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
- [DSCIB] 100 - students from all majors are welcome!
- Instructor
- Adam Poliak
- Teaching Assistants
- Course Staff
- Website
- https://bmc-ds-100.github.io/
- Discussion Forum
- Piazza
- Time and place
- Spring 2025, T/TH 1:10-2:30pm, Location: Dalton 300
- Lab T: 2:40-4:00pm, Location: Canaday Computer Lab
- Office Hours
- Times
- Prerequisites
- None - no prior programming or college-math background is required
- 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
Grading
- Homeworks: 25%
- Labs: 5%
- Projects: 15% (8%, 4.5%, 2.5%)
- Midterm: 20%
- Final: 30%
- Participation: 5%
- Late day policy
- Each student has 8 late days for the homeworks and projects. At most 2 late days can be used for one assignment.
See the Policies for more details.
Acknowledgments
Eric Van Dusen, his staff, and The Data Science Education Community have been very helpful in adopting this course first at Barnard and now at Bryn Mawr.