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To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

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AI Data: Analyze, Govern, Plan

This course is part of Managing AI Projects That Ship and Scale Specialization

Instructor: ansrsource instructors

Included with

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1 module

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

1 module

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

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Build your subject-matter expertise

This course is part of the Managing AI Projects That Ship and Scale Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There is 1 module in this course

AI projects succeed or fail on the strength of their data. In this course, you’ll learn to diagnose pipeline bottlenecks, enforce governance standards, and plan data workflows that keep projects on track. Through real-world scenarios, you’ll trace issues like stale feature vectors, audit data handling for compliance with regulations such as General Data Protection Regulation (GDPR), and design actionable plans for data acquisition, storage, and annotation. You’ll also practice translating technical findings into project-ready deliverables, like drafting a Jira epic for large-scale data labeling.