KNIME Analytics Platform is a visual workflow tool for data science, and unlike its main commercial competitor it is genuinely free and open source. You download it, you use it, you deploy workflows, and nobody asks for a licence fee. That single fact changes the calculation compared with Alteryx, because you can learn it, use it on personal projects, and take those skills anywhere without a purchasing decision in the way. The education programme is well organised.
Courses are levelled from L1 basics through to L4 advanced topics, with separate tracks for data science, data engineering and integrations. Each course is self paced with exercises, and the levelling actually means something rather than being decorative. The L1 material assumes nothing, L2 introduces flow variables and loops, and by L3 and L4 you are into components, deployment and more sophisticated modelling. That progression is clearer than most vendor training manages.
The workflow paradigm has a genuine advantage that gets undersold. Every step of an analysis is a visible node with visible inputs and outputs. Someone can open your workflow and see exactly what you did without reading code. In regulated environments, where an auditor may need to trace how a number was produced, that visibility is a real feature and it is a substantial part of why KNIME has strong adoption in pharmaceutical, chemical and manufacturing settings, particularly across Europe.
If you work in one of those industries, this is not a fringe skill. The Python and R integration is the sensible part of the design. You can drop a Python node into a workflow when the visual nodes cannot express what you need, which means you are not trapped by the paradigm. Used well, that gives you the auditability of the workflow with the flexibility of code where it matters.
I would encourage anyone learning KNIME to reach for those nodes early rather than treating them as a last resort. Now the concern that applies to every tool of this kind. Drag and drop interfaces are approachable, and approachability is how people avoid learning to program for years while feeling productive. Someone can become genuinely skilled at KNIME and still be unable to write a loop, and the day their employer changes tools, or they apply for a role elsewhere, that gap becomes expensive.
The job market for Python and SQL is larger than the market for KNIME by an enormous margin. That is not a comment on the software's quality, it is a statement about where demand is. If you are choosing what to learn with no employer constraint, learn Python and SQL first and treat KNIME as an addition. The machine learning nodes carry the usual risk.
You can drop a random forest node into a workflow, connect it, and get predictions without understanding what a random forest does, how to validate it, or why your accuracy figure might be meaningless because of leakage in your preprocessing. The training does cover validation and it does not force you to confront the concepts the way writing scikit-learn code does. Learn the modelling theory elsewhere, from Andrew Ng or an equivalent, and use KNIME to apply it rather than to substitute for it. The interface shows its age.
It is a Java desktop application and it feels like one. Functional, unlovely, and occasionally slow with larger workflows. This is cosmetic and it does affect how enthusiastic you feel about spending eight hours in it. The certifications are paid and are meaningful mainly inside organisations that use the platform.
If your employer values it, take it. Otherwise the free courses give you the capability and the certificate adds little. My three point five is for free, open source software with well structured free training that has genuine standing in specific industries, marked down for a narrow job market relative to code based skills and for the way visual tools let people defer learning to program. Good second tool, questionable first one.