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DS 301 Apply

Turn what you learned into your own Analytics Workflow Canvas + Executable Workflow Prototype + Runbook

What this final artifact is for

The Analytics Workflow Canvas + Executable Workflow Prototype + Runbook is DS 301’s final artifact. Pick an analytics workflow you actually face, refactor it into a reliable workflow with an input contract, validation, observability, and a human approval point, and write clearly about the problem, audience, data, method, verification, evidence, limitations, risk and governance, human reviewer, and next step. This becomes the starting point for later reliability discussions and governance decisions.

Info

Human, AI, Human: a person defines the problem, audience, and quality expectations; AI may help draft pipeline code; a person verifies the evidence, claims, and the decision to publish or use the result, and remains responsible for it.

What to fill in

  • Problem, audience, and data (source, grain, time coverage, key definitions)
  • Method and workflow (covering ingest→validate→transform→analyze→publish)
  • At least three verification checks
  • Evidence and output: what the workflow actually produced and its observability records
  • Limitations
  • Risk and governance considerations (human approval, rollback, observability)
  • Human reviewer
  • Next step

A well-structured Analytics Workflow Canvas + Executable Workflow Prototype + Runbook

A well-structured artifact includes: a clear, testable problem; a clearly defined data source and grain; a reproducible method; at least three verification checks; evidence and claims that match the observed execution output; and at least one limitation.

Info

The platform does not automatically judge whether your workflow design is entirely correct. This artifact is used for learning feedback and later discussion, and is reviewed by a human.

Exit Ticket

Finally, use the Exit Ticket to record today’s most important idea, the step you are still unsure about, where you will apply this method in a real context, the risk you most need to verify, and your difficulty and usefulness ratings.

Before you submit

Warning

Do not enter company secrets, client data, personal data, or other unauthorized sensitive information. Unless authorized, never upload confidential organizational data to an AI tool; check your organization’s policy before using real operational data.

Go to the forms

What you need to complete

  • Complete both DS 301 modules
  • Score at least 80% on the course post-test
  • To earn the Data Products & Analytics Workflow Engineering Capability Certificate, also submit an Analytics Workflow Canvas + Executable Workflow Prototype + Runbook and have a Credential Admin review it against the rubric and pass it

How you are assessed

A Credential Admin scores the Analytics Workflow Canvas + Executable Workflow Prototype + Runbook against eight weighted domains: Problem & Audience Framing, Data Source/Grain/Lineage, Method (the five-stage workflow), Verification & Testing, Evidence & Output Fidelity, Risk/Governance & Rollback, Human Review, and Runbook & Next Steps. Only a score at or above the threshold, with no critical-fail rule triggered, counts as PASS; a score below the threshold or any triggered critical-fail rule is a FAIL.

Info

This scoring logic is public so you know what is being assessed; it is not the same as a quiz answer key. The full rubric criteria and critical-fail rule descriptions are visible only to a Credential Admin/reviewer.

Applied-work evidence

  • One recorded rubric result (PASS, REVISION_REQUIRED, or FAIL) with no critical-fail rule triggered
  • A PASS records capability evidence separately for all 9 DS 301 fine-grained competencies (Data Product Framing, Function & Stage Decomposition, Configuration & Secret Separation, Data Contract & Validation, Reliable Scheduling & Idempotency, Observability & Lineage, Approval & Rollback Governance, Testing & Failure Recovery, Analytics Workflow Canvas Design)

What you can earn

DS 301 Course Completion Certificate: earned once you complete both modules and meet the post-test threshold; no Analytics Workflow Canvas submission required. Data Products & Analytics Workflow Engineering Capability Certificate: requires a separately submitted and reviewed Canvas. The two are independent and do not affect each other.

What this does not mean

This credential confirms the holder completed DS 301’s required coursework and post-test, or (for the capability certificate) demonstrated problem framing, data understanding, workflow design, verification, observability, and governance skills on the Analytics Workflow Canvas + Executable Workflow Prototype + Runbook.

  • It does not mean the holder has a formal data-engineering or analytics-workflow professional certification or hiring qualification
  • It does not mean any specific analytics workflow the holder designs later is necessarily correct or reliable
  • It does not mean human review before publishing or a major decision can be skipped (Human, AI, Human)

What next?