DS 202 Apply
Turn what you learned into your own AI-Assisted Data Science Workflow + Verification Record
What this final artifact is for
The AI-Assisted Data Science Workflow + Verification Record is the final artifact of DS 202. Choose a real analysis problem you face, let AI help propose an approach and write code, and clearly write the problem, audience, data, method, verification, evidence, limitations, risk and governance, the human reviewer, and the next step. This becomes the starting point for later practice discussion and governance decisions.
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What to fill in
- Problem, audience, and data (source, grain, time coverage, key definitions)
- Method and workflow (including the prompt actually used)
- At least three verification checks
- Evidence and output: what the analysis or workflow actually produced
- Limitations
- Risk and governance considerations (privacy, sensitive-data handling, provenance)
- The human reviewer
- Next step
A well-structured AI-Assisted Data Science Workflow + Verification Record
A well-structured artifact includes a clear, checkable problem, a clearly stated data source and grain, a reproducible method, at least three verification checks, evidence and claims consistent with the observed code output, and at least one limitation.
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Exit Ticket
Finally, use the Exit Ticket to record today's most important idea, the step you are still unsure about, where you would apply this method in a real context, the risk you most need to verify, and your difficulty and usefulness ratings.
Before you submit
Warning
What you need to complete
- Complete both DS 202 modules
- Score 80% or higher on the post-test
- To earn the AI-Assisted Data Science Capability Certificate, also submit an AI-Assisted Data Science Workflow + Verification Record and have it reviewed and passed against the rubric by a Credential Admin
How you are assessed
A Credential Admin scores the AI-Assisted Data Science Workflow + Verification Record across six weighted domains: Problem & Audience Framing, Data Source, Grain & Context, Method & Prompt Workflow, Verification Evidence (Ladder), Provenance & Governance, and Human Review & Limitations. A PASS requires at least 15 of 18 points with no critical-fail rule triggered; 12 points or fewer, or any triggered critical-fail rule, is a FAIL.
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Evidence of practice
- A recorded rubric result (PASS, REVISION_REQUIRED, or FAIL) with no critical-fail rule triggered
- A passing review leaves capability evidence for each of the nine fine-grained DS 202 competencies individually (AI Role Boundaries, Data Context & Schema, Verifiable Prompt Design, Read-Before-Run Code Review, Silent Analytical Error Debugging, Verification Ladder, Provenance & Reproducibility, Sensitive Data & AI Governance, Workflow Ownership & Human Approval)
What you can earn
DS 202 Course Completion Certificate: earned by completing both modules and meeting the post-test threshold -- no Workflow + Verification Record submission required. AI-Assisted Data Science Capability Certificate: requires a separately submitted and reviewed Workflow + Verification Record. The two are independent of each other.
What this does not mean
This credential confirms the holder completed DS 202's required course content and post-test, or (for the capability certificate) demonstrated problem framing, data understanding, method design, verification, provenance, and human review on the AI-Assisted Data Science Workflow + Verification Record.
- It is not a formal professional certification or hiring qualification in data science
- It does not guarantee that any specific analysis conclusion the holder produces later is correct
- It does not replace human review before high-risk decisions (Human, AI, Human)