# Udacity Courses & Nanodegrees Scraper (`crawlerbros/udacity-scraper`) Actor Scrape Udacity school pages and program pages. Extract Nanodegree and course metadata including difficulty, duration, ratings, skills, instructors, partner, and pricing signals. - **URL**: https://apify.com/crawlerbros/udacity-scraper.md - **Developed by:** [Crawler Bros](https://apify.com/crawlerbros) (community) - **Categories:** Developer tools, Automation, Other - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Udacity Scraper Scrape Udacity school pages and direct program pages. This actor extracts public program metadata for Nanodegrees, courses, and other catalog items directly from Udacityโ€™s rendered page data. ### Modes - `bySchool` - `byProgramUrls` ### Example Input Browse a school page: ```json { "mode": "bySchool", "schoolSlug": "programming", "fetchDetail": true, "maxItems": 10 } ```` Scrape direct program URLs: ```json { "mode": "byProgramUrls", "programUrls": [ "https://www.udacity.com/course/ai-programming-python-nanodegree--nd089" ], "maxItems": 1 } ``` ### Output Each record can include: - `programKey`, `title`, `programUrl`, `slug` - `semanticType`, `difficultyLevel`, `duration`, `isFree` - `rating`, `reviewCount` - `skills` - `partnerName` - `instructors` - `summary`, `aboutText` - `prerequisites`, `additionalRequirements` - `siteName`, `recordType`, `scrapedAt` Empty values are omitted from output. ### Notes - The actor relies on Udacityโ€™s public Next.js page props rather than private APIs. - `bySchool` is the best default lane for high-yield real-data testing. - `fetchDetail=true` enriches school-listing items with program-page metadata. # Actor input Schema ## `mode` (type: `string`): How to fetch Udacity programs. ## `schoolSlug` (type: `string`): Udacity school slug such as `programming`, `artificial-intelligence`, or `data-science`. ## `programUrls` (type: `array`): Direct Udacity program URLs such as https://www.udacity.com/course/ai-programming-python-nanodegree--nd089 ## `difficultyLevel` (type: `string`): Optional exact difficulty filter. ## `semanticType` (type: `string`): Optional exact semantic type filter. ## `freeOnly` (type: `boolean`): Only emit programs that Udacity marks as free. ## `fetchDetail` (type: `boolean`): Follow each program URL for richer detail fields like instructors, partner, and reviews. ## `maxItems` (type: `integer`): Hard cap on emitted Udacity programs. ## Actor input object example ```json { "mode": "bySchool", "schoolSlug": "programming", "programUrls": [], "difficultyLevel": "", "semanticType": "", "freeOnly": false, "fetchDetail": true, "maxItems": 20 } ``` # Actor output Schema ## `programs` (type: `string`): Dataset containing all scraped Udacity program records. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "mode": "bySchool", "schoolSlug": "programming", "programUrls": [], "difficultyLevel": "", "semanticType": "", "freeOnly": false, "fetchDetail": true, "maxItems": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("crawlerbros/udacity-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "mode": "bySchool", "schoolSlug": "programming", "programUrls": [], "difficultyLevel": "", "semanticType": "", "freeOnly": False, "fetchDetail": True, "maxItems": 20, } # Run the Actor and wait for it to finish run = client.actor("crawlerbros/udacity-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "mode": "bySchool", "schoolSlug": "programming", "programUrls": [], "difficultyLevel": "", "semanticType": "", "freeOnly": false, "fetchDetail": true, "maxItems": 20 }' | apify call crawlerbros/udacity-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=crawlerbros/udacity-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Udacity Courses & Nanodegrees Scraper", "description": "Scrape Udacity school pages and program pages. Extract Nanodegree and course metadata including difficulty, duration, ratings, skills, instructors, partner, and pricing signals.", "version": "1.0", "x-build-id": "IoC3bfcIWhjlq3ZHL" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/crawlerbros~udacity-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-crawlerbros-udacity-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/crawlerbros~udacity-scraper/runs": { "post": { "operationId": "runs-sync-crawlerbros-udacity-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/crawlerbros~udacity-scraper/run-sync": { "post": { "operationId": "run-sync-crawlerbros-udacity-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "bySchool", "byProgramUrls" ], "type": "string", "description": "How to fetch Udacity programs.", "default": "bