Course Design > Course Goals/Syllabus Database > Spatiotemporal Data Analysis

Spatiotemporal Data Analysis

Author Profile
Michael Evans

University of Arizona / Laboratory of Tree-Ring Research
University with graduate programs, including doctoral programs

Course URL:
Subject: Geoscience:Atmospheric Science
Resource Type: Course Information:Goals/Syllabi, Course Information
Grade Level: College Lower (13-14)
Ready for Use: Course Goals Only
Theme: Teach the Earth:Course Topics:Atmospheric Science
Course Type: Entry Level
Topics: Atmosphere
Course Size:

less than 15

Course Context:

This will be a course for lower division graduate students in geosciences, geography, and atmospheric sciences. Prerequisites will likely be courses in basic statistics and time series analysis. Format likely to be a 3 hour weekly computer lab meeting with limited lecture and dicussion components.

An experimental version of this course may be offered in Spring 2004.

Course Goals:

1. Analyze and interpret principal features resolvable in three-dimensional gridded climate data products.

2. Understand the strengths and weaknesses of empirical basis function-based statistical tools.

3. Critically assess similar analyses in the climate dynamics literature.

How course activities and course structure help students achieve these goals:

Students will perform analysis of an actual historical climate data set themselves, in incremental fashion, building the software tools necessary for each step of the analysis (Goal 1). They will also read, critique and discuss papers describing these steps (Goals 2,3).

Skills Goals

4. Learn and apply elementary linear/matrix algebraic tools for performing these analyses.

5. Learn to program in a high-level (vector/matrix) scripting language.

6. Learn elements of the peer-review process.

How course activities and course structure help students achieve these goals:

Students will complete simple 'pencil and paper' exercises in which they work with the basic statistical principles, prior to coding software to perform the same tasks on large datasets (Goals 4,5). They will also critique similar published analyses in prepared short written statements and orally in class (Goal 6).

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