The short version: for practical DSP, Udemy’s signal-processing courses (Mike X Cohen’s in particular) are the accessible route — search the Udemy catalog for current ratings. Engineering-track learners should weigh the MATLAB toolchain via our MATLAB courses guide, and EPFL’s classic DSP course remains free to audit on Coursera.
Last updated: July 2026. Written by Josh Hutcheson, OnlineCourseing editor. Every featured course was live-verified with current ratings this month.
QUICK VERDICT
Bottom line: Mike X Cohen’s Signal processing problems, solved in MATLAB and in Python (4.6★, updated June 2026) is the clear pick — problem-first teaching, both major languages, actively maintained. His Fourier transform course is the best deep-dive on DSP’s core concept, and EPFL’s Coursera course adds the university-grade theory track, free to audit.
- Best for: engineers, neuroscience/biomedical researchers, audio programmers, and data scientists working with sensor data
- Pricing: Udemy picks $10–20 on sale; the EPFL Coursera course is free to audit
- Skip if: you want an accredited credential — no DSP certification exists; the value here is skills
Check price: the top DSP course →
Signal processing — cleaning, filtering, and extracting meaning from time-varying data — underpins audio engineering, EEG and biomedical analysis, communications, radar, and a growing share of sensor-driven machine learning. It is also brutally dependent on one concept: the Fourier transform. Courses that hand-wave it leave you copying filter code you cannot debug. We re-verified every course on the previous version of this page this month; several 2021-era listings were dead (including one whose Udemy page now redirects to the homepage) and have been purged. The three below are live, current, and genuinely worth taking.
Fair warning on prerequisites: you will want comfort with complex numbers and basic statistics before diving in — our guide to the best statistics courses covers the latter. Signals are just structured time series, so the time series analysis courses make a natural next step once your DSP foundations are in.
1. Signal processing problems, solved in MATLAB and in Python (Udemy) — best overall
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4.6★ · 2,557 ratings · 20,316 students · last updated June 2026
Mike X Cohen is a computational neuroscientist who built his career on these exact methods, and it shows: the course is organized around problems — denoising a contaminated signal, designing and applying filters, detecting features, working with spectrograms — rather than a lecture-first theory tour. Every problem is solved twice, once in MATLAB and once in Python, which both future-proofs the skills and quietly teaches you that the concepts, not the syntax, are the point.
Updated June 2026 and actively maintained, it is the rare technical Udemy course you can buy without checking the recording date. It assumes some programming comfort in either language; total beginners should build basic Python or MATLAB fluency first.
2. Master the Fourier transform and its applications (Udemy) — best for the core concept
4.5★ · 2,615 ratings · 19,862 students · last updated June 2026
Also by Cohen, and the best standalone treatment of the concept every other DSP topic stands on. The course builds the Fourier transform from visual intuition — what it means to decompose a signal into sinusoids — up through the discrete transform, the FFT, spectral analysis, and the practical gotchas (windowing, zero-padding, aliasing) that wreck real analyses. Again taught in both MATLAB and Python, again updated June 2026.
If you have ever nodded along to an FFT explanation without truly getting it, this is the course that fixes that — and it makes every subsequent filtering and spectral topic dramatically easier.
3. Digital Signal Processing 1 (Coursera / EPFL) — best university theory track
4.5★ · 648 reviews · 62,790 enrolled · free to audit
EPFL’s DSP sequence is the academic gold standard on Coursera, and this first course covers discrete-time signals, the DFT, and the algorithmic foundations with Swiss-engineering-school rigor. It is more mathematical than the Udemy picks — expect real derivations — and it rewards learners who want to understand DSP deeply enough for graduate work or serious engineering roles. Free to audit; the paid certificate carries EPFL’s name, one of Europe’s top engineering schools.
View on Coursera (free to audit) →
Signal processing courses compared
| Course | Platform | Rating | Updated | Best for |
|---|---|---|---|---|
| Signal processing problems (Cohen) | Udemy | 4.6★ (2,557) | Jun 2026 | Practical DSP, both languages |
| Master the Fourier transform (Cohen) | Udemy | 4.5★ (2,615) | Jun 2026 | The core concept, deeply |
| Digital Signal Processing 1 (EPFL) | Coursera | 4.5★ (648) | Evergreen | University-grade theory |
| Think DSP (Downey) | greenteapress.com (free) | n/a | Free book | Python-first self-study |
MATLAB or Python for DSP?
Ten years ago this was a real dilemma; today it is mostly settled by context. Python with scipy.signal and numpy handles the standard toolkit free of charge and integrates with the ML ecosystem, which is why research and data science lean Python. MATLAB persists in aerospace, automotive, and academic EE departments, where Simulink and the toolboxes justify the license. Both Cohen courses teach every problem in both languages — take that as the professional’s answer: learn the concepts once, switch syntax freely. For free Python-first self-study, Allen Downey’s Think DSP is an excellent companion text.
Who actually needs signal processing?
More people than the course catalogs suggest. Electrical and biomedical engineers, obviously — but also audio programmers building effects and synthesizers, neuroscience researchers processing EEG/MEG data (Cohen’s home field), IoT and wearables developers cleaning accelerometer streams, and machine learning engineers whose “tabular” data is secretly sensor time series. If your data comes off a sensor at a sample rate, DSP is the difference between features and noise.
Frequently asked questions
What is the best signal processing course on Udemy?
Signal processing problems, solved in MATLAB and in Python by Mike X Cohen — 4.6 stars, 2,557 ratings, updated June 2026. It teaches DSP by solving real problems in both MATLAB and Python, and the instructor is a neuroscientist who uses these methods professionally. His companion Fourier transform course is the best dedicated treatment of the single most important concept in the field.
Do I need MATLAB, or can I learn signal processing in Python?
Python is fully sufficient in 2026 — scipy.signal plus numpy covers the standard DSP toolkit, and it is free. MATLAB remains the default in some engineering departments and industries (aerospace, automotive), and its Signal Processing Toolbox is genuinely polished. Both Mike X Cohen courses teach every problem in both languages, which neatly removes the dilemma.
What math do I need before a signal processing course?
Comfort with algebra and complex numbers is the real floor; linear algebra and basic calculus help substantially. The Fourier transform — the heart of DSP — is built on sines, cosines, and complex exponentials. Cohen’s Fourier course builds the intuition visually before the math, which is why we recommend it even to learners who found textbook treatments impenetrable.
Is there a signal processing certification?
No vendor or industry certification exists for DSP the way it does for cloud or networking. The credential path runs through degrees (EE/biomedical) or demonstrated work. The EPFL Coursera course offers a paid certificate with a respected university’s name; treat it as a nice-to-have, not a career key.
Related guides
- Best Statistics Courses
- Best Time Series Analysis Courses
- Best Image Processing Courses
- Best Data Science Courses
Start with the top DSP course →
Written by Josh Hutcheson — E-Learning specialist and founder of OnlineCourseing. Last updated: July 9, 2026.