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Meetings: Tue/Thu 09:30-10:45 in 105 Robinson Hall

Instructor: Andreas Muenchow

Goal: Provide each graduate student with a set of tools to confidently handle data analysis tasks in both time (space) and frequency (wave number) domains.

Format: This is a traditional lecture style course, however, most learning will take place via a set of computer projects that translate lecture materials into tested code.


Expectations: Each student shall be able to develop, apply, and critically evaluate

  • Auto-spectral analyses
  • Digital filters
  • Linear systems
  • Least-squares function fitting, and
  • Empirical orthogonal functions

Grading: 80% for problem-based projects, 10% homeworks, 10% individual in-class participation

Exam: None, but I may use occasional quizzes related to prior lectures (always open book).
Wed Aug.-21 2019