Fall 2026

R for Statistics I

STAT 303 teaches using the R language to manipulate data and perform exploratory data analysis.

offering recorded1 credits
Recorded instructors · Fall 2026 John Gillett4.2/5Bo Yang2.2/5Ming Pei +1 more

Summary

1 / 6

The course involves multiple quizzes, homework assignments, and an exam, with reviewers noting the workload is substantial despite the straightforward nature of the tasks.

Grade history

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

Prerequisites

Course map

STAT 240, 301, 302, 312, 324, 371,MATH/​STAT 310,ECON 310, GEN BUS 303, 304, 306, 307, 317,PSYCH 210,B M E 325,I SY E 210,SOC/​C&E SOC 360, graduate/professional standing, or declared in Statistics VISP

STAT 303 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

John Gillett

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

Raw average: 4.39/5 from 67 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: 4.8/5 raw quality · 3.0/5 difficulty · 5 reviews

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

John Gillett is praised for accessible online lectures, proactive assistance, and helpful explanations of R applications. Reviewers note his willingness to answer questions and support students with minimal CS experience.

Some students report difficulty hearing recorded lectures and finding examples hard to replicate. This reviewer also notes a hands-off grading style and challenges in applying examples to different contexts.

Recent recorded grades — Fall 2024: 3.46 GPA, 69.4% A/AB (n=36 letter grades); Fall 2025: 3.47 GPA, 72.7% A/AB (n=33 letter grades); Spring 2026: 3.72 GPA, 90.0% A/AB (n=30 letter grades). Includes jointly taught sections.

Bo Yang

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

Raw average: 1.68/5 from 56 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: 1.6/5 raw quality · 3.7/5 difficulty · 20 reviews

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

Bo Yang receives widespread criticism for poor lecture quality, dismissiveness, and lack of accessibility, with reviewers citing passive-aggressive behavior and unhelpful office hours. While one review notes sensible structure and another finds him merely okay, the majority report significant frustration with his teaching style and communication.

Recent recorded grades — Spring 2025: 3.83 GPA, 92.9% A/AB (n=127 letter grades); Fall 2025: 3.94 GPA, 96.2% A/AB (n=160 letter grades); Spring 2026: 3.95 GPA, 97.5% A/AB (n=161 letter grades). Includes jointly taught sections.

No course-specific feedback yet.

Recent recorded grades — Spring 2025: 3.83 GPA, 92.9% A/AB (n=127 letter grades); Fall 2025: 3.94 GPA, 96.2% A/AB (n=160 letter grades); Spring 2026: 3.95 GPA, 97.5% A/AB (n=161 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

Historical reviews of Alexander Covington: Alexander Covington's course relies on self-learning via online videos, with exams covering extensive details from homework. Reviewers report extremely high workload, tough grading, and a lack of homework solutions or feedback, leading some to avoid subsequent courses.

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

BO YANG is recorded teaching in Fall 2019, Spring 2020, Fall 2020, Fall 2021, Spring 2022, Fall 2022, 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.

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

ZHIFENG CHEN is recorded teaching in Fall 2024, Spring 2025, Fall 2025, 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 Instruction27 / 450
LEC 002Classroom Instruction3 / 30
LEC 003Online Only84 / 860
LEC 004Online Only3 / 100
LEC 005Online Only72 / 960

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

John Gillett receives high praise for clear explanations, accessible support, and well-structured online materials, making the course manageable even for students with minimal coding experience.

Recent recorded grades — Spring 2025: 3.83 GPA, 92.9% A/AB (n=127 letter grades); Fall 2025: 3.86 GPA, 92.2% A/AB (n=193 letter grades); Spring 2026: 3.91 GPA, 96.3% A/AB (n=191 letter grades).

difficulty & workload

The course involves multiple quizzes, homework assignments, and an exam, with reviewers noting the workload is substantial despite the straightforward nature of the tasks.

Bo Yang is frequently criticized for poor lecture quality, lack of accessibility, and dismissive communication, forcing students to rely heavily on self-study and external resources to succeed.

Topics

  • Data manipulation and exploratory data analysis.

Skills

  • Data manipulation and exploratory data analysis using R.

Grades

Historical instructor

Fall 2026 · 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.

5 earlier forecasts · 0.17 GPA average error.

Grades over time

Through Fall 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 303 · Fall 2026

R for Statistics I

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": "1272:932:025055",
    "course_id": "STAT 303",
    "course_uid": "course_9d705456a9a3f278c1b60cc6",
    "term_id": "1272",
    "source_course_id": "025055",
    "source_subject_id": "932",
    "title": "R for Statistics I",
    "credits_min": 1,
    "credits_max": 1,
    "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 2026-09-07

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.

Download part 1
Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "a35302be9061281dae9e5dc601f2d9b29947b4ac916aeb0b383e2f5957ee1ff5",
  "requirements_status": "needs_review",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}