Spring 2027

Data Science Modeling II

Data Science Modeling II teaches exploring, modeling, and analyzing data using R, covering probability, hypothesis testing, regression, ANOVA, and machine learning methods like random forests.

offering recorded4 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Brian Powers, Bi Cheng Wu

Some students found the tests very difficult, with one noting they were harder than the midterms. Another described the instructor as super strict, warning students to be careful about making mistakes.

What you’ll be able to do

  • Understand and apply basic concepts in probability; combine basic probability models to build more complicated ones; and critique models and their assumptions

  • Formulate statistical hypotheses for different kinds of research questions and test those hypotheses using both classical and Monte Carlo methods.

  • Understand and apply principles of statistical estimation and prediction, including fitting models and assessing model quality, in the context of both linear and logistic regression.

  • Apply statistical tools to answer research questions using real-world data and present these findings clearly in both spoken and written form to non-experts.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

(MATH 211, 217, or 221) and STAT 240

STAT 340 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

BI CHENG WU is recorded teaching in Fall 2021, Spring 2022, Spring 2023, Fall 2023, Spring 2024, Fall 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

BRIAN POWERS is recorded teaching in Spring 2023, Fall 2023, Spring 2024, Fall 2024, Spring 2025, Fall 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

KARL ROHE is recorded teaching in Spring 2020. Recorded history may be incomplete and does not establish a future schedule.

KEITH LEVIN is recorded teaching in Fall 2021, Fall 2022. Recorded history may be incomplete and does not establish a future schedule.

SAHIFA SIDDIQUA is recorded teaching in Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

YONGYI GUO is recorded teaching in Spring 2024, Spring 2026. 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 / 1980
DIS 311Classroom Instruction0 / 220
DIS 313Classroom Instruction0 / 220
LEC 002Classroom Instruction0 / 1980
DIS 314Classroom Instruction0 / 220
DIS 315Classroom Instruction0 / 220
DIS 316Classroom Instruction0 / 220
DIS 321Classroom Instruction0 / 220
DIS 322Classroom Instruction0 / 220
DIS 323Classroom Instruction0 / 220
DIS 324Classroom Instruction0 / 220
DIS 325Classroom Instruction0 / 220
DIS 326Classroom Instruction0 / 220
DIS 312Classroom Instruction0 / 220
DIS 317Classroom Instruction0 / 220
DIS 318Classroom Instruction0 / 220
DIS 319Classroom Instruction0 / 220
DIS 327Classroom Instruction0 / 220
DIS 328Classroom Instruction0 / 220
DIS 329Classroom Instruction0 / 220

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 · Karl Rohe, Brian Powers

Students noted that lectures were often boring or that tests did not reflect the course material. In one instance, a group project was a significant component of the final grade.

Recent recorded grades — Spring 2025: 2.94 GPA, 35.4% A/AB (n=452 letter grades); Fall 2025: 3.04 GPA, 37.6% A/AB (n=399 letter grades); Spring 2026: 2.83 GPA, 36.1% A/AB (n=416 letter grades).

difficulty & workload

Past student reviews · Brian Powers, Bi Cheng Wu

Some students found the tests very difficult, with one noting they were harder than the midterms. Another described the instructor as super strict, warning students to be careful about making mistakes.

Past student reviews · Bi Cheng Wu, Yongyi Guo

Feedback on instruction varied widely; some students appreciated instructor leniency but struggled with limited practice problems, while others found lectures inefficient due to excessive reading of code or formulas.

Topics

  • Probability models, central limit theorem, and Monte Carlo simulation
  • Hypothesis testing, Bayesian inference, regression, ANOVA, bootstrap, random forests, and cross-validation

Skills

  • Exploring, modeling, and analyzing data using R
  • Formulating and testing statistical hypotheses using classical and Monte Carlo methods
  • Fitting and assessing linear and logistic regression models
  • Presenting statistical findings clearly in spoken and written form

Grades

Historical instructor

Spring 2027 · Projected

Before grades are released

– average GPA

Approximate 80% prediction interval

About this estimate

The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.

The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.

4 earlier forecasts · 0.26 GPA average error.

Grades over time

Through Spring 2027

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.

Download part 1
Selected offering source records
STAT 340 · Spring 2027

Data Science Modeling II

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": "1274:932:025501",
    "course_id": "STAT 340",
    "course_uid": "course_d2d0c15e32bd1631a6074548",
    "term_id": "1274",
    "source_course_id": "025501",
    "source_subject_id": "932",
    "title": "Data Science Modeling II",
    "credits_min": 4,
    "credits_max": 4,
    "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 2026-10-14

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 1Download part 2
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "537b3132acb07b7308cf1241e0e2f68310982ad09084c8d013d0daee3a6b716d",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}