Spring 2027

Simulation and Probabilistic Modeling

Covers stochastic system analysis via analytics and simulation, focusing on queueing, manufacturing, Markov chains, and project management.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Qiaomin Xie

Homework was incredibly difficult because students did not do related examples in class or lab, and instructors were not great at helping.

What you’ll be able to do

  • Apply knowledge of math, science, and engineering principles to model real stochastic systems

  • Identify, formulate, and solve problems using appropriate data analytic and simulation approaches

  • Understand and apply probabilistic modeling techniques such as Markov Chains and queueing theory to study stochastic systems

  • Apply statistical methods to analyze stochastic behaviors of the systems

  • Apply simulation software to model the process and evaluate performance measures of the systems

Source: UW Guide

Grade history

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average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

(MATH 331,STAT/​MATH 309, 409, 431, 531,STAT 311, or concurrent enrollment) and (I SY E 210,E C E 331,MATH/​STAT 310, 410,STAT 312, 324, 371, or concurrent enrollment) and (MATH 320, 340, 345, or concurrent enrollment), or Engr Guest Student

ISYE 320 used by

“Used by” includes alternatives; linked courses may have other requirements. 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

AMANDA SMITH is recorded teaching in Fall 2019, Fall 2020, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

CHEN WANG is recorded teaching in Spring 2012. Recorded history may be incomplete and does not establish a future schedule.

QIAOMIN XIE is recorded teaching in Spring 2022, Spring 2023, Fall 2023, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

XIN WANG is recorded teaching in Fall 2016, Fall 2017, Fall 2018. Recorded history may be incomplete and does not establish a future schedule.

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 / 460

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 · Amanda Smith

Amanda Smith taught difficult concepts well and provided sufficient homework to grasp the material without busy work.

Recent recorded grades — Spring 2025: 3.45 GPA, 78.9% A/AB (n=38 letter grades); Fall 2025: 3.63 GPA, 84.5% A/AB (n=71 letter grades); Spring 2026: 3.69 GPA, 90.9% A/AB (n=44 letter grades).

difficulty & workload

Past student reviews · Qiaomin Xie

Homework was incredibly difficult because students did not do related examples in class or lab, and instructors were not great at helping.

Past student reviews · Xin Wang

The course went through material too slowly, preventing the coverage of all content, though tests were usually not too difficult if students understood the homework.

Topics

  • Stochastic systems analysis
  • Arrival and service process modeling
  • State spaces and transition probabilities
  • Queuing and manufacturing system simulation
  • Continuous time Markov analysis
  • Simulation project management and testing

Skills

  • Analyze stochastic systems using analytic methods and computer simulation.
  • Develop empirical and theoretical models of arrival and service processes.
  • Define state spaces and calculate state transition probabilities.
  • Perform continuous time Markov analysis for manufacturing systems.
  • Manage simulation projects, including testing and evaluating emerging trends.

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 320 · Spring 2027

Simulation and Probabilistic Modeling

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": "1274:490:010231",
    "course_id": "ISYE 320",
    "course_uid": "course_10d2c585c41b0b82531af186",
    "term_id": "1274",
    "source_course_id": "010231",
    "source_subject_id": "490",
    "title": "Simulation and Probabilistic Modeling",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall, Spring"
  }
]
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.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "c12d5b93a60a56f85305063162742943fc70499d30f8e2525418aab259dbe6e8",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}