Syllabus
Course information
Instructor: Prof. Sarah Cassie Burnett
Email: sarah.burnett@gwu.edu
Office Hours: Wednesday 9-11 and by appointment.
Location: Samson Hall Rm. 302
Lecture 10
Classroom: Funger Hall Rm. 221
Teaching Assistant: Haeyeon Jeong
Email: haeyeon.jeong@gwu.edu
Office Hour: Tuesday 11:20-13:20
Location: Samson Hall Rm. 304
Lecture 11
Classroom: Media and Public Affairs Building Rm. B07
Teaching Assistant: Sai Rachana Kandikattu
Email: sairachanak@gwu.edu
Office Hours: Tuesday 16-17:30
Location: Samson Hall Rm. 304
Teaching Assistant: Rasika Nilatkar
Email: rasika.nilatkar@gwu.edu
Office Hours: Thursday 11-12:30
Location: Samson Hall Rm. 304
Credit Hours: 3.0.
Learning Objectives
Develop Proficiency in R Programming: Students will learn the fundamentals of R programming, including basic syntax, data manipulation, and the use of R packages, enabling them to write and execute R scripts effectively.
Master Data Visualization Techniques: Students will gain a deep understanding of the grammar of graphics and best practices in data visualization, allowing them to create clear, accurate, and impactful visual representations of data.
Apply Data Wrangling Techniques: Students will learn to organize, clean, and transform datasets into tidy formats using R, preparing them for analysis by applying grouping, summarizing, and joining operations.
Understand and Implement Statistical Models: Students will learn the principles of statistical modeling, including linear, multiple, and logistic regression, and will apply these models to real-world data to make predictions and informed decisions.
Conduct Hypothesis Testing and Interpret Results: Students will learn the concepts of sampling, uncertainty, and hypothesis testing, enabling them to design research studies, test hypotheses, and draw valid conclusions from data.
Course Topics
| Lectures | Topics |
|---|---|
| Lectures 1–4 | Foundations of Data Science - Getting started - Meet ouR tech stack - R coding basics - Intro to the Tidyverse |
| Lectures 5–7 | Data Visualization - Grammar of graphics - Advanced visualization techniques - Visualizing and communicating results |
| Lectures 8–12 | Data Wrangling - What is tidy data? - Transforming and summarizing data - Joining and tidying data - Cleaning data |
| Lecture 13–17 | Exploratory Data Analysis - Categorical data - Continuous data - Describing distributions |
| Lecture 18–20 | Statistical Inference - Sampling and uncertainty - Single proportion tests - Differences between two groups |
| Lectures 21–27 | Modeling Data - Fitting a line to data - Least squares regression - Dealing with outliers - Multiple linear regression - Model selection and checking assumptions - Logistic regression (introduction and applications) - Interpreting results and interactions - How would we model this today? |
Readings
Students will read approximately 15 pages per week of academic material during the semester. All academic readings will be linked to the course website. There are no textbooks that need to be purchased for this course.
Assignments
- Quizzes (20%)
- Participation (20%)
- Homework Assignments (20%)
- Online Blackboard Quizzes (10%)
- Coding Assignments (10%)
- Final project (40% of final grade)
Quizzes
Students will complete approximately ten graded quizzes. These are designed to check understanding of key data science concepts. I will drop your lowest two quiz scores so you do not have to worry about making up missed quizzes.
Participation
Participation in Data Science for All is expected. Students will earn credit through in-class activities. While there are no drops for missed participation, a syllabus quiz and 1–2 feedback surveys will also count toward this grade and can replace a missed day. Participation makes up a maximum of 20% of the course grade.
Homework Assignments
Students will complete 4–5 coding assignments and routine online quizzes. Coding assignments provide guided practice in R programming and data analysis, while online quizzes reinforce current material and help prepare for in-class quizzes.
Online quizzes will be delivered through Blackboard. They are untimed and allow multiple attempts (with the score averaged) before the deadline. Coding assignments will be released one week before they are due; online quizzes will be available 24 hours before their deadline. Be sure to read instructions early so you have time to ask questions.
Final Project
The course will conclude with a group final project that applies the skills developed throughout the semester. Students will work collaboratively to design and execute an original data analysis, and will present their findings in both a professional written report and a presentation.
