Introductory course on applied geoscience data analysis covering uncertainty propagation, hypothesis testing, linear regression, time series, and experimental design for rock, soil, and water sampling.
Quantitatively describe and display data distributions in geoscience applications.
Calculate and propagate uncertainties in geoscience data.
Test hypotheses about geoscience data using central tendencies and uncertainties.
Describe how linear regression works and use it to quantify trends and their uncertainties in geoscience data.
Recognize and account for common pitfalls in applications of data analysis to the geosciences.
Analyze time series and directional data across the geosciences.
Design geoscience data collection for testing hypotheses.
Source: UW Guide
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Raw average: 3.48/5 from 23 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.
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| Section | Mode | Enrolled / capacity | Waitlist |
|---|---|---|---|
| LAB 301 | Classroom Instruction | 0 / 20 | 0 |
| LEC 001 | Classroom Instruction | 0 / 20 | 0 |
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Introduction to the fundamentals of applied geoscience data analysis, focusing on solving practical problems using real real-world data. Covers applied data analysis topics that are essential across a wide range of geoscience fields, including geophysics, geochemistry, hydrogeology, structural geology, sedimentology, geochronology, glacial geology, and geomorphology. Covers the use of example geoscience data sets to display and describe data; calculate and propagate uncertainties; test hypotheses; fit functions to data with linear regression and multiple linear regression; account for artifactual and serial correlation; analyze time series and directional data; and design experiments to optimize collection of rock, soil, and water samples.
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Geoscience Data Analysis
Recorded 2026-10-14[
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]Original comments behind the course and instructor summaries.
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Recorded model configuration, reasoning, and tool conversations.
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