Learning Data Science - Professional Level

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The Advanced book taught you to define the task and preserve its limits. Professional study means signing off an analysis like a contract: what evidence is required, what would make you refuse, and how to package the result without leaking or overstating it. This 79-page guide runs through 18 original units on paper: write a signoff contract with a refusal condition; trace versions without laundering their authority; check measurement drift before trend interpretation; describe who could enter the analysis; keep uncertain linkage as uncertainty; carry outcome bounds through a decision gate; respect the unit that could vary independently; transport a summary only with a target mix; distinguish a causal contrast from an observed contrast; keep assigned groups when outcomes disappear; expose the analysis path behind a selected result; define the family before controlling its error; compare error costs without granting deployment; audit subgroup denominators before a fairness claim; test a future-facing pipeline on a future-facing split; make a failing test preserve useful evidence; package evidence without leaking or overstating it; and a capstone signed with conditional and failed gates. 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. A signoff can honestly be refused or conditional, and the book treats that as a professional result. Best suited to readers who have finished the Advanced edition and want to take responsibility for analyses others will act on. No software or workplace access is assumed; every gate and contract is introduced on a fictional case first. Important: this is educational data science, not a professional qualification, an audit, operational approval