Spring 2027

Data Management and Analysis for Industrial Engineers

Covers fundamentals of data management and analysis for industrial engineers, including database strategies, preprocessing, visualization, and modeling using MySQL and R.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Tina Xu

Homework was similar to in-class problems, and one student found it easy to get extra credit for attendance. Exam difficulty varied by review, with some finding them easy and others describing them as decently hard with little preparation guidance.

What you’ll be able to do

  • Demonstrate knowledge of fundamental industrial database management strategies

  • Apply data preprocessing, visualization, and modeling techniques

  • Apply industrial database management techniques using structured query language (SQL)

  • Use leading programming software to perform data regression analysis

  • Create and solve a real-life industrial data analytics problem, and present results effectively to audience

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
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All recorded terms · compare terms & instructors

Prerequisites

Course map

I SY E 210,B M E 325,E C E 331,MATH 331,MATH/​STAT 309, 409, 410, 431,STAT 311, 324, or member of Engineering Guest Students

ISYE 312

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

Prerequisite text tree

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

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

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 480

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

Meeting source records
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Student experience

the class

Past student reviews · Tina Xu

Students consistently described Tina Xu’s lectures as boring and delivered by reading from disorganized slides. Despite the low-energy delivery, several reviewers noted that the course content itself was not overly difficult.

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

Past student reviews · Tina Xu

Homework was similar to in-class problems, and one student found it easy to get extra credit for attendance. Exam difficulty varied by review, with some finding them easy and others describing them as decently hard with little preparation guidance.

Past student reviews · Tina Xu

Reviewers cited a lack of clear expectations and guidance for exams and group projects as frustrating. One student appreciated the instructor’s flexibility with late submissions, while others found the low support levels difficult to navigate.

Topics

  • Database management, data preprocessing, visualization, and modeling
  • Solving real industrial engineering problems
  • Programming with MySQL and R

Skills

  • Industrial database management strategies
  • Data preprocessing, visualization, and modeling
  • Database management using SQL
  • Data regression analysis using programming software

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

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

1280 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.

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Selected offering source records
ISYE 312 · Spring 2027

Data Management and Analysis for Industrial Engineers

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:490:025761",
    "course_id": "ISYE 312",
    "course_uid": "course_5b7e840b49cedb60a91edc99",
    "term_id": "1274",
    "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
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "2554af1a1dbb6928aa33627f503c063a32c2c363bdd24cf06feb84df0a04fa87",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}