IE 5640 — Data Mining for Engineering Applications
4 semester hoursGraduateLectureusually offered: fall, springBostonOnlineTraditionalVideo Streaming
Introduces data mining concepts and statistics/machine learning techniques for analyzing and discovering knowledge from large data sets that occur in engineering domains such as manufacturing, healthcare, sustainability, and energy. Topics include data reduction, data exploration, data visualization, concept description, mining association rules, classification, prediction, and clustering. Discusses data mining case studies that are drawn from manufacturing, retail, healthcare, biomedical, telecommunication, and other sectors.
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Fall 2023 | 2 | 24 | 60 | 40% | 18.0 |
| Fall 2024 | 2 | 8 | 60 | 13% | 26.0 |
| Spring 2025 | 1 | 5 | 45 | 11% | 40.0 |
| Fall 2025 | 1 | 6 | 16 | 38% | 10.0 |
| Spring 2026 | 1 | 9 | 16 | 56% | 7.0 |
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 42% · T 25% · W 0% · Th 10% · F 8%
Common patterns: M (42% of sections), T (17% of sections), async (12% of sections), S (12% of sections), R (10% of sections), TF (8% of sections) — in patterns, R means Thursday
Professors
Fall
- Mohammad Amin Javadi (84% of students) · reviews
- Srinivasan Radhakrishnan (16% of students) · reviews
Spring
- Shahin Shahrampour (64% of students) · reviews
- Srinivasan Radhakrishnan (36% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Links
Official catalog (IE course descriptions) · Student reviews on RateMyHusky · All IE courses · Plan it at numap.app