Spring 2027

Data Science Modeling I

Data Science Modeling I introduces reproducible data management, modeling, analysis, and statistical inference using R through hands-on case studies.

offering recorded4 credits

Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Bi Cheng Wu

Graded work was take-home and open-note, but it took a long time; students needed to stay on top of content and spend significant effort on homework.

What you’ll be able to do

wrangle data: transform data, possibly from multiple sources, into a form convenient for analysis
explore data: visualize and summarize data, generate questions/hypotheses, and address them
program: write R code using the R Studio integrated statistical computing environment to carry out reproducible data analysis
model data: use probability and random variables in statistical computing environment to carry out reproducible data analysis
interpret data: explain what can be inferred from the data analysis and make predictions
communicate: use R Markdown to integrate prose, visualizations, code, interpretation, and results
collaborate: work with other students to solve data challenges
make statistical inferences: employ confidence intervals and hypothesis tests using both computational and standard methods such as binomial and t-tests

Source: UW Guide

Grade history

↗

average GPA
letter grades

All recorded terms · compare terms & instructors

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

BI CHENG WU is recorded teaching in Fall 2021, Spring 2022, Fall 2022, Spring 2023, Fall 2023, Spring 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

BRET LARGET is recorded teaching in Fall 2019, Fall 2020, Fall 2021, Fall 2022, Spring 2023, Fall 2023, Spring 2024, Fall 2024. Recorded history may be incomplete and does not establish a future schedule.

CAMERON JONES is recorded teaching in Spring 2024, Fall 2024. Recorded history may be incomplete and does not establish a future schedule.

SAHIFA SIDDIQUA 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

Spring 2027

Schedule loads here as you scroll.

Section	Mode	Enrolled / capacity
LEC 001	Classroom Instruction	0 / 126
LEC 002	Classroom Instruction	0 / 126
DIS 311	Classroom Instruction	0 / 21
DIS 312	Classroom Instruction	0 / 21
DIS 313	Classroom Instruction	0 / 21
DIS 314	Classroom Instruction	0 / 21
DIS 315	Classroom Instruction	0 / 21
DIS 316	Classroom Instruction	0 / 21
DIS 321	Classroom Instruction	0 / 21
DIS 322	Classroom Instruction	0 / 21
DIS 323	Classroom Instruction	0 / 21
LEC 003	Classroom Instruction	0 / 126
DIS 324	Classroom Instruction	0 / 21
DIS 325	Classroom Instruction	0 / 21
DIS 326	Classroom Instruction	0 / 21
DIS 331	Classroom Instruction	0 / 21
DIS 332	Classroom Instruction	0 / 21
DIS 333	Classroom Instruction	0 / 21
DIS 334	Classroom Instruction	0 / 21
DIS 335	Classroom Instruction	0 / 21
DIS 336	Classroom Instruction	0 / 21

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 · Sahifa Siddiqua

Sahifa Siddiqua’s notes were well-organized, and she explained topics clearly while remaining open to questions during and after class.

Recent recorded grades — Spring 2025: 3.23 GPA, 48.8% A/AB (n=686 letter grades); Fall 2025: 3.19 GPA, 53.1% A/AB (n=746 letter grades); Spring 2026: 3.03 GPA, 41.4% A/AB (n=599 letter grades).

difficulty & workload

Past student reviews · Bi Cheng Wu

Graded work was take-home and open-note, but it took a long time; students needed to stay on top of content and spend significant effort on homework.

Past student reviews · Sahifa Siddiqua

One student felt the instructor did not explain concepts in class and expected them on midterms, which included additions that were practically invisible.

Topics

Probability and random variables
Statistical inference for proportions and means
Simple linear regression
Data graphics and visualization

Skills

Write R code in RStudio for reproducible analysis
Transform data from multiple sources for analysis
Apply confidence intervals and hypothesis tests
Perform simple linear regression

Grades

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.

5 earlier forecasts · 0.15 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 240 · Spring 2027

Data Science Modeling I

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": "1274:932:025403",
    "course_id": "STAT 240",
    "course_uid": "course_03f8aaae24bac7658b319ca8",
    "term_id": "1274",
    "source_course_id": "025403",
    "source_subject_id": "932",
    "title": "Data Science Modeling I",
    "credits_min": 4,
    "credits_max": 4,
    "typically_offered": "Not Applicable"
  }
]

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

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": "dc9db8cb8aae12844f4170aff2fd108a089240a99baa083e3f8bf4e81bc02998",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}

Data Science Modeling I

Summary

What you’ll be able to do

Grade history

Prerequisites

Professors

Calendar & sections

Student experience

the class

difficulty & workload

Topics

Skills

Grades

Spring 2027 · Projected

Grades over time

Where this course fits relative to UW–Madison

Sources & history

Catalog & offerings

Student reviews

Grade history

Where this course fits relative to