Fall 2026

Introduction to Computational Statistics

Introduction to computational statistics focusing on inference principles for complex data structures using numerical methods like Monte Carlo and optimization.

no offering record for this termCredits unavailable
Recorded instructors · Fall 2026 No instructors listed

Summary

1 / 5

Historical reviews of Fangfang Wang: Homework is described as really long, vague, and huge parts of the grade, requiring significant self-study due to rushed lectures.

Grade history

average GPA
letter grades
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Prerequisites

Course map

STAT/​MATH 310 and (STAT 333 or 340), graduate/professional standing, or declared in Statistics VISP

COMPSCI/STAT 471

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

Prerequisite text tree

Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

Historical reviews of Fangfang Wang: Fangfang Wang's course features long, vague programming assignments that heavily impact grades, with no traditional exams but including quizzes and a final project. Lectures are criticized for covering excessive content too quickly, leading students to self-teach relevant material.

FANGFANG WANG is recorded teaching in Spring 2018, Spring 2019. 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

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Student experience

the class

Historical reviews of Fangfang Wang: The course is heavily CS-focused with no exams, relying instead on long programming assignments, quizzes, and a final project.

Recent recorded grades — Spring 2020: 3.84 GPA, 89.7% A/AB (n=29 letter grades); Fall 2020: 2.50 GPA, 31.6% A/AB (n=19 letter grades); Fall 2025: 3.74 GPA, 94.7% A/AB (n=19 letter grades).

difficulty & workload

Historical reviews of Fangfang Wang: Homework is described as really long, vague, and huge parts of the grade, requiring significant self-study due to rushed lectures.

Historical reviews of Fangfang Wang: Lectures covered too much content in too little time, leading some students to tune out and teach themselves relevant material.

Topics

  • Numerical linear algebra, optimization, Monte Carlo, and graph theory
  • Bootstrapping, permutation, Bayesian inference, EM algorithm, and multivariate analysis

Skills

  • Developing inference principles for complex data and computations
  • Applying numerical linear algebra, optimization, Monte Carlo, and graph theory to statistical inference
  • Implementing bootstrapping, permutation tests, Bayesian inference, EM algorithm, and multivariate analysis

Grades

Latest available · Fall 2025— not enough history to project Fall 2026.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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Grades over time

Through Fall 2025

More grade details Grade mix, volume & source data

Not enough comparable courses for Fall 2026 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

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Raw records
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Student reviews

Original comments behind the course and instructor summaries.

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Grade history

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Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "5fc79bb1d0418767ab9d5ce3529d7dce3f09e0683ccaad4ff3a47a937e78416e",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}