✓ TestedWorks with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-05
Professional training catalogs convert to Course schema for educational search discovery. Courses contain program details, instructor information, and learning outcomes that map to Course properties. This conversion enables course listings in search results with pricing and duration.
The Problem
Training providers maintain course catalogs without Course schema markup. Learning management systems store course data separately from marketing pages. Professional development programs need structured markup for search visibility. Bulk conversion applies Course schema to entire training catalogs. Schema.org Course specification
How to Fix It
1
Upload Course Catalog Data
Import training information including course titles, descriptions, instructors, duration, pricing, and learning objectives.
2
Map Course Properties
Assign catalog data to Course schema fields including name, description, provider, instructor, courseCode, and hasCourseInstance.
3
Configure Educational Details
Set timeRequired, educationalCredentialAwarded, and coursePrerequisites for program requirements.
4
Add Instance Information
Create CourseInstance objects for scheduled sessions with start dates, end dates, locations, and enrollment details.
5
Generate Course Schema
Export Course JSON-LD for course pages, training catalogs, and educational platform listings.
Frequently Asked Questions
What training programs use Course schema?
Professional certifications, online courses, workshops, boot camps, continuing education, corporate training, and any structured learning program for business professionals.
How do I handle self-paced courses?
Self-paced courses use the base Course schema without CourseInstance. Include timeRequired for estimated completion time instead of fixed start/end dates.
Can I include prerequisites?
Course schema supports coursePrerequisites to list required prior knowledge or courses. Reference other Course objects or describe prerequisite skills as text.
Example: Input → Output
name
email
phone
city
status
Alice Johnson
alice@example.com
+1-555-0101
New York
active
alice johnson
ALICE@EXAMPLE.COM
5550101
new york
Active
Red rows show common data quality issues. dataclean.to normalizes and generates JSON-LD automatically.