Spring 2027

Navigating the Data Revolution: Concepts of Data & Information Science

Introduction to Data Science with hands-on projects analyzing real-world data and creating visualizations, covering statistics, data management, programming, and ethics.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Jeff Nyhoff

The main demands were showing up for attendance at the start and end of class and following along with straightforward material.

What you’ll be able to do

  • Understand and communicate the different types of data science projects, and be familiar with the tools and methods used in data science

  • Apply data analysis to primary data sources to solve problems

  • Communicate about data and analyses clearly and persuasively in written, spoken, and graphical/visualized form

  • Identify probability distributions commonly used as foundations for statistical modeling

  • Reason around ethical and privacy issues in data science conduct and apply ethical practices.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
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All recorded terms · compare terms & instructors

Prerequisites

Course map

Satisfied Quantitative Reasoning (QR) A, satisfied Communications A, and sophomore standing

    • Satisfied Quantitative Reasoning (QR) A
    • satisfied Communications A
    • sophomore standing
    take all
LIS 440

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

Prerequisite text tree
  • All of
    • Satisfied Quantitative Reasoning (QR) A
    • satisfied Communications A
    • sophomore standing

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

JEFF NYHOFF is recorded teaching in Spring 2023, Spring 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 001Classroom Instruction0 / 1300

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 · Jeff Nyhoff

Students found Jeff Nyhoff's course very easy, with repetitive assignments and a lenient grading style, though some felt they learned little.

Recent recorded grades — Spring 2025: 3.81 GPA, 91.2% A/AB (n=102 letter grades); Fall 2025: 3.89 GPA, 92.7% A/AB (n=137 letter grades); Spring 2026: 3.91 GPA, 95.1% A/AB (n=103 letter grades).

difficulty & workload

Past student reviews · Jeff Nyhoff

The main demands were showing up for attendance at the start and end of class and following along with straightforward material.

Past student reviews · Jeff Nyhoff

Lectures were described as boring or confusing, with one student noting the instructor sometimes went off on tangents while another found the pace too fast.

Topics

  • Analysis of real-world data
  • Graphical data visualization
  • Statistical tests
  • Data ethics

Skills

  • Statistical tests, data management, data programming, data ethics, and data visualization
  • Development of graphical visualizations
  • Applying data analysis to primary data sources

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
LIS 440 · Spring 2027

Navigating the Data Revolution: Concepts of Data & Information Science

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": "1274:544:026071",
    "course_id": "LIS 440",
    "course_uid": "course_eefb8f2e349aefa0dedfe2e4",
    "term_id": "1274",
    "source_course_id": "026071",
    "source_subject_id": "544",
    "title": "Navigating the Data Revolution: Concepts of Data & Information Science",
    "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": "22c4a614e5440e2a4aeb73fb8ec4c07781b778e48678d8b696abf5c9608d6a1d",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}