Spring 2027

Data Science with R

An iterative data science course focusing on data collection, wrangling, analysis, and communication using R and statistical/machine learning tools.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Karl Rohe

Students described the course as easy and suggested it was a straightforward path to earning an A.

What you’ll be able to do

  • Describe and apply the key steps taken in most data science projects and how these steps fit into a coherent whole

  • Identify how a data set can be used for a specific purpose

  • Clean and analyze data

  • Communicate the results of data analysis

  • Develop agile and reproducible code that enables iterative development of a data science project using the tools in the tidyverse

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

(STAT 333 or 340) and (MATH 320, 340, 341, 345, or 375), graduate/professional standing, or declared in Statistics VISP

STAT 433

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

KARL ROHE is recorded teaching in Fall 2021, Fall 2022. 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 / 750
LEC 002Classroom Instruction0 / 200

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

Karl Rohe taught the course with passion that made the class enjoyable and sparked interest in the subject.

Recent recorded grades — Fall 2021: 3.56 GPA, 78.9% A/AB (n=57 letter grades); Fall 2022: 4.00 GPA, 100.0% A/AB (n=63 letter grades).

difficulty & workload

Past student reviews · Karl Rohe

Students described the course as easy and suggested it was a straightforward path to earning an A.

Past student reviews · Karl Rohe

Students noted that while the professor was knowledgeable, his tendency to drift from the topic sometimes made explanations less clear.

Topics

  • Data collection, wrangling, analysis, and communication
  • Scientist, coder, mathematician, methodologist, and skeptic stances
  • Computational tools in R
  • Statistical and machine learning tools

Skills

  • Applying key steps in data science projects
  • Identifying dataset utility for specific purposes
  • Cleaning and analyzing data
  • Communicating data analysis results
  • Developing agile and reproducible code with tidyverse

Grades

Latest available · Fall 2022— 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 Fall 2022

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2022 · 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

1213 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 433 · Spring 2027

Data Science with R

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": "1274:932:025928",
    "course_id": "STAT 433",
    "course_uid": "course_c7a9aed08e9193ac68e21e89",
    "term_id": "1274",
    "source_course_id": "025928",
    "source_subject_id": "932",
    "title": "Data Science with R",
    "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": "4279ae9083f02e7fa1333dcb4d38d5030618ecde42dfe2d8a0f8e2d63b17069c",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}