Learning-Data-Science-Advanced-English-r1

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The Beginner book introduced data and its first summaries. Advanced study means defining the task before computing anything: name the target quantity, make the schema testable, predict what a join will do, and keep the limits of every estimate in view. This 80-page guide runs through 18 original units on paper: define the target quantity before computing; make grain and schema testable; parse values without erasing uncertainty; predict join cardinality before merging; bound the effect of missing outcomes; choose robust summaries without deleting evidence; use graphics to question assumptions; make survey weights answer a stated target; interpret a confidence interval as a procedure; enumerate a tiny randomisation distribution; report the search behind a selected result; read a pooled comparison beside its strata; aggregate time with its exposure; separate groups when the question is new groups; fit data transformations on training only; keep metric denominators visible; make a rerun inspectable without calling it valid; and a capstone handing over a bounded integrated analysis. Every unit has concepts, a worked case or lab, and an assessment with an extension challenge. An optional code appendix supports the paper work. Every lab stays on paper or uses harmless offline arithmetic with the supplied fictional records - no real records, account access, external communication or automated action is needed or authorised. The book is explicit that a result is only as strong as the question, the denominators and the search behind it. Best suited to readers who have finished the Beginner edition or have equivalent comfort with tables, averages and simple graphs. The mathematics stays elementary and every method is shown on a fictional case before you try it; the code appendix is optional, not required. Important: this is educational data science, not a professional qualification, an audit, operational approval or legal, medical or financial advice. Use the supplied fict