MISM 3515 — Data Mining for Business
4 semester hoursUndergraduateLectureusually offered: fall, springtypical days: MBostonTraditional
Covers key concepts, techniques, methods, and applications of data mining in the context of business. Offers students opportunities to learn how to distill key insights from a large amount of unknown data, which techniques to choose from, how to apply the techniques and methods to get the answer and insights from the data, and how to interpret the results from the analysis. Example predictive analysis techniques include market basket analysis and principle component analysis. Covers all techniques using business examples and user-friendly tools.
Prerequisites
- one of:
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Fall 2023 | 1 | 34 | 40 | 85% | 6.0 |
| Fall 2024 | 1 | 34 | 42 | 81% | 8.0 |
| Spring 2025 | 1 | 37 | 40 | 93% | 3.0 |
| Fall 2025 | 1 | 37 | 42 | 88% | 5.0 |
| Spring 2026 | 1 | 37 | 40 | 93% | 3.0 |
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 60% · T 21% · W 0% · Th 41% · F 19%
Common patterns: MR (41% of sections), T (21% of sections), M (19% of sections), F (19% of sections) — in patterns, R means Thursday
Professors
Fall
- Bhawesh Sah (68% of students) · reviews
- Xiaoping Liu (32% of students) · reviews
Spring
- Bhawesh Sah (100% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Links
Official catalog (MISM course descriptions) · Student reviews on RateMyHusky · All MISM courses · Plan it at numap.app