CSCI 2291 Data Science: Methods and Applications
This course focuses on efficient organization and processing of data, data visualization and communication, statistical modeling, and machine learning, integrating concepts in responsible data science and social impact, such as bias in data collection and modeling, privacy, ethical design of data science experiments, and model interpretability. Students will apply data science techniques to real-world problems and publicly available datasets arising across the range of human inquiry.
Course overview
- Department
- Computer Science
- School
- MCAS
- Credits
- 3
- Level
- Undergraduate
- Offered
- Every Spring
Catalog details
- Prerequisites
- CSCI1090 and MATH2250
Requirements fulfilled
- Computer Science B.A.: CSCI 2000-level-or-higher elective (Current University Catalog; students should confirm their catalog year)
- Applied Physics B.S.: Corequisite: Computer Science (one course) (Current University Catalog; students should confirm their catalog year)
- Physics B.S.: Corequisite: Computer Science (one course) (Current University Catalog; students should confirm their catalog year)
- Mathematics B.S.: Natural science, computer science, or economics corequisites (Current University Catalog; students should confirm their catalog year)
- Biology B.S.: Quantitative corequisite options (Current University Catalog; students should confirm their catalog year)
Official evaluation summary
3.56 / 5
Data freshness
Instructors
Sections
- Section 01