Building on analyst fundamentals into predictive modeling.
Data science builds directly on analyst skills — SQL, EDA, and dashboards remain essential.
Hypothesis testing, distributions, and the reasoning behind model evaluation — the foundation everything else depends on.
Regression and classification — training and evaluating models on real, labeled data.
Learn metrics beyond accuracy, and how to avoid fooling yourself about a model's real performance.
Take a model from a notebook to something usable — deployment knowledge separates practitioners from theorists.
Complete a project from raw data through a deployed, evaluated model — the strongest possible portfolio piece.
Every stage above is built into the Machine Learning Track program's curriculum, labs, and projects.
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