{"slug":"eda","sectionId":"data-preparation","title":"Exploratory data analysis","summary":"Summary statistics, distributions, outliers, missing values, duplicates, leakage, imbalance, and train/test mismatch.","focus":["data profiling","leakage hints","target imbalance","distribution mismatch"],"featureIdea":"Dataset profiler for missing values, outliers, leakage hints, imbalance, and distribution mismatch.","status":"shell","requiresBackend":true,"tags":["eda","profiling","leakage"],"locale":"en","sectionTitle":"Data Preparation","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/eda","apiPath":"/api/learning/eda","selfCheck":["What is the core job of \"Exploratory data analysis\"?","Which common mistake would break a production implementation of this topic?","Which inputs or limits must be validated before the interactive feature ships?","What is the smallest test that proves the future implementation behaves correctly?","When does this module really need backend compute, and when is a UI simulation enough?"],"implementationNotes":["Start with one focused feature, not a full course inside one page.","All public inputs must be typed, bounded, and covered by reject-case tests.","If a model, dataset, or job is added, document source, license, limits, and fallback.","The interaction must explain the topic rather than serve as decoration."]}