The modular style used at AllSignalProcessing.com enables a limitless number of ways to proceed through the material. You can use the material as a reference and study topics that you need to learn. Or you can proceed through the lessons in a systematic fashion. The lessons are ordered sequentially so you will naturally acquire the prerequisite knowledge if you follow them in the order provided. Several example signal processing curricula are provided here to illustrate several topical courses of study.
Typical University-Level Undergraduate Signal Processing Curriculum
Most undergraduate signal-processing classes will emphasize sampling and reconstruction of signals, the discrete Fourier transform, system analysis using the z-transform, and frequency selective filter design. This type of class often assumes knowledge of Fourier transforms and some systems theory and emphasizes the mathematical underpinnings of the methods.
Broad Range of Signal Processing Topics with No Prior Background
One of the tenets of AllSignalProcessing.com is that a background in calculus is sufficient to learn signal processing. I regularly teach a course that covers a wide range of topics in signal processing for students with no prior background. We begin with the definition of a signal and end with topics that are typically covered in advanced graduate-level courses. The goal is to become a proficient user of signal processing methods.
Topical Signal Processing Curricula
There are a large number of ways to arrange the lessons to cover specific topics. Two example topical signal processing curricula are provided here.
If your main goal is to learn signal processing techniques for estimating spectra from data, then this curriculum is for you. It leads you through sampling, the discrete Fourier transform, the role of windows, and methods for estimating power spectra and coherence.
Filtering and Filter Design
This sample curriculum is geared toward helping you learn signal processing methods for filtering data and designing filters.