Fall 2025
Barnard College
Instructor: Eysa Lee
TAs:
Computing Fellows:
Lectures: Mon/Wed 11:40 AM - 12:55 PM, Diana Center 504
Labs:
Office Hours:
This course and its co-requisite lab course will introduce students to the methods and tools used in data science to obtain insights from data. Students will learn how to analyze data arising from real-world phenomena while mastering critical concepts and skills in computer programming and statistical inference. The course will involve hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. The course is ideal for students looking to increase their digital literacy and expand their use and understanding of computation and data analysis across disciplines. No prior programming or college-level math background is required.
This course is based on UC Berkeley’s Foundation of Data Science course and their Computational and Inferential Thinking textbook. Students will complete lab and homework assignments using cloud-based JupyterHub notebooks that can be accessed in the browser, so no software will need to be installed as part of this course.
This course involves weekly labs, homework assignments, a written midterm exam, and a final project. There is no final exam for this course.
Final grades are based on the instructor's holistic evaluation of your performance and follow Barnard's grading system. As a general guideline, assignments are weighted as follows:
All labs are graded out of 10 points. You receive 5 points for attendance, and 5 points for a fully complete + correct notebook. If your notebook is partially complete / correct but shows effort, you will receive 3 points. To submit your lab, you should ensure that all grader cells have been run (grader.check) and submit a PDF of your notebook to Courseworks.
If you are going to be late for lab or unable to attend, you must email your lab TA in advance. If you don’t come to lab or don’t email the TA in advance, you will receive 0 points for attendance. You are permitted one unexcused absence from lab during the semester.
Any requests for regrades of assignments must be received within 1 week after the grade was received. If you request a regrade, we reserve the right to lower your grade if, upon re-review of your assignment, the original grading was found to be too generous. Any requests for regrades after this timeframe will not be considered.
Assignments may be submitted up to 5 days after the deadline for a penalty:
The first half of the course is largely focused on introducing and practicing Python programming through the formatting and visualization of tabular data. The second half of the course then involves an overview of statistics that can be used to make inferences and predictions from data.
The tentative schedule below is schedule to change.
| Week | Date | Topic | Lab | Assignment |
|---|---|---|---|---|
| 1 | 9/3 | Introduction | ||
| 2 | 9/8 | Cause and Effect | ||
| 9/10 | Python Intro: Expressions & Data Types | Lab 1 | ||
| 3 | 9/15 | Tables | HW 1 (Due 9/24) | |
| 9/17 | Charts | Lab 2 | ||
| 4 | 9/22 | Histograms | HW 2 (Due 10/1) | |
| 9/24 | Hisograms & Charts Continued | Lab 3 | ||
| 5 | 9/29 | Functions & Groups | HW 3 (Due 10/8) | |
| 10/1 | Pivots & Joins | Lab 4 | ||
| 6 | 10/6 | Conditionals and Iteration | HW 4 (Due 10/15) | |
| 10/8 | Probability and Sampling | Lab 5 | ||
| 7 | 10/13 | No Class (Holiday) | ||
| 10/15 | Midterm Review | |||
| 8 | 10/20 | Midterm Exam | ||
| 10/22 | Special Topics | |||
| 9 | 10/27 | Models & Empirical Simulations | HW 5 (Due 11/5) | |
| 10/29 | Hypothesis Testing | Lab 6 | ||
| 10 | 11/3 | Statistical Significance | HW 6 (Due 11/12) | |
| 11/5 | A/B Testing | Lab 7 | ||
| 11 | 11/10 | Confidence Intervals | HW 7 (Due 11/19) | |
| 11/12 | Normal Distribution | Lab 8 | ||
| 12 | 11/17 | Sample Means | HW 8 (Due 11/26) | |
| 11/19 | Correlation & Linear Regression | Lab 9 | ||
| 13 | 11/24 | Least Squares & Residuals | HW 9 (Due 12/3) | |
| 11/26 | No Class (Holiday) | |||
| 14 | 12/1 | Regression Inference | ||
| 12/3 | Special Topics | Lab: Final Project Consultations | ||
| 15 | 12/8 | Special Topics |
The latest schedule is visible on the course homepage and will be updated as the course progresses.
You are expected to hold yourself to the highest standard of academic integrity and honesty, as reflected in the Barnard Honor Code. Approved by the student body in 1912 and updated in 2016, the Code states:
We, the students of Barnard College, resolve to uphold the honor of the College by engaging with integrity in all of our academic pursuits. We affirm that academic integrity is the honorable creation and presentation of our own work. We acknowledge that it is our responsibility to seek clarification of proper forms of collaboration and use of academic resources in all assignments or exams. We consider academic integrity to include the proper use and care for all print, electronic, or other academic resources. We will respect the rights of others to engage in pursuit of learning in order to uphold our commitment to honor. We pledge to do all that is in our power to create a spirit of honesty and honor for its own sake.
This course is meant to build your programming skills, so it is not advised to use any generative AI tools for any assignments outside of the final project. This will help you build intuition about how to write code and fix common bugs. The most productive use of generative AI for this course would be using it as a support tool, such as explaining code snippets from sample code. You are likely better off reading Python documentation or the Data 8 Python Reference as they represent best practices, and generated code may be verbose or potentially incorrect (may not compile). An over-reliance on AI can hinder independent thinking and creativity.
For your final project, AI generated text is not permitted as part of your written descriptions in your final report. Your report must include your own original writing and reflections. Violations can result in a failing grade for the assignment and/or the course.
It is important for undergraduates to recognize and identify the different pressures, burdens, and stressors you may be facing, whether personal, emotional, physical, financial, mental, or academic. We as a community urge you to make yourself– your own health, sanity, and wellness–your priority throughout this term and your career here. Sleep, exercise, and eating well can all be a part of a healthy regimen to cope with stress. Resources exist to support you in several sectors of your life, and we encourage you to make use of them. Should you have any questions about navigating these resources, please visit these sites:
If you anticipate barriers to your academic experience due to a documented disability or emerging health challenge, please contact your instructor and/or the Center for Accessibility Resources & Disability Services (CARDS) as early as possible. If you have questions regarding registering a disability or receiving accommodations for the semester, contact CARDS at (212) 854-4634 or [email protected]. You can learn more about on-campus support at barnard.edu/disability-services. CARDS is located in 101 Altschul Hall.