Spring 2025

Instructor:

• Tiffany Tseng, Barnard Computer Science [email protected]

TAs:

• Amaya Kejriwal [email protected]
• Justin Zeng [email protected]
• Erin Ma [email protected]
• Ken Mah [email protected]

Computing Fellows:

• Claudia Lihar [email protected]
• Salia Nahshal [email protected]

Lectures:

S01 - MW 11:40-12:55am - 140 Horace Mann

S02 - MW 1:10-2:25pm - 152 Horace Mann

Labs:

S01 - W 5:00-6:30pm Amaya Kejriwal

S02 - TH 10:00-11:30am - Justin Zeng

S03 - TH 1:00-2:30pm - Erin Ma

S04 - TH 3:00-4:30pm - Ken Mah

Office Hours:

Tue 10:30-12:00 - Justin (Milstein 503)

Tue 3:00-4:30 - Amaya (Milstein 503)

Wed 2:30-4:00 - Erin (via Google Meet)

Fri 12:00-1:30 - Ken (Milstein 503)

OVERVIEW

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, restaurant and movie reviews, geographic data, poverty rates, and more. 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.

LEARNING OUTCOMES

• Learn how to explore datasets by visualizing data to identify patterns
• Conduct statistical analyses to draw inferences about the world
• Use data to make predictions about possible outcomes using models
• Gain familiarity with programming in Python and experience analyzing data, building visualizations, and running statistical tests using Jupyter Notebooks

MATERIALS

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.

GRADING

The course involves weekly labs and homework assignments where students will practice programming and statistics concepts covered in lecture. Weekly lab assignments will be due by 11:59pm on Sundays, while weekly homework assignments will be due by 11:59pm on Wednesdays. We expect all grading to be completed within one week of your submission deadline.

There will be a written midterm exam on March 10th.

The class culminates in a final project in which students work in groups of 2 to analyze a dataset and write up a report on their findings. There is no final exam for this course.

Final grades are assigned according to the instructor’s holistic evaluation of your performance, following the Barnard Grading system (excellent, good, satisfactory, etc). As a rough guide, refer to the percentages below, but keep in mind that all grading decisions are based on a complete review of your performance, which may deviate from the strict percentage breakdown below.

35% - Homework Assignments

20% - Midterm Exam