Spring 2027

R for Statistics II

R For Statistics II covers the statistical language R, focusing on writing conditional expressions, loops, and functions, manipulating data structures, extracting text data, and creating visualizations.

offering recorded1 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 5

Past student reviews · Bo Yang

One student reported earning an A despite the instructor's lack of support, citing prior high-pace work experience that covered the course content.

Grade history

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

All recorded terms · compare terms & instructors

Prerequisites

Course map

STAT 303

STAT 304 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

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.

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 003Online Only0 / 800
LEC 004Online Only0 / 100

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records

No records available.

Student experience

the class

Past student reviews · Bo Yang

Past students described the instructor as rude and unhelpful, noting he did nothing to assist with gaining R skills. One student found interactions painful, while another advised avoiding him entirely.

Recent recorded grades — Spring 2025: 3.76 GPA, 84.2% A/AB (n=57 letter grades); Fall 2025: 3.72 GPA, 80.6% A/AB (n=67 letter grades); Spring 2026: 3.77 GPA, 92.2% A/AB (n=64 letter grades).

difficulty & workload

Past student reviews · Bo Yang

One student reported earning an A despite the instructor's lack of support, citing prior high-pace work experience that covered the course content.

Past student reviews · Bo Yang

Students found the instructor difficult to interact with, with one avoiding email and class conversations due to perceived rudeness. Another student stated he could not teach.

Topics

  • R programming constructs including conditionals, loops, and functions
  • Data manipulation using matrices and arrays
  • Text data extraction
  • High-level data visualization

Skills

  • Writing conditional expressions, loops, and functions in R
  • Manipulating data matrices and arrays
  • Extracting data from text
  • Making high level visualizations of data

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 · 0.06 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 304 · Spring 2027

R for Statistics 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": "1274:932:025056",
    "course_id": "STAT 304",
    "course_uid": "course_2364ea4d92955fbad9988956",
    "term_id": "1274",
    "source_course_id": "025056",
    "source_subject_id": "932",
    "title": "R for Statistics II",
    "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
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 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "226d72688e61c921be01c9309e98c0ccfc1eff13c82ab2eb3cb648dc578b93a8",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}