according to the following scale:
| Letter | Range |
|---|---|
| A | 95% – 100% |
| A- | 91% – 94% |
| B+ | 87% – 90% |
| B | 84% – 86% |
| B- | 80% – 83% |
| C | 70% – 79% |
| F | < 70% |
Come Prepared.
Do the readings. Think about the readings on their own terms, but also in terms of how the concepts apply to things you’re interested in.
It is expected that students bring their computers to class to partake in computational activities or play with coding being discussed in class. Moreover, students should have all relevant software up and running on their machines.
Ask Questions.
Collaborate.
Start homework early.
Try doing it the hard way.
Participation is required in this course. I define participation as:
I reserve the right to deduct participation points from students who are not participating as expected.
When in-person, a sheet of paper will be made available at the front of the room at the start of every class. Students must write their name on the paper. The paper will be removed 5 minutes after the start of class. Students who walk in late after that point will not have an opportunity to write their name and will be considered absent. This log will be used, in part, to calculate the attendance grade.
When virtual, attendance will be drawn from the Zoom attendance log. The instructor will take attendance at the start of class. Students who join the Zoom call after that point will not be counted.
If absent, each student is responsible to make up the materials missed during a lecture on their own. All lecture notes/lecture materials will be posted on the class website. Thus, students who missed a lecture should reach out to their peers in the class for lecture notes. It is not the responsibility of the Professor/TA to fill absentee students in on any missed content.
Class-relevant and/or coding-related questions, Slack is the preferred method of communication. Please use the general or the relevant channel for these questions.
For private questions concerning the class, email is the preferred method of communication. All email messages must originate from your Georgetown University email account(s). Please use a professional salutation, proper spelling and grammar, and patience in waiting for a response. The professor reserves the right to not respond to emails that are drafted inappropriately. Please email the professor and the TA directly rather than through the Canvas messaging system. Emails sent through CANVAS will be ignored.
I will try my best to respond to all emails/slack questions within 24 hours of being sent during a weekday. I will not respond to emails/slack sent late Friday (after 5:00 pm) or during the weekend until Monday (9:00 am). Please plan accordingly if you have questions regarding current or upcoming assignments. Please address the professor and TA by their last name unless stated otherwise.
When meeting in-person: the use of laptops, tablets, or other mobile devices is permitted only for class-related work. Audio and video recording is not allowed unless prior approval is given by the professor. Please mute all electronic devices during class.
Assignments should be clear, legible, and submitted in the required format. Writing assignments will be graded on the basis of content, logic, analysis, mechanics, organization, and research. Due dates for all assignments will be posted on Canvas and are non-negotiable. Exceptions to this policy will be made only under extremely unusual circumstances and will require valid documentation from the student. Late problem sets will be penalized a letter grade per day.
When reaching out to the professor or teaching assistant regarding a technical question, error, or issue you must demonstrate that you made a good faith effort to debugging/isolate your problem prior to reaching out. In as concise a way as possible, send a record of what you tried to do. The professor/TA is a resource of last resort. As software is continually being refined in data science and new approaches continually emerge and changing, learning how to frame your question and find a similar solution online is a key tool for success in this domain. If you make a diligent effort beforehand to solve your problem, we will do the same in trying to help you figure out a solution.
You should always consult with the TAs regarding your problem before reaching out to the professor.
Increasingly, with the proliferation of certain websites, questions about the ownership of course materials have arisen (and Georgetown is actively working on policies to address these concerns). I consider my syllabus, lectures, videos, handouts, problem sets, and problem set answers to be my intellectual property. I respectfully request that you refrain from sharing my materials in any electronic (or paper) format. You are welcome to save my lectures for your own use, but they should not be posted anywhere. Sharing notes, on an occasional basis, with others in the class is fine as long as they are not posted. Students found in breach of this policy will fail the course.
If you believe you have a disability, then you should contact the Academic Resource Center (arc@georgetown.edu) for further information. The Center is located in the Leavey Center, Suite 335 (202-687-8354). The Academic Resource Center is the campus office responsible for reviewing documentation provided by students with disabilities and for determining reasonable accommodations in accordance with the Americans with Disabilities Act (ASA) and University policies. For more information, go to http://academicsupport.georgetown.edu/disability/.
