Yes, but the hands-on work is guided rather than project-based. You'll work through Python notebook labs and practice exercises that let you test ideas such as the Karplus-Strong algorithm, plot Fourier results, or examine signal examples like dial tones. That practice helps you connect the math to signals you can hear, visualize, and interpret.

You'll cover discrete-time signals, the vector-space view of signals, and frequency-domain analysis. The course teaches core Fourier tools such as the DFT and STFT, then uses them to interpret audio, speech, and communication signals. Overall, it helps you move between time and frequency representations and reason about how digital signals behave.

After finishing, you should be able to analyze sampled signals with common Fourier representations and explain them using the language of vectors, bases, and frequency. For example, you could inspect a DFT plot to identify dominant components in a musical sound or another discrete-time signal. That's a solid outcome if your goal is to interpret and analyze signals in a principled way.

It's more concept-focused, with guided practice to reinforce the ideas. Most of the course is spent understanding how signals, frequency analysis, and the underlying math fit together, while the labs and exercises let you test those ideas on concrete examples.