Turn data into decisions.
Four tracks that move you along the analytics ladder — from cleaning and visualising data, to modelling it, to building and training neural networks. Each one is tool-heavy and project-driven, because analytics roles are hired on portfolio evidence, not theory.
The Data Analyst full stack course covers the complete analysis workflow: collect, clean, explore, visualise, and then communicate the finding to someone who has to act on it. That last step is what most analyst candidates cannot do, and it is what gets you hired.
The Data Science full stack course runs the complete workflow: collect, clean, explore, model, evaluate, communicate. It is deliberately broader than the Data Analyst track — you do not stop at the dashboard, you build the predictive model behind it.
This track goes deep on the machine learning layer alone — algorithm by algorithm, with the mathematics explained and then implemented rather than imported and hoped for.
Deep Learning covers neural network architectures properly: what each layer does, why gradients vanish, how a convolution actually sees an image, and what a transformer attends to.
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Track record
Not sure which one?
The difference between these courses is smaller than the marketing suggests, and the right one depends entirely on your background and the roles you want. Tell us both and we'll tell you which of the 4 tracks fits — including if the answer is "none of them yet".
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Next step
Book a free demo class, meet the trainer, and see the curriculum in action before you pay anything. No fee is taken online.