McCourt School students are expected to uphold the academic policies set forth by Georgetown University and the Graduate School of Arts and Sciences. Students should therefore familiarize themselves with all the rules, regulations, and procedures relevant to their pursuit of a Graduate School degree. The policies are located at: http://grad.georgetown.edu/academics/policies/.
Plagiarism is the intentional or unintentional presentation of another person’s idea or product as one’s own. Plagiarism includes, but is not limited to the following: copying verbatim all or part of someone else’s written work; using phrases, charts, figures, illustrations, code, or mathematical / scientific solutions without citing the source; paraphrasing ideas, conclusions, or research without citing the source; and using all or part of a literary plot, poem, film, musical score, or other artistic product without attributing the work to its creator. In technology, plagiarism is the verbatim use of a code chunk from a peer or third party website to complete an assignment questions. Students can avoid unintentional plagiarism by following carefully accepted scholarly practices. Students who plagiarize will receive a 0 on the plagiarized assignment and may fail the course, if deemed necessary.
Georgetown University promotes respect for all religions. Any student who is unable to attend classes or to participate in any examination, presentation, or assignment on a given day because of the observance of a major religious holiday (see below) or related travel shall be excused and provided with the opportunity to make up, without unreasonable burden, any work that has been missed for this reason and shall not in any other way be penalized for the absence or rescheduled work. Students will remain responsible for all assigned work. Students should notify professors in writing at the beginning of the semester of religious observances that conflict with their classes. The Office of the Provost, in consultation with Campus Ministry and the Registrar, will publish, before classes begin for a given term, a list of major religious holidays likely to affect Georgetown students. The Provost and the Main Campus Executive Faculty encourage faculty to accommodate students whose bona fide religious observances in other ways impede normal participation in a course. Students who cannot be accommodated should discuss the matter with an advising dean.
Please know that as a faculty member I am committed to supporting survivors of sexual misconduct, including relationship violence, sexual harassment and sexual assault. However, university policy also requires me to report any disclosures about sexual misconduct to the Title IX Coordinator, whose role is to coordinate the University’s response to sexual misconduct.
Georgetown has a number of fully confidential professional resources who can provide support and assistance to survivors of sexual assault and other forms of sexual misconduct. These resources include:
Associate Director
Jen Schweer, MA, LPC
Health Education Services for Sexual Assault Response and Prevention
(202) 687-0323
jls242@georgetown.eduErica Shirley
Trauma Specialist
Counseling and Psychiatric Services (CAPS)
(202) 687-6985
els54@georgetown.eduMore information about campus resources and reporting sexual misconduct can be found at http://sexualassault.georgetown.edu.
| Week | Date | Topic | Assignment | Coding Discussion |
|---|---|---|---|---|
| 1 | 1-Sep | Introductions, Installations, and IDEs | ||
| 2 | 8-Sep | Version Control, Workflow, and Reproducibility | X | |
| 3 | 15-Sep | Object-Oriented Programming in Python | X | |
| 4 | 22-Sep | Introduction to Algorithms | Assignment 1 Assigned | |
| 5 | 29-Sep | From Nested Lists to Data Frames | Assignment 1 Due | |
| 6 | 6-Oct | Approaches to Data Manipulation in Python | X | |
| 7 | 13-Oct | Data Visualization and Exploration | Assignment 2 Assigned | |
| 8 | 20-Oct | Drawing from (Un-)Structured Data Sources | Assignment 2 Due; Assignment 3 Assigned | |
| 9 | 27-Oct | Introduction to Statistical Learning | Assignment 3 Due | |
| 10 | 3-Nov | Continuous Outcomes and Linear Regression | Project Proposals Due | X |
| 11 | 10-Nov | Probability, Bayes Theorem, and Classification | X | |
| 12 | 17-Nov | Algorithmic Approaches to Supervised Learning | X | |
| 13 | 24-Nov | Interpretable Machine Learning | Assignment 4 Assigned | |
| 14 | 1-Dec | Project Presentations | Assignment 4 Due |
IMPORTANT: This syllabus is subject to change and may be amended throughout the course to reflect any changes deemed necessary by the professor. Any changes will be announced in class or over Slack.
Students are encouraged to generate a response before looking at the responses of their peers. If a student appears to have copied an answer of another student, we’ll examine the time stamp and only award a quality point to the first entry of the timeline of responses that appear duplicative.↩︎