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There is no course textbook.
The course was previously taught from Bendat and Piersol, 4th edition, 2010. Older versions should be fine, but you'll have to dig a bit if equation numbers have changed etc.
Apparently this is now available online! Link
Bendat and Piersol is a relatively formal statistics text, which is helpful for clarifying concepts after they have been thought about a bit. Also, such explanations can sometimes be missed during class, or not explained properly by the professor, so it is nice to have a formal text to fall back on.
It does not do almost anything on filtering, which we will cover as well. Fortunately, there are plenty of online resources.
Other texts
- I often refer to Emery and Thomson simply because it is a compendium of the most commonly used techniques in my field. They also have a lot of real-world examples.
- Press et. al., (Numerical Recipes) is the definitive resource for basic computing algorithms. Of course, a lot of these algorithm's are available as libraries for common languages, so perhaps not as useful as it was 20 years ago.
- A text that is at a slightly lower level, but has lots of tutorials (in Matlab!) is by Trauth, "MATLAB recipes for earth sciences" and is available online at the library: Link. It has very little derivation, and no discussion of confidence intervals or error analysis, and therefore is a bit beneath this course, but the logical layout is nice.
- An excellent set of notebooks, similar to what I am attempting with this course are Python for Signal Processing. I found this after I started, but a lot of it is relevant to this class.