Spring 2027

Data Analytics for Economists

Use core economic datasets such as the Panel Study on Income Dynamics, Consumer Expenditure Survey, National Income and Product Accounts, and the American Community Survey for quantitative economic research. Learn to clean and manipulate data to create datasets usable for economic research and to implement theory-based and atheoretic econometric models.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 7

Past student reviews · Kim Ruhl

One student rated the difficulty as moderate, noting the structured lectures helped them learn Python from zero to one.

What you’ll be able to do

  • Be familiar with core data sets often used in microeconomics, macroeconomics, industrial organization, and finance

  • Assemble manipulate economic data into datasets that can be used to conduct economic research

  • Recognize the organization of economic data into cross-section, panel, and time series datasets

  • Use python and pandas to efficiently manipulate and analyze economic data

  • Identify the strengths and weaknesses of theory-based and atheoretic econometric models

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
ECON 770

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

Prerequisite text tree
  • Graduate/professional standing

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

KIM RUHL is recorded teaching in Fall 2020, Fall 2021, Fall 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

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.

Meeting source records
Download part 1

Student experience

the class

Past student reviews · Kim Ruhl

Past students found this course inspirational for learning Python from scratch, providing a well-organized path into data analysis and visualization.

Recent recorded grades — Fall 2023: 3.74 GPA, 96.0% A/AB (n=75 letter grades); Fall 2024: 3.77 GPA, 94.8% A/AB (n=58 letter grades); Fall 2025: 3.77 GPA, 94.2% A/AB (n=52 letter grades).

difficulty & workload

Past student reviews · Kim Ruhl

One student rated the difficulty as moderate, noting the structured lectures helped them learn Python from zero to one.

Past student reviews · Kim Ruhl

A past student appreciated the hands-on experience with data in Python, which sparked their interest in programming and further learning.

Grades

Latest available · Fall 2025— 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 Fall 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2025 · 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

1316 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

No offering records for the selected term.

Raw records 
[]
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
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": "cf61e57a8ad9b87fd8fe6629a86095c6488d3b922bc92a711457d0a8bd1b82a4",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}