Tina Xu’s lectures are consistently described as boring and monotone, with slides that are rarely helpful. While she is personally nice and offers extra credit for attendance, she provides little guidance for exams. Reviewers disagree on difficulty, with some finding exams easy and others finding them decently hard.
Fall 2026
Data Management and Analysis for Industrial Engineers
ISYE 312 teaches data management and analysis fundamentals for industrial engineers, focusing on database strategies, preprocessing, visualization, and modeling with MySQL and R.
Recorded instructors · Fall 2026 Tina Xu
Summary
1 / 6
Homework and projects are graded easily and mirror class problems, but exams require independent preparation due to low guidance. Extra credit is available for lecture attendance.
Grade history
↗ average GPA
letter grades
letter grades
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All recorded terms · compare terms & instructors
Prerequisites
Course map(I SY E 210,E C E 331,MATH/STAT 309,STAT 311, or 324) or member of Engineering Guest Students
- take one
- member of Engineering Guest Students
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Any of
- Any of
- member of Engineering Guest Students
Professors
Fall 2026Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Fall 2026Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|---|---|---|
| LEC 001 | Classroom Instruction | 66 / 70 | 1 |
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.
Meeting source records
Student experience
the class
Tina Xu teaches ISYE 312 with unengaging, monotone lectures that rely heavily on reading slides. While the course material and exams are generally manageable, students report a lack of clear guidance and organization.
Recent recorded grades — Spring 2025: 3.72 GPA, 86.0% A/AB (n=57 letter grades); Fall 2025: 3.64 GPA, 79.7% A/AB (n=69 letter grades); Spring 2026: 3.68 GPA, 78.7% A/AB (n=47 letter grades).
difficulty & workload
Homework and projects are graded easily and mirror class problems, but exams require independent preparation due to low guidance. Extra credit is available for lecture attendance.
Students find the lectures boring and difficult to sit through due to a lack of energy and organization. While the professor is described as nice, expectations for exams and projects are often unclear.
Topics
Skills
Grades
Latest available · Spring 2026— not enough history to project Fall 2026.
Grade distribution · % of letter grades
A
AB
B
BC
C
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F
Grades over time
Through Spring 2026
More grade details Grade mix, volume & source data
Where this course fits relative to
Latest available grades · Spring 2026 · all course levels
GPA
Higher than % of other courses in this group.
Course GPAs · red marks this course’s range
letter grades
More recorded grades than % of other courses in this group.
Typical course in this group: letter grades.
About this comparison
1283 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.
Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.
Sources & history
UW–Madison
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
Data Management and Analysis for Industrial Engineers
Recorded 2026-09-07Raw records
[
{
"run_id": "20260907T155543-ce3781c4",
"semester": "1272",
"observed_at": "2026-09-07 15:55:43.033547+00:00",
"offering_id": "1272:490:025761",
"course_id": "ISYE 312",
"course_uid": "course_5b7e840b49cedb60a91edc99",
"term_id": "1272",
"source_course_id": "025761",
"source_subject_id": "490",
"title": "Data Management and Analysis for Industrial Engineers",
"credits_min": 3,
"credits_max": 3,
"typically_offered": "Not Applicable"
}
]Rate My Professors
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Madgrades
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesModel outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
"model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
"task_version": "14",
"output_id": "230337591312e4b19c7ce676c49d7ae4d900290da1da8436e66d10a16e575a7c",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}