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AI Literacy · AI-203

Data Literacy for AI

Understand data quality, missing information, privacy, and bias so you can interpret AI-supported results responsibly.

LEVELIntermediate
LESSON TIMEAbout 5 hours
FORMAT6 self-paced lessons
PATHWAYCDAP required

Practice and the final project time is additional to the lesson estimate.

Check if it fits youSee the full syllabus

IS THIS COURSE FOR YOU?

What to expect from Data Literacy for AI

Learners who need to interpret data used by or produced with AI.

What you will learn

  • Assess data quality and completeness
  • Recognize privacy and representation concerns
  • Communicate data limitations responsibly

LESSON OUTLINE SAMPLE

Recognize structured and unstructured data

Recognize structured and unstructured data is more than a term to remember: it is a practical skill that requires you to understand the situation, select an appropriate method, complete the work carefully, and verify the result. Use this study guide to connect the underlying ideas to the decisions you will make in the lesson activity.

This sample shows the lesson goals and selected takeaways. Enroll to access the complete lesson notes, activities, and assignments.

What this lesson covers

  • Explain the essential ideas in Recognize structured and unstructured data.
  • Apply the skill to a realistic AI-supported task.
  • Document the evidence, safety checks, and human judgment used.

Selected takeaways

  • AI results depend on the data used to create or guide them. Data literacy means understanding what each field represents, checking quality and completeness, protecting privacy, identifying groups or situations that may be missing, and interpreting results within the limits of the available evidence.
  • Distinguish structured and unstructured data, fields, records, labels, and examples.
  • Human review and documented verification are required before important AI output is used.

COMPLETE SYLLABUS

3 modules · 6 lessons

6 follow-up assignments · 3 assessments
01
Understand data used by AIDistinguish structured and unstructured data, fields, records, labels, and examples.
2 lessons
  1. 1.1Recognize structured and unstructured data
  2. 1.2Understand records, fields, labels, and examples
02
Evaluate data quality and representationFind missing, inaccurate, outdated, duplicated, or unrepresentative information.
2 lessons
  1. 2.1Check accuracy, completeness, and consistency
  2. 2.2Identify missing groups and dataset bias
03
Use data responsiblyProtect privacy, interpret results cautiously, and communicate limitations clearly.
2 lessons
  1. 3.1Protect privacy and permissions
  2. 3.2Interpret AI-supported results and limitations

FINAL COURSE PROJECT

Data Readiness Assessment for AI

Submit a data inventory, quality profile, bias and privacy review, remediation plan and readiness recommendation, supporting evidence of your process, and a concise reflection. Do not include passwords, private records or confidential information.

Approximately 4 hours · Instructor reviewed

How success is evaluated

  • The project responds directly to the stated audience, purpose and scenario.
  • The submitted artifact demonstrates skills from multiple modules rather than one isolated lesson.
  • The learner provides evidence of testing, verification and revision.
  • The work addresses completeness, accuracy, representativeness, privacy and justified conclusions.
  • The explanation is original, clear, well organized and suitable for instructor review.

GRADING AND CERTIFICATE POLICY

Progress by demonstrating mastery

This course is completed through verified evidence of learning—not by averaging every activity into a traditional grade. You can use feedback, revise your work, and resubmit when an instructor requests changes.

  1. Complete the lessonsFinish the required lessons and applied practice in the published course release.
  2. Pass knowledge checksMeet each assessment’s published passing score. Attempt limits and retry guidance are shown before starting.
  3. Demonstrate practical skillsSubmit required assignments using safe, non-confidential evidence and receive instructor approval.
  4. Pass the final projectComplete the course-specific project and satisfy its published marking rubric. Revise and resubmit when requested.
  5. Unlock the certificateThe certificate becomes eligible only after every required lesson, assessment, assignment approval, and final-project requirement is verified.
How grading decisions are made
  • Courses use mastery-based completion rather than an averaged GPA or assignment-category percentage.
  • Practice activities help learners improve and do not reduce a course grade.
  • The rubric displayed with the active assignment in My Learning is authoritative.
  • Accuracy, safety, privacy, accessibility, or ethical failures may require revision even when other work is strong.
  • Instructor feedback and earlier submissions remain visible when work is revised and resubmitted.

Published assessment requirements: Understand data used by AI knowledge check: 75% passing score, up to 3 attempts · Evaluate data quality and representation knowledge check: 75% passing score, up to 3 attempts · Use data responsibly knowledge check: 75% passing score, up to 3 attempts

Ready to begin?

Study at your pace and keep access to your learning record and feedback.