Spring 2027

Statistical Data Visualization

Covers statistical data visualization techniques in data science, including data prep, EDA, spatial/graph data, dimensionality reduction, and interactive models.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Kris Sankaran

The workload included a significant group project, homework portfolio, and exams, though some students found the assessment structure manageable.

What you’ll be able to do

  • Apply preprocessing strategies, including outlier removal, missing data imputation, and tidying, in a way that supports downstream visualization

  • Develop a vocabulary of visual encoding that support exploration of geospatial, temporal, tree-structured, and network data, and demonstrate facility implementing them using packages in the R programming language

  • Design dynamic queries that support interactive visualization of heterogenous data and demonstrate facility implementing them using the shiny package in the R programming language

  • Design effective visualizations to summarize the results of dimensionality reduction and clustering algorithms

  • Use visual artifacts derived from complex statistical machine learning models to discuss the patterns they learn and mistakes they make

  • Recognize chart junk in real-word visualizations and propose improved alternatives

Source: UW Guide

Grade history

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

Prerequisites

Course map

(STAT 240 or 303), graduate professional/standing, or declared in Statistics VISP

    • take one
    • graduate professional/standing
    • declared in Statistics VISP
    take one
STAT 436

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Any of

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

CAMERON JONES is recorded teaching in Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

KRIS SANKARAN is recorded teaching in Spring 2023, Spring 2024, Fall 2024, Spring 2025, Fall 2025. 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 · Kris Sankaran

Students described the course as engaging and valuable, with clear online notes and a flipped format that emphasizes independent reading.

Recent recorded grades — Spring 2025: 3.48 GPA, 67.7% A/AB (n=96 letter grades); Fall 2025: 3.53 GPA, 76.1% A/AB (n=92 letter grades); Spring 2026: 3.46 GPA, 75.8% A/AB (n=95 letter grades).

difficulty & workload

Past student reviews · Kris Sankaran

The workload included a significant group project, homework portfolio, and exams, though some students found the assessment structure manageable.

Past student reviews · Kris Sankaran

Experiences varied regarding organization, with one student finding materials scattered across files, while another noted that clear rubrics made grading predictable.

Topics

  • Spatial, tabular, and graph data
  • Dimensionality reduction
  • Model visualization and interpretability
  • Interactive queries and navigation

Skills

  • Data preprocessing for visualization
  • Visual encoding with R packages
  • Interactive visualization with Shiny
  • Critiquing and improving visualizations

Grades

Latest available · Spring 2026— 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 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

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.

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Selected offering source records
STAT 436 · Spring 2027

Statistical Data Visualization

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": "1274:932:026276",
    "course_id": "STAT 436",
    "course_uid": "course_f57701752c565e5b0c704a1d",
    "term_id": "1274",
    "source_course_id": "026276",
    "source_subject_id": "932",
    "title": "Statistical Data Visualization",
    "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 1Download part 2
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "42503677cdae38bf96a8f9715e2d3e18d07f1a2c9ac85488542181d8ce782c5f",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}