MISM 3405 — Data Wrangling for Business Analytics
4 semester hoursUndergraduateLectureusually offered: springtypical days: T/FBostonTraditional
Covers data wrangling principles and novel techniques for business analytics. Key topics include data profiling, data retrieval, data cleansing, and data integration, as well as data extraction and exploration via APIs. Applies the principles of data wrangling for structured and unstructured data using industry tools such as Oracle, SQL, statistical programming languages (R/Python), and visualization tools (Tableau). Offers students an opportunity to learn data wrangling techniques to identify and solve real-world data challenges, creating business value from the vast amount and types of traditional and big data.
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Spring 2024 | 1 | 28 | 40 | 70% | 12.0 |
| Summer A 2024 | 1 | 16 | 40 | 40% | 24.0 |
| Spring 2025 | 1 | 40 | 40 | 100% | 0.0 |
| Summer A 2025 | 1 | 13 | 40 | 33% | 27.0 |
| Spring 2026 | 1 | 32 | 40 | 80% | 8.0 |
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 22% · T 100% · W 22% · Th 22% · F 78%
Common patterns: TF (78% of sections), MTWR (22% of sections) — in patterns, R means Thursday
Professors
Spring
- Tareq Nasralah (72% of students) · reviews
- Carol Lee (28% of students) · reviews
Summer A
- Tareq Nasralah (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