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.

offering recorded3 credits
Recorded instructors · Fall 2026 Tina Xu3.2/5

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
A
AB
B
BC
C
D
F

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

ISYE 312

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Fall 2026
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 2.38/5 from 13 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 2.5/5 raw quality · 2.5/5 difficulty · 4 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

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.

Historical instructors & teaching patterns

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction66 / 701

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
Download part 1

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

  • Industrial database management strategies.
  • Data preprocessing and visualization.
  • Database analysis using MySQL and R.

Skills

  • Formulating and solving industrial engineering problems using data management and modeling strategies.
  • Industrial database management, data preprocessing, visualization, and modeling using MySQL and R.

Grades

Latest available · Spring 2026— not enough history to project Fall 2026.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
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.

Download part 1
Selected offering source records
ISYE 312 · Fall 2026

Data Management and Analysis for Industrial Engineers

Recorded 2026-09-07
Raw 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
Download part 1
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-09-07

View the dataset on Hugging Face
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
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"
}