CSCI2291

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

Requirements fulfilled

No source-backed degree requirement is attached to this course yet.

Official evaluation summary

3.56 / 5

Based on 133 aggregate responses from BC Avalanche/Blue evaluations.

Data freshness

Course and evaluation data last updated 2026-07-23. Source details and limitations are documented in Data Sources and Methodology.

Instructors

  • Cristina Maier
    Fall 2026, Fall 2025, Fall 2024 · Official rating 3.17/5
  • George Mohler
    Spring 2026, Spring 2023 · Official rating 4.29/5
  • Sergio Alvarez
    Spring 2025, Spring 2024, Spring 2022 · Official rating 3.56/5

Sections

  • Section 01
    Fall 2026 · Cristina Maier · 245 Beacon Street Room 107 MWF 01:00PM-01:50PM · Offered