Spring 2027

Fundamentals of Data Analytics for Economists

Introduction to data underlying quantitative economic analysis. Covers formulating research questions, accessing economic data sources, conducting preliminary and formal analysis, and reporting findings professionally.

offering recorded3–4 credits
Recorded instructors · Spring 2027 John Brauer3.7/5

Summary

1 / 6

Past student reviews · John Brauer

A student described the class as essentially coding econometrics in Python with almost no code explanation, finding it impossible without prior CS220 and econometrics training.

What you’ll be able to do

  • Understand the economist's research process: formulating a question; preliminary and formal analysis; and communicating results.

  • Effectively communicate quantitative economic ideas through figures.

  • Access and analyze panel datasets, such as the Panel Study of Income Dynamics, and time series datasets, such as the National Income and Product Accounts.

  • Use modern tools to effectively manage data and perform analysis.

Source: UW Guide

Grade history

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

Prerequisites

Course map

[ECON 310, (STAT 240 and 340), or (STAT 303 and 333)], and (ECON 301 or 311)

ECON 570

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

Prerequisite text tree

Professors

Spring 2027
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 3.67/5 from 3 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.

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

Past student reviews · John Brauer

John Brauer’s ECON 570 required coding econometrics in Python. One student found it impossible without CS220 and econometrics, noting minimal code explanation, while another with data science background found it easy and valuable for improving Python skills.

Students described John Brauer as fair and understanding, noting he actively tried to give partial credit. Attending class and paying attention was reported to lead to an enjoyable experience, with the professor not trying to sabotage students.

Recent recorded grades — Spring 2025: 3.81 GPA, 92.2% A/AB (n=129 letter grades); Fall 2025: 3.82 GPA, 94.6% A/AB (n=74 letter grades); Spring 2026: 3.79 GPA, 91.1% A/AB (n=157 letter grades).

Historical instructors & teaching patterns

Past student reviews · Kim Ruhl, Ashley Swanson

Historical students described Professor Ruhl’s lectures as clear, noting the course was project-heavy with tricky exams. Professor Swanson’s lectures were concise, informative, and fast-paced, covering distinct topics without deep detail. While Ruhl received mixed feedback on engagement, Swanson was noted as friendly and tough but curved, with students finding the class straightforward and not overly time-consuming.

ASHLEY SWANSON is recorded teaching in Spring 2023, Spring 2024, Fall 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

JOHN BRAUER is recorded teaching in Spring 2025, Fall 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

KIM RUHL is recorded teaching in Spring 2020, Spring 2022, Fall 2023. 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 / 720
DIS 301Classroom Instruction0 / 240
DIS 302Classroom Instruction0 / 240
DIS 303Classroom Instruction0 / 240
LEC 002Classroom Instruction0 / 950
DIS 311Classroom Instruction0 / 240
DIS 312Classroom Instruction0 / 240
DIS 313Classroom Instruction0 / 240
DIS 304Classroom Instruction0 / 240
DIS 314Classroom Instruction0 / 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

Past student reviews · John Brauer

One student found the course enjoyable with John Brauer, noting he was fair and tried to give partial credit to those who attended and paid attention.

Recent recorded grades — Spring 2025: 3.81 GPA, 92.2% A/AB (n=129 letter grades); Fall 2025: 3.80 GPA, 92.9% A/AB (n=156 letter grades); Spring 2026: 3.79 GPA, 91.1% A/AB (n=157 letter grades).

difficulty & workload

Past student reviews · John Brauer

A student described the class as essentially coding econometrics in Python with almost no code explanation, finding it impossible without prior CS220 and econometrics training.

Past student reviews · John Brauer

A Data Science student found the class extremely easy and low-stress, reporting an easy A and improved Python skills from the good content provided.

Topics

  • Data underlying quantitative economic analysis
  • Economic data sources
  • Panel datasets (PSID) and time series datasets (NIPA)
  • Economist's research process

Skills

  • Formulate research questions, access economic data sources, conduct preliminary and formal analysis, and report findings professionally.
  • Access and analyze panel datasets like PSID and time series datasets like NIPA.
  • Use modern tools to manage data and perform analysis.
  • Communicate quantitative economic ideas through figures.

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
ECON 570 · Spring 2027

Fundamentals of Data Analytics for Economists

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": "1274:296:025570",
    "course_id": "ECON 570",
    "course_uid": "course_05d67a715fdacf96f5e1d957",
    "term_id": "1274",
    "source_course_id": "025570",
    "source_subject_id": "296",
    "title": "Fundamentals of Data Analytics for Economists",
    "credits_min": 3,
    "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 2026-10-14

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": "164ab4048213ee8b45447635ca4d83417c6d26f388d96ec28a44aabfbe5b70cc",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}