MISM 6412 — Data Mining and Machine Learning for Business
4 semester hoursGraduateLectureusually offered: springtypical days: T/FOakland, CATraditional
Explores data mining perspectives and methods within a business context. Introduces the theoretical foundations of key data mining techniques and provides guidance on selecting and applying the appropriate methods for various business scenarios, highlighting the advantages of each approach. Offers students an opportunity to engage with contemporary data mining software applications and to develop basic programming skills. Covers both supervised and unsupervised learning methods, focusing on solving real-world business problems including data cleaning, data transformation, and data modeling.
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Spring 2026 | 1 | 6 | 25 | 24% | 19.0 |
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 0% · T 100% · W 0% · Th 0% · F 100%
Common patterns: TF (100% of sections) — in patterns, R means Thursday
Professors
Spring
- Carol Theokary (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) · All MISM courses · Plan it at numap.app