Course Grading
The grading scale below maps your final point or percentage total to your final letter grade for the course.
| Range | Letter Grade |
|---|---|
| 94-100 | A |
| 90-93 | A- |
| 87-89 | B+ |
| 84-86 | B |
| 80-83 | B- |
| 77-79 | C+ |
| 74-76 | C |
| 70-73 | C- |
| 67-69 | D+ |
| 64-66 | D |
| 60-63 | D- |
| <59 | F |
Late Work
- Homework is generally due by
MondayTuesday 11:59pm with no lates accepted unless approved by the instructor. - Solutions to assignments will not be posted, though assignment solutions can be discussed during office hours.
- Accommodations are automatically approved for university-approved absences such as sporting events, religious holidays, etc. but these should be provided to the instructor by the end of the second week of the semester.
- The lowest coding assignment, the lowest two in-person quizzes will be dropped, and the lowest two online quiz grades will be dropped.
- The online syllabus quiz and feedback surveys can be used to replace missing participation.
Regrade Policy
Any concerns regarding grading must be raised within 1 week of the graded assignment being returned. After this period, no score changes will be allowed.
Course Help
Office hours
The purpose of office hours is primarily to discuss/clarify course concepts and for us to provide homework-related hints. You can drop in on any of the listed course instructors office hours without letting us know beforehand. Mine will be held in-person, unless there’s an exception. For more private matters, it’s best to make an appointment with me in advance to ensure privacy. For data science advising questions there are additional office hours here.
Include “[DATS 1001]” at the beginning of the subject line of your email. If you have a question related to course materials, email me and the TAs from your lecture. I’ll check my emails every weekday at 8:30 and 16:30. When emailing, address your professors properly. For me, “Prof. Burnett” is preferred.
Data Science Help Desk
The Data Science Program has a help desk located in Samson Hall 304. They will have someone available for tech and coding related help on weekdays from 11:00-16:00 starting after week 3. This will also be where the teaching assistants will hold office hours.
Tutoring
GWU Academic Commons offers peer tutoring. They provide free, one-on-one support from trained undergraduate tutors who have successfully completed the courses they support, including this course. You can book an appointment for peer tutoring here starting Sept. 2.
Policy on AI Tools
You are encouraged to reuse any code snippets provided by me, your TAs or in the course materials, as well as assistance received during office hours and GW tutoring. However, all graded assignments must be your own work. This includes work that is free from unauthorized assistance, including from other people or generative AI tools.
Because we want to maximize your learning in this course, you are not permitted to use AI-based technology (like ChatGPT and Copilot) to write code for your assignments. It is often very clear to instructional staff when AI has been used by a student to solve a problem, especially because the AI algorithm is aware of content beyond the scope of the course. If you use AI to solve homework problems, your work will be reported to the Office Of Student Conduct. For the same reasons as mentioned, you are not allowed to paste code that was found through Googling or found on StackOverflow or Chegg. Solutions that make use of code found on the internet will be reported to the Office Of Student Conduct.
Take care not to use AI or Large Language Models (LLMs) to produce work you want to claim to be your own. One way to think of LLMs is as a tutor or classmate. What would it be appropriate to ask a tutor or classmate for help with? What would you not be permitted ask a tutor or classmate for help with?
You are allowed to discuss homework with other students in the class. However, you are not allowed to share your code or assignments. The work you submit should be original. Unreasonably similar or excessive solutions will be reported to the Office Of Student Conduct.
Prerequisites
Academic
There are no academic prerequisites for this course.
Technological
Configuration and software
To fully participate in our course, you will need regular access to broadband Internet access as well as other technology components. Please consult GW Online’s Technical Requirements and Support for details on recommended configurations and software available to you. You will need to use the following tools and platforms:
Skills
For our course, you should be able to:
- Access and use GW’s Blackboard system.
- Use your GW email for university-related communications per university policy.
- Use productivity software (e.g., Office 365, Google Suite) to collaborate with peers and submit assignments.
- Use web conferencing tools (e.g., Zoom, Webex) to collaborate with peers and me.
- Use a mobile device and/or computer to upload documents, images, and recordings.
- Seek technology help and tools by contacting GW Information Technology | (202)-994-4948 | ithelp@gwu.edu.
If you need assistance with technology tools we’ll use in this course, please visit the Technology Support link in the left navigation menu in our course on Blackboard.
Demonstrating Academic Integrity
All of us in the course will comply with the GW Code of Academic Integrity. It states that “we, the Students, Faculty, Librarians, Staff, and Administration of The George Washington University, believing academic integrity to be central to the mission of the University, commit ourselves to promoting high standards for the integrity of academic work. Commitment to academic integrity upholds educational equity, development, and dissemination of meaningful knowledge, and mutual respect that our community values and nurtures. The George Washington University Code of Academic Integrity is established to further this commitment.”
