Spring 2027

Data-driven Dynamical Systems, Stochastic Modeling and Prediction

Introduction to data-driven dynamical systems covering mathematical theory, numerical algorithms, and coding for stochastic modeling and prediction.

offering recorded3 credits
Recorded instructors · Spring 2027 Nan Chen4.2/5

Summary

1 / 6

Past student reviews · Samuel Stechmann

The course received a low difficulty rating of 2 out of 5 from a past student.

What you’ll be able to do

  • Conduct numerical simulations of stochastic dynamical systems and implement data assimilation methods.

  • Analyze data from a stochastic dynamical system and characterize the system's properties in terms of its statistics, such as mean, variance, and autocorrelation function.

  • Calculate analytical formulas for the solutions of discrete-time stochastic processes and stochastic differential equations.

  • Recall and state the basic properties of Brownian motion and the Wiener process.

  • Illustrate the basic properties of Brownian motion and the Wiener process using numerical simulations.

  • Estimate the parameters of a dynamical system using simulated data.

  • Predict the future evolution of a dynamical system using analytical formulas and numerical simulations.

  • Assess the accuracy of a prediction in terms of different definitions of error and uncertainty.

  • Identify applications of course content in areas of modern research.

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 320, 340, 341, 345, or 375) and (STAT/​MATH 309, 431,STAT 311, or MATH 531) and (MATH 322, 341, 375, 421, or 467), graduate/professional standing, or declared in Mathematics VISP (undergraduate or graduate)

MATH 616

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

Prerequisite text tree

Professors

Spring 2027

Nan Chen

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 4.59/5 from 29 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.

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

Recent recorded grades — Spring 2026: 4.00 GPA, 100.0% A/AB (n=40 letter grades).

Historical instructors & teaching patterns

NAN CHEN is recorded teaching in Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

SAMUEL STECHMANN is recorded teaching in Fall 2024. 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 / 400

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 · Samuel Stechmann

Past students found the lectures funny, clear, and engaging, and recommended taking the class with the instructor.

Recent recorded grades — Fall 2024: 3.57 GPA, 75.7% A/AB (n=37 letter grades); Spring 2026: 4.00 GPA, 100.0% A/AB (n=40 letter grades).

difficulty & workload

Past student reviews · Samuel Stechmann

The course received a low difficulty rating of 2 out of 5 from a past student.

Past student reviews · Samuel Stechmann

The instructor was described as awesome and effective in delivering clear, engaging instruction.

Topics

  • Stochastic toolkits, linear Gaussian processes, nonlinear stochastic systems, SDEs, data assimilation, parameter estimation, forecasting

Skills

  • Solving coding problems using a programming language
  • Conducting numerical simulations and implementing data assimilation methods
  • Estimating parameters of dynamical systems using simulated data
  • Assessing prediction accuracy via error and uncertainty metrics

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
MATH 616 · Spring 2027

Data-Driven Dynamical Systems, Stochastic Modeling and Prediction

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": "1274:600:026698",
    "course_id": "MATH 616",
    "course_uid": "course_2d244e0d230263518e578140",
    "term_id": "1274",
    "source_course_id": "026698",
    "source_subject_id": "600",
    "title": "Data-Driven Dynamical Systems, Stochastic Modeling and Prediction",
    "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": "329094f3eea61c049ab7c59bac16f1a608b686df42bdd7877be923a42b26fed6",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}