A final checklist verifies that the project is not merely “working on my machine”: inputs are preserved, schema checks exist, results are reproducible, tests pass, outputs are labelled and limitations are documented.
A final checklist verifies that the project is not merely “working on my machine”: inputs are preserved, schema checks exist, results are reproducible, tests pass, outputs are labelled and limitations are documented.
Learning goal: explain why Final Checklist and Next Learning Paths behaves this way, apply it to a small example, and verify the result independently. Begin by being able to justify this first step: Rerun from a clean state, compare expected artifacts, run tests, inspect logs/errors and record remaining limitations or sensible next extensions.
Treat this as a sequence of observable decisions rather than one opaque command. Stage 1: Rerun from a clean state, compare expected artifacts, run tests, inspect logs/errors and record remaining limitations or sensible next extensions. Stage 2: Keep the stage inside the same data/validation definitions used by the rest of the project. Stage 3: Save the evidence produced by this stage so the next stage can be audited.
Rerun from a clean state, compare expected artifacts, run tests, inspect logs/errors and record remaining limitations or sensible next extensions.
Keep the stage inside the same data/validation definitions used by the rest of the project.
Save the evidence produced by this stage so the next stage can be audited.
Evidence: a signed-off checklist points to the exact command and output artifacts.
The worked evidence makes the output of this project stage concrete and auditable.
For Final Checklist and Next Learning Paths, trace the specific input through the mechanism above and independently verify one returned value, state change or side effect.
InputObjects/values supplied to the operation.StateNames or mutable objects that may change during execution.OutputReturned value, side effect, file, plot or exception to inspect.Use Final Checklist and Next Learning Paths when it answers a defined question in Capstone: A Small Data Project and its inputs/assumptions match the current data or program state.
Reconsider Final Checklist and Next Learning Paths when the required information is unavailable, the operation would violate a validation/data boundary, or a simpler operation answers the question more transparently.
Construct a tiny example of Final Checklist and Next Learning Paths. First rerun from a clean state, compare expected artifacts, run tests, inspect logs/errors and record remaining limitations or sensible next extensions. Then keep the stage inside the same data/validation definitions used by the rest of the project. Predict the result before execution and explain one boundary or failure case.
Which approach best demonstrates understanding of Final Checklist and Next Learning Paths?
Step 1Rerun from a clean state, compare expected artifacts, run tests, inspect logs/errors and record remaining limitations or sensible next extensions.Step 2Keep the stage inside the same data/validation definitions used by the rest of the project.Step 3Save the evidence produced by this stage so the next stage can be audited.