Academic dishonesty is defined as cheating of any kind, including misrepresenting one’s own work, taking credit for the work of others without crediting them and without appropriate authorization, and the fabrication of information. For details and complete code, see the Code of Academic Integrity. Common examples of academic dishonesty include cheating, fabrication, plagiarism, falsification, forgery of University academic documents, and facilitating academic dishonesty by others. Learn more about avoiding these:
Plagiarism: What is it and how to avoid it from GW Libraries.
Maintaining academic honesty can be a challenging skill to learn. If you have questions about maintaining our course standards, please talk with me early on.
Policies
To make this a meaningful learning experience for everyone, please read and understand the following policies. All GW policies can be found on the GW Office of Ethics, Compliance, and Privacy site. All GW community members are responsible for adhering to and activating in accordance with all university policies. Please contact me if you have any questions.
Accessibility and Accommodations
GW’s Disability Support Services
If you are a student with a disability, or think you may have a disability, you can let me know, and/or you can talk to GW’s Office of Disability Support Services (DSS). DSS works with both students with disabilities and instructors to identify reasonable accommodations. Contact the DSS office at (202) 994-8250, by email on dss@gwu.edu, or in-person in Rome Hall Suite 102 to establish eligibility and to coordinate reasonable accommodations. If you have already been approved for accommodations, please send me your accommodation letter and meet with me so we can develop an implementation plan together.
How are course technology tools accessible to everyone? To find out, access Technology Support Technology Tools Policies in the Blackboard course menu.
Accommodations Beyond Disability
Everyone has different needs for learning. If you don’t have a documented disability but feel that you would benefit from learning support for other reasons, please don’t hesitate to talk to me. If you have substantial non-academic obligations or other concerns (e.g., food insecurity, work, childcare, athletic commitments, language barriers, financial issues, technology access, commuting, etc.) that make learning difficult, please contact me. I’ll keep this information confidential, and together, we can brainstorm ways to meet your needs.
Other Needs
Any student who has difficulty affording groceries or accessing sufficient food to eat every day, or who lacks a safe and stable place to live, and believes this may affect their performance in the course, is urged to contact GW’s Office of Student Financial Assistance for support. Furthermore, please notify me if you are comfortable doing so. Some other resources to support you are found under the course menu item Student Resources and include support for academic achievement and personal well-being. (Adapted from Goldrick-Rab, 2017)
Counseling and Psychological Services
GW’s Health Center offers counseling and psychological services to GW students. Please note that staff is licensed to offer short term therapy to students in Washington, DC, Maryland, and Virginia. If you are living outside these regions, the office may be able to refer you elsewhere. Assistance and referrals 24 hours a day, 365 days a year and can be reached on (202) 994-5300.
The Center provides assistance and referral to address students’ personal, social, career, and study skills problems. Services for students include: crisis and emergency mental health consultations, confidential assessment, counseling services (individual and small group), and referrals.
CAPS Workshops are open to any student. These can be exceptionally valuable and help you build essential skills and cope with common ongoing mental health concerns. Please contact the GW Health Center at (202) 994-5300 for more information.
Religious Observances
As members of the GW community, you have the right to observe religious holidays. University policy requires that students notify their instructors during the first two weeks of the semester if they plan to be absent from class on days of religious observance. For further details, please consult the university policy on religious holiday observance.
Incomplete Grades
Undergraduate students
Incomplete grades may be given to undergraduate students only if for reasons beyond the student’s control (such as medical or family emergency) they are unable to complete the final work of the course. Faculty should not assign an Incomplete grade if not asked by the student.
A contract must be signed by the instructor and the student and filed in the department office. A copy should be submitted to the Academic Advising office in Phillips 107. A student has up to a calendar year to finish the coursework for the class, and when completed a grade change form must be submitted to the Academic Advising office to update the grade.
For further policy and contract information for undergraduate students, please consult with your advisor and also visit the website for Columbian College of Arts and Sciences Academic Advising.
Graduate students
Incomplete grades may be given to graduate students only if for reasons beyond the student’s control (such as medical or family emergency) they are unable to complete the final work of the course. Faculty should not assign an Incomplete grade if not asked by the student.
For further information, please consult with your advisor and complete a CCAS graduate student incomplete grade form.