Generate Course Schema for Professional Training Programs

✓ Tested Works with CSV, Excel, Google Sheets → JSON-LD Schema

By dataclean.to team · 2026-02-05

B2B training programs convert to Course schema for educational search visibility. Professional certifications, online courses, and training workshops contain structured information that maps to Course properties. This conversion enables course listings in search results.

The Problem

B2B training providers offer courses without structured schema markup. Learning management systems store course data separately from marketing pages. Professional certification programs need Course schema for discovery. Bulk conversion applies schema to entire course catalogs. Schema.org Course specification

How to Fix It

Upload Course Catalog

Import course information including titles, descriptions, instructors, duration, pricing, and learning outcomes.

Map Course Properties

Assign course details to Course schema fields including name, description, provider, instructor, and courseCode.

Configure Educational Details

Set educationalCredentialAwarded, timeRequired, and coursePrerequisites for program requirements.

Add Offering Information

Include CourseInstance for scheduled sessions with start dates, locations, and enrollment options.

Generate Course Schema

Export Course JSON-LD for course pages, training catalogs, and educational platform listings.

Frequently Asked Questions

What B2B training uses Course schema?

Professional certifications, online courses, workshops, seminars, training programs, continuing education, and any structured learning offering for business professionals.

How do I handle self-paced vs scheduled courses?

Use CourseInstance for scheduled sessions with specific start dates. Self-paced courses use the base Course schema without instance-specific scheduling details.

Can I include certification information?

Course schema supports educationalCredentialAwarded to specify certificates, badges, or professional credentials earned upon completion.

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.

{
  "@context": "https://schema.org",
  "@type": "Dataset",
  "name": "Cleaned Customer Data",
  "description": "Normalized customer records with standardized fields",
  "keywords": ["customer data", "CRM", "contact list"]
}

💡 How it works: Consistent data formatting reduces import errors and makes your dataset compatible with downstream tools.

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