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DS 103 Apply

Turn what you learned into your own Evidence-Based Data Story

What the final artifact is for

The Evidence-Based Data Story is DS 103's final artifact. You choose a real question you actually face, build 3-5 charts that answer it, and write the problem, audience, data, method, verification, evidence, limitations, governance considerations, human reviewer and next step. It becomes the starting point for later practice discussions.

Info

Human, AI, Human: a person defines the question and the audience, AI may help draft the charts or narrative, and a person verifies the evidence, the claims and the conclusion, and remains responsible for the result.

What to fill in

  • The problem, audience and data (source, grain, time coverage, key definitions)
  • The method and workflow
  • At least three verification checks
  • Evidence and output: 3-5 charts and their evidence statements
  • Limitations
  • Risk and governance considerations, as relevant
  • The human reviewer
  • The next step

A structurally complete Evidence-Based Data Story

A structurally complete artifact includes: a clear, checkable question; a stated data source and grain; a reproducible method; at least three verification checks; charts and evidence statements that match the claims; at least one limitation.

Info

The platform never automatically judges whether your chart choices or claims were correct. This artifact feeds learning feedback and later discussion, and is reviewed by people.

Exit Ticket

Finally, use the Exit Ticket to record the most important idea from today, the step you are still unsure about, where you would apply this 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 institutional or company data to an AI tool; confirm your institution's rules before using real internal operations data.

Open the forms

What you need to complete

  • Complete both DS 103 modules
  • Score at least 80% on the post-test
  • To earn the Visualization & Storytelling Capability Certificate, also submit an Evidence-Based Data Story and have it reviewed and passed by a Credential Admin against the rubric

How you are assessed

A Credential Admin scores the Evidence-Based Data Story across six weighted domains: Problem & Audience Framing, Chart Type & Question Fit, Scale & Baseline Integrity, Accessible Encoding, Evidence Statement Discipline, and Verification, Governance & Human Review. A score of at least 15 out of 18, with no critical-fail rule triggered, is a PASS; 12 or below, or any triggered critical-fail rule, is a FAIL.

Info

This scoring logic is published so you know what is being assessed -- it is not the same as an answer key for a test.

What counts as evidence

  • A recorded rubric result (PASS, REVISION_REQUIRED, or FAIL) with no critical-fail rule triggered
  • A PASS leaves capability evidence for each of the six fine-grained DS 103 competencies individually: chart selection, distribution and relationship reading, scale and baseline integrity, accessible encoding and labeling, bounded claims and aggregation, and source and AI-chart review

What you can earn

DS 103 Course Completion Certificate: earned by completing both modules and meeting the post-test threshold, with no Evidence-Based Data Story required. Visualization & Storytelling Capability Certificate: requires a separately submitted and reviewed Evidence-Based Data Story. The two are independent and do not affect each other.

What this credential does not mean

This credential confirms the holder completed DS 103's required coursework and post-test, or (for the capability certificate) demonstrated problem framing, chart selection, scale integrity, bounded claims, and human review in the Evidence-Based Data Story -- it is not a specific job qualification or professional certification.

What next?