Data Science for Agricultural and Applied Economics
Introductory data science course for agricultural and applied economics focusing on Python and R for data loading, cleaning, visualization, and storage.
Summary
What you’ll be able to do
Use Python and R to load, clean and visualize data.
Prepare and manipulate/transform data to support analysis in Python and R.
Create effective visualizations to communicate complex data.
Implement Simple Linear Regression in Python and R.
Apply the above R and Python methods to public-use datasets related to agricultural and applied economics.
Source: UW Guide
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Prerequisites
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- Graduate/professional standing
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- Graduate/professional standing
Professors
Spring 2027No instructors recorded for this selection.
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Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
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Student experience
the class
Introduction to data and data processing using both Python and R programming languages. Concepts covered include loading data, data acquisition, cleaning data, visualization/exploring data, and storing data.
difficulty & workload
No workload feedback recorded.
Topics
- Data lifecycle: loading, acquisition, cleaning, visualization, exploration, and storage.
- Simple linear regression.
- Application of data science methods to agricultural and applied economics datasets.
Skills
- Loading, cleaning, and visualizing data using Python and R.
- Preparing, manipulating, and transforming data for analysis.
- Implementing simple linear regression models.
- Data acquisition, exploration, and storage techniques.
Grades
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Catalog & offerings
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Grade history
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Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
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Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "54b0b1b33b3d9f29bf226d6289872a8f1afb1d8e1665fcd39083965b110c57f1",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}