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Data science is the study of extracting value from data. This 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. This class 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 math background is required.

Course number
BC COMS 1016 - students from all majors are welcome!
Adam Poliak
Teaching Assistants
Course Staff
Discussion Forum
Slack Requires signing up via a or email
Time and place
Fall B, MTWR 2:40-3:55pm, Location: Check courseworks for Zoom link
Lab 01 TR 9:00 - 9:50am
Lab 02 MW 6:10 - 7:00pm
Office Hours
None - no prior programming or college-math background is required
Modes of Thinking Requirement
Thinking Quantitatively and Empirically
Thinking Technologically and Digitally
Course Readings
Each lecture has an accompanying chapter/section of the textbook
Some lectures will have accompanying optional reading related to the lecture’s topic
This is a project-based course. There will be an open book take home midterm but the majority of your grade will be based on assignments.
  • Assignments (Homeworks, mini-projects, Final project) - 70%
  • Exams and Quizes (Midterm, Daily Quizes) - 25%
  • Participation - 5%
Late day policy
To account for issues that arise in these uncertain times, each student has 10 late days for the homeworks and projects.
See the Policies for more details.


A Google Cloud Education grant is supporting the computational infrastructure for the course.
Eric Van Dusen, his staff, and The Data Science Education Community have been very helpful in adopting this course at Barnard.