INFO 6105 — Data Science Engineering Methods and Tools

4 semester hoursGraduateLectureusually offered: fall, springArlington, VABostonMiami, FLOakland, CASeattle, WASilicon Valley, CAToronto, CanadaVancouver, CanadaTraditional

Introduces the fundamental techniques for machine learning and data science engineering. Discusses a variety of machine learning algorithms, along with examples of their implementation, evaluation, and best practices. Lays the foundation of how learning models are derived from complex data pipelines, both algorithmically and practically. Topics include supervised learning (parametric/nonparametric algorithms, support vector machines, kernels, neural networks, deep learning) and unsupervised learning (clustering, dimensionality reduction, recommender systems). Based on numerous real-world case studies.

Prerequisites

Offering history

TermSectionsEnrolledCapacityFullOpen seats/section
Fall 20231025237368%12.1
Spring 2024935144978%10.9
Fall 20241530247663%11.6
Spring 20251327040068%10.0
Fall 20251220741050%16.9
Spring 2026109532629%23.1

Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.

Meeting times

Share of recent sections by weekday: M 25% · T 17% · W 29% · Th 24% · F 15%

Common patterns: W (18% of sections), T (17% of sections), S (14% of sections), M (13% of sections), R (12% of sections), MR (12% of sections) — in patterns, R means Thursday

Professors

Fall

Spring

Percentages are each professor's average share of the season's enrolled students in recent terms.

Unlocks

CSYE 7105, CSYE 7470, DAMG 7105, DAMG 7245, INFO 6106, INFO 7390

Courses that list INFO 6105 in their prerequisites.

Links

Official catalog (INFO course descriptions) · Student reviews on RateMyHusky · All INFO courses · Plan it at numap.app