The google-pathways-converter is a Python package that converts a dictionary of keyword arguments into Google-pathways-friendly JSON-LD.
Google Pathways illuminates "pathways" for job seekers trying to acquire skills for a new role, job, or career. Google Pathways is an online utility integrated with the Google search engine: in other words, Google reads structured data from publicly available web pages, and if compatible with Pathways, the data appears in Google search results. Try it for yourself! Go to Google, and enter “job training and Richmond, VA”. You should see a panel with expandable results for jobs, paid training, and educational programs near Richmond.
Google only ingests programs data that aligns with the schema definitions standardized by schema.org. Schema.org specifies schemas for two program types: WorkBasedProgram and EducationalOccupationalProgram. A work-based program both provides job training and pays a wage. An educational-occupational program does not pay a wage, but offers a learning experience, which may or may not require the student to pay tuition and/or fees.
A BrightHive Data Trust may include data resources that describe work-based and/or educational-occupational programs. In many cases, Data Trust members want to easily, securely, and effectively disseminate information about their program offerings – something that Google Pathways facilitates. To this end, the google-pathways-converter library transforms data resources into Schema.org definitions, which can be consumed and returned by Google Pathways.
google-pathways-converter can be installed into your virtual environment in a number of ways. Pipenv users can run the following command:
pipenv install -e git+https://github.com/brighthive/google-pathways-converter.git@master#egg=converterThen, install the library wherever you need to it.
from converter import educational_occupational_programs_converter, work_based_programs_converterYou need to consider the following fields for the educational-occupational converter: educational_occupational_programs_converter.
| Field | Type |
|---|---|
| REQUIRED FIELDS | |
| program_name | String |
| program_url | String |
| provider_address | A list of dicts. Each dict should have the following keys: street_address, address_locality, address_region, postal_code, and address_country. |
| application_deadline | String that represents a date value in ISO-8601 format |
| offers_price | Integer |
| provider_name | String |
| provider_url | String |
| provider_telephone | String |
| time_to_complete | String that represents a duration in ISO-8601 format |
| identifier_cip AND/OR identifier_program_id | String |
| occupational_category | A list of strings. Each string must reference a BLS SOC-6 code, e.g., ["47-2111", "49-9021"]. |
| RECOMMENDED FIELDS | |
| application_start_date | String |
| start_date | String that represents a date in ISO-8601 format |
| end_date | String that represents a date in ISO-8601 format |
| occupational_credential_awarded | String |
| educational_program_mode | String with one of three values: "IN_PERSON", "ONLINE", or "HYBRID". Only programs that take place 100% online qualify as being in "ONLINE" mode. |
| maximum_enrollment | String |
| offers_price | Integer |
| time_of_day | String |
| program_prerequisites | A dict with up to two specified keywords: "competency_required" (knowledge, skill, ability or personal attribute that a program participant must be able to demonstrate) and/or "credential_category" (a type of credential that a program participant must have) |
You need to consider the following fields for the work-based converter: work_based_programs_converter.
| Field | Type |
|---|---|
| REQUIRED FIELDS | |
| program_name | String |
| program_url | String |
| provider_address | A list of dicts. Each dict should have the following keys: street_address, address_locality, address_region, postal_code, and address_country. |
| program_description | String |
| occupational_category | A list of strings. Each string must reference a BLS SOC-6 code, e.g., ["47-2111", "49-9021"]. |
| RECOMMENDED FIELDS | |
| program_description | String |
| provider_name | String |
| provider_url | String |
| provider_telephone | String |
| identifier_program_id | String |
| start_date | String that represents a date in ISO-8601 format |
| end_date | String that represents a date in ISO-8601 format |
| occupational_credential_awarded | String |
| maximum_enrollment | String |
| offers_price | Integer |
| time_of_day | String |
| program_prerequisites | A dict with up to two specified keywords: "competency_required" (knowledge, skill, ability or personal attribute that a program participant must be able to demonstrate) and/or "credential_category" (a type of credential that a program participant must have) |
Below gives an example of the educational_occupational_programs_converter. Note: you can pass in a dict (recommended), or you may pass in the kwargs directly. If you use a dict, then remember to unpack it.
from converter import educational_occupational_programs_converter
programs_input = {
'application_deadline': '2020-04-01',
'program_name': 'Goodwill Program',
'offers_price': 2000,
'program_url': 'goodwill.org',
'provider_name': 'Goodwill of Springfield',
'provider_url': 'goodwill.org',
'provider_telephone': '333-343-4444',
'provider_address': [
{
'street_address': '1 Grickle Grass Lane',
'address_locality': 'Springfield',
'address_region': 'MA',
'address_country': 'US',
'postal_code': '88881'
}
],
'time_to_complete': 'P6M',
'identifier_cip': '51.3333',
'identifier_program_id': '5688',
'application_start_date': '2020-01-01',
'program_prerequisites': {
'credential_category': 'HighSchool',
'competency_required': 'Valid driver’s license'
},
'end_date': '2020-12-01',
'occupational_credential_awarded': 'Food Handlers Certification',
'maximum_enrollment': '50',
'start_date': '2020-04-01',
'time_of_day': 'Evening',
'educational_program_mode': 'HYBRID'
}
output = educational_occupational_programs_converter(**programs_input)
json.dumps(output, sort_keys=True)# Results!
{
"@context": "http://schema.org/",
"@type": "EducationalOccupationalProgram",
"applicationDeadline": "2020-04-01",
"applicationStartDate": "2020-01-01",
"endDate": "2020-12-01",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "CIP2010",
"value": "51.3333"
},
{
"@type": "PropertyValue",
"propertyID": "ProgramID",
"value": "5688"
}
],
"maximumEnrollment": "50",
"name": "Goodwill Program",
"occupationalCredentialAwarded": "Food Handlers Certification",
"offers": {
"@type": "Offer",
"category": "Total Cost",
"priceSpecification": {
"@type": "PriceSpecification",
"price": 2000,
"priceCurrency": "USD"
}
},
"programPrerequisites": {
"@type": "EducationalOccupationalCredential",
"credentialCategory": "HighSchool",
"competencyRequired": "Valid driver’s license"
},
"provider": {
"@type": "EducationalOrganization",
"address": [
{
"@type": "PostalAddress",
"addressLocality": "Springfield",
"addressRegion": "MA",
"addressCountry": "US",
"postalCode": "88881",
"streetAddress": "1 Grickle Grass Lane"
}
],
"contactPoint": {
"@type": "ContactPoint",
"telephone": "333-343-4444"
},
"name": "Goodwill of Springfield",
"url": "goodwill.org"
},
"startDate": "2020-04-01",
"timeOfDay": "Evening", "timeToComplete": "P6M", "url": "goodwill.org",
"educationalProgramMode": "HYBRID"
}google-pathways-converter comes with a comprehensive suite of unit tests. Execute all tests by running:
pipenv run pytest
We welcome code contributions, suggestions, and reports! Please report bugs, and make suggestions using Github issues. The BrightHive team will triage and prioritize your issue as soon as possible.
- Regina Compton (Software Engineer)
- Logan Ripplinger (Software Engineer)