Fall 2026

Data Science Modeling II

STAT 340 teaches data exploration, modeling, and analysis using R, covering probability, simulation, hypothesis testing, Bayesian inference, regression, and machine learning techniques.

offering recorded4 credits
Recorded instructors · Fall 2026 Brian Powers3.4/5Bi Cheng Wu2.0/5Aditya Hoode +8 more

Summary

1 / 7

Reviewers consistently report that exams are extremely difficult, often unrelated to lectures or homework, with low averages and insufficient time for paper-based tests.

Grade history

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average GPA
letter grades
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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

Fall 2026
/5Adjusted rating
/5RMP difficulty
captured reviews
About this rating

Raw average: 3.17/5 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.

For this course: 3.1/5 raw quality · 3.9/5 difficulty · 17 reviews

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

Brian Powers is approachable, caring, and a clear explainer who answers questions thoroughly and maintains organized Canvas resources. Reviewers appreciate his passion and helpfulness during office hours, noting he is a standout compared to other department instructors.

Critics report that lectures are tangential or unclear, and exams are unfairly difficult with little alignment to homework. Some students found the tests excessively hard and the course structure poorly designed, leading to frustration despite the professor's personal kindness.

Recent recorded grades — Spring 2025: 2.91 GPA, 34.3% A/AB (n=245 letter grades); Fall 2025: 3.00 GPA, 36.9% A/AB (n=198 letter grades); Spring 2026: 2.84 GPA, 35.9% A/AB (n=92 letter grades).

Bi Cheng Wu

/5Adjusted rating
/5RMP difficulty
captured reviews
About this rating

Raw average: 1.75/5 from 142 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: 1.4/5 raw quality · 4.3/5 difficulty · 49 reviews

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

Bi Cheng Wu is described as caring and lenient with late work, though this leniency is criticized for making exam preparation difficult due to few assignments.

Many reviewers report that lectures are boring and exams are difficult with poor alignment to homework. Students also cite poor communication, demeaning office hours, and disorganized feedback.

Recent recorded grades — Fall 2024: 3.04 GPA, 39.2% A/AB (n=301 letter grades); Spring 2025: 2.96 GPA, 36.7% A/AB (n=207 letter grades); Spring 2026: 2.85 GPA, 37.0% A/AB (n=138 letter grades).

No course-specific feedback yet.

Historical instructors & teaching patterns

Historical reviews of Yongyi Guo: Bi Cheng Wu is preferred over current instructor Yongyi Guo for live coding instruction, though Guo's exams are considered fair and not too difficult. Recorded lectures and notes are available, reducing the necessity of attendance.

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

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction125 / 1670
DIS 311Classroom Instruction11 / 230
DIS 312Classroom Instruction17 / 240
DIS 313Classroom Instruction22 / 240
DIS 316Classroom Instruction19 / 240
LEC 002Classroom Instruction153 / 1823
DIS 321Classroom Instruction7 / 140
DIS 322Classroom Instruction13 / 240
DIS 323Classroom Instruction23 / 240
DIS 325Classroom Instruction16 / 240
DIS 326Classroom Instruction24 / 240
DIS 317Classroom Instruction23 / 240
DIS 318Classroom Instruction15 / 240
DIS 319Classroom Instruction18 / 240
DIS 327Classroom Instruction22 / 243
DIS 328Classroom Instruction24 / 240
DIS 329Classroom Instruction24 / 240
LEC 003Classroom Instruction131 / 1540
DIS 331Classroom Instruction21 / 240
DIS 332Classroom Instruction18 / 240
DIS 333Classroom Instruction9 / 120
DIS 334Classroom Instruction24 / 240
DIS 335Classroom Instruction17 / 240
DIS 336Classroom Instruction9 / 120
DIS 337Classroom Instruction9 / 100
DIS 339Classroom Instruction24 / 240

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

Brian Powers is praised as a caring, approachable lecturer who explains concepts well and provides fair exams, though some find the course design challenging.

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

Reviewers consistently report that exams are extremely difficult, often unrelated to lectures or homework, with low averages and insufficient time for paper-based tests.

Bi Cheng Wu receives mixed reviews; while some find him lenient and patient, others criticize his disorganized lectures, vague feedback, and poor communication.

Topics

  • Probability models and the central limit theorem
  • Monte Carlo simulation
  • Hypothesis testing
  • Bayesian inference
  • Linear and logistic regression
  • Analysis of Variance (ANOVA)
  • The bootstrap method
  • Random forests and cross-validation

Skills

  • Data exploration and modeling using R
  • Statistical inference and advanced modeling techniques
  • Reproducible data analysis and communication

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

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AB
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BC
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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.

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Selected offering source records
STAT 340 · Fall 2026

Data Science Modeling II

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": "1272:932:025501",
    "course_id": "STAT 340",
    "course_uid": "course_d2d0c15e32bd1631a6074548",
    "term_id": "1272",
    "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
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-09-07

Download the original dataset
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.

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Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "884a51ec5f167b92af0a8525f50504fb3d61042fccd619d3d7890e0ef3b14d02",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}