Roadmap: What You Will Be Able to Do

What this quest delivers

By the end of the next seven tracks you will be able to take a tabular problem from first conversation to deployed artifact: write the contract, audit columns, build a leakage-safe pipeline, train a sane baseline, compare linear models and tree ensembles, calibrate probabilities, select an honest threshold, monitor drift, and document the handoff. The goal is to own the decision path, not merely fit an estimator.

Habits last longer than tools

The enduring sequence is column audit before feature engineering, baseline before sophisticated models, calibration before thresholding, and rollback before launch. Library interfaces change; these habits remain useful. Record why a result is good and where to look when it becomes bad.

Carry one dataset through the quest

Bring a real or realistic dataset and apply each lesson to it. Before modeling, write the target, prediction time, legal features, metric, and simplest baseline in a README. Using one continuing project makes the consequences of each choice accumulate instead of turning the lessons into disconnected trivia.

The learning record is an artifact

For each experiment, keep the hypothesis, choice, measurement, and reason for failure. Record the data version and code revision alongside the result. A final model file cannot explain why a feature was rejected or why a metric was chosen; a concise experiment trail prevents the same dead end from being rediscovered.

Know the boundary before starting

This quest assumes an analysis-ready DataFrame. Ingestion and large-scale processing belong to the data-engineering boundary; here the focus is auditing meaning, framing prediction, evaluating models, and translating scores into operations. If reliable data does not exist, design the events that will create trustworthy labels first, including consent and retention when collection changes product behavior.