MKTG 2602 — Quantitative Analysis of Consumer Data
4 semester hoursUndergraduateLectureusually offered: fall, springtypical days: W/FBostonTraditional
Introduces the fundamental techniques of quantitative data analysis and visualization in the marketing context. Emphasizes real-world consumer data and applications using R. Offers students an opportunity to learn a wide variety of foundational data-driven inference methods and progress to more advanced coursework delving into analyzing and understanding complex behavioral data. No previous experience in data analysis or programming required.
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Fall 2023 | 1 | 32 | 40 | 80% | 8.0 |
| Spring 2024 | 2 | 65 | 70 | 93% | 2.5 |
| Summer A 2024 | 1 | 21 | 40 | 53% | 19.0 |
| Fall 2024 | 1 | 40 | 40 | 100% | 0.0 |
| Spring 2025 | 2 | 75 | 80 | 94% | 2.5 |
| Summer A 2025 | 1 | 33 | 40 | 83% | 7.0 |
| Fall 2025 | 2 | 75 | 75 | 100% | 0.0 |
| Spring 2026 | 2 | 76 | 80 | 95% | 2.0 |
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 13% · T 31% · W 66% · Th 5% · F 71%
Common patterns: WF (53% of sections), TF (18% of sections), T (9% of sections), M (8% of sections), W (7% of sections), MTWR (5% of sections) — in patterns, R means Thursday
Professors
Fall
- Daniel Katz (51% of students) · reviews
- Mirna Snyder (49% of students) · reviews
Spring
- Emil Palikot (35% of students)
- Daniel Katz (18% of students) · reviews
- Kenneth Parker (17% of students) · reviews
- Mirna Snyder (16% of students) · reviews
Summer A
- Daniel Katz (61% of students) · reviews
- Mirna Snyder (39% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Unlocks
Courses that list MKTG 2602 in their prerequisites.
Links
Official catalog (MKTG course descriptions) · Student reviews on RateMyHusky · All MKTG courses · Plan it at numap.app