Fall 2026

Data Science Modeling I

STAT 240 introduces reproducible data science modeling and statistical inference using R and R Markdown.

offering recorded4 credits
Recorded instructors · Fall 2026 Sahifa Siddiqua2.6/5. Rinkle Rose RennyAnanya Datta +14 more

Summary

1 / 7

Students report that exams are significantly harder than lectures and homework, with unclear instructions and arbitrary difficulty. Self-study is often necessary due to the disconnect between taught material and assessments.

Grade history

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

Prerequisites

Course map

Satisfied Quantitative Reasoning (QR) A

  • Satisfied Quantitative Reasoning (QR) A
STAT 240 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
  • Satisfied Quantitative Reasoning (QR) A

Professors

Fall 2026

Sahifa Siddiqua

/5Adjusted rating
/5RMP difficulty
captured reviews
About this rating

Raw average: 1.91/5 from 33 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 2.0/5 raw quality · 3.9/5 difficulty · 26 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

Sahifa Siddiqua is praised for well-organized notes and clear explanations that help students prepare for exams. Reviewers note her detailed materials allow success even if lectures are skipped.

Many reviewers criticize her for poor teaching, claiming exams do not match lecture content or homework. Concerns include unresponsive emails, unclear instructions, and arbitrary grading difficulties.

Recent recorded grades — Spring 2025: 3.20 GPA, 44.9% A/AB (n=414 letter grades); Fall 2025: 3.29 GPA, 58.6% A/AB (n=198 letter grades); Spring 2026: 3.04 GPA, 38.9% A/AB (n=324 letter grades).

No course-specific feedback yet.

Recent recorded grades — Spring 2025: 3.27 GPA, 54.8% A/AB (n=272 letter grades); Fall 2025: 3.30 GPA, 58.3% A/AB (n=381 letter grades); Spring 2026: 3.01 GPA, 44.4% A/AB (n=275 letter grades).

Historical instructors & teaching patterns

Historical reviews of Bret Larget: Bret Larget demonstrates passion and helpfulness in office hours, though some students find his lectures disorganized or unclear. While he attempts to make content applicable, others report that he simply reads from slides, leading to confusion and a perception that the course material is difficult to learn through provided resources.

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.

MIRANDA RINTOUL is recorded teaching in Fall 2024, Spring 2025, Fall 2025, Spring 2026. 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

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction110 / 1441
DIS 314Classroom Instruction9 / 240
DIS 315Classroom Instruction16 / 240
DIS 316Classroom Instruction18 / 240
LEC 003Classroom Instruction121 / 1440
DIS 311Classroom Instruction19 / 240
DIS 312Classroom Instruction24 / 241
DIS 313Classroom Instruction24 / 240
DIS 331Classroom Instruction14 / 240
DIS 332Classroom Instruction21 / 240
DIS 333Classroom Instruction24 / 240
LEC 004Classroom Instruction163 / 2160
DIS 341Classroom Instruction24 / 240
DIS 334Classroom Instruction18 / 240
DIS 335Classroom Instruction20 / 240
DIS 336Classroom Instruction24 / 240
DIS 342Classroom Instruction23 / 240
DIS 343Classroom Instruction22 / 240
DIS 344Classroom Instruction11 / 240
DIS 345Classroom Instruction24 / 240
DIS 346Classroom Instruction19 / 240
LEC 002Classroom Instruction149 / 2160
DIS 321Classroom Instruction13 / 240
DIS 322Classroom Instruction24 / 240
DIS 323Classroom Instruction17 / 240
DIS 324Classroom Instruction15 / 240
DIS 325Classroom Instruction24 / 240
DIS 326Classroom Instruction24 / 240
DIS 327Classroom Instruction4 / 240
DIS 328Classroom Instruction22 / 240
DIS 329Classroom Instruction6 / 240
DIS 347Classroom Instruction24 / 240
DIS 348Classroom Instruction10 / 240
DIS 349Classroom Instruction6 / 240

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

Reviews for Sahifa Siddiqua are sharply divided. Some praise her organization and knowledge, while others report poor teaching, rude behavior, and exams that do not match course content.

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

Students report that exams are significantly harder than lectures and homework, with unclear instructions and arbitrary difficulty. Self-study is often necessary due to the disconnect between taught material and assessments.

Experiences range from well-organized notes to disorganized classes with unclear instructions. Students also report passive-aggressive email behavior and unresponsive office hours.

Topics

  • Data wrangling and visualization
  • Probability distributions
  • Statistical inference for proportions and means
  • Simple linear regression
  • R Markdown reporting

Skills

  • Reproducible data management and analysis
  • Data wrangling and R programming
  • Data graphics and visualization
  • Probability concepts and distributions
  • Statistical inference and regression
  • Report generation using R Markdown

Grades

Historical instructor

Fall 2026 · 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 · 0.15 GPA average error.

Grades over time

Through Fall 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

1283 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 240 · Fall 2026

Data Science Modeling I

Recorded 2026-09-07
Raw records
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:932:025403",
    "course_id": "STAT 240",
    "course_uid": "course_03f8aaae24bac7658b319ca8",
    "term_id": "1272",
    "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
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-09-07

Download the original dataset
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "e56ed7960106b657be62b41a657bc920b3fc8d2e6a37294f648c6fed3ac70045",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}