Friday, January 11, 2013

Open Course Ware @ FIX University

Computational Methods for Data Analysis

Nathan Kutz

Exploratory and objective data analysis methods applied to the physical, engineering, and biological sciences.

Nathan Kutz
University of Washington

Upcoming Deadlines



Welcome to Week 1 of Computational Methods for Data Analysis!

Objective data analysis starts with a study of time-frequency analysis. Fourier transforms and wavelet transforms are the most powerful tools used in time-frequency analysis. Lecture 1 introduces Fourier transform, it's properties, and an efficient algorithm to compute it. Lecture 2 draws examples of noisy data from RADAR and SONAR, we will explore and construct typical RADAR signals. Lectures 2 and 3 explore in detail "Filtering" and "Averaging" as de-noising tools to extract meaningful frequency signatures from RADAR data. Example codes will be developed in-class with MATLAB. You will have the background needed to take Quizzes 1, 2 and 3 after viewing Lectures 1, 2 and 3 respectively.
Mon 7 Jan 2013 12:02 AM PST

Welcome to Computational Methods for Data Analysis

Thank you for joining the Computational Methods for Data Analysis course! Please take a few moments to read through the course welcome page followed by watching the introductory and week one lecture videos. There is a lot of useful information there about the course.

For now, you should plan to allocate between five and 10 hours per week on the course. There will be roughly two hours of lectures per week, as well as weekly quizzes (graded automatically) for each lecture.

In this course you will learn how to recognize and solve numerically practical problems which may arise in your research. Given the computational nature of the course, access to MATLAB ( or Octave ( is essential. MATLAB provides student editions for $99 that can be downloaded via the web. Octave is a free (or by donation) alternative to MATLAB that can also be downloaded and installed via the web. Either software should suffice for all the needs of the course, but MATLAB is the strongly recommended alternative.

To complement the course, a set of notes detailing each individual lecture is included. The notes should be read through thoroughly and routinely as all the course content is contained therein. It is imperative that the student engage in a focused effort to learn the notes as the lectures are simply a supplement to the notes, not the other way around. You are also encouraged to interact with each other within the discussion forums in order to arrive at the various solution sets. 

Course Lecture Packet:
Download: Course Lecture Notes Packet 

Again, welcome, and I hope that you enjoy this course!

Dr. Nathan Kutz
Mon 7 Jan 2013 12:00 AM PST 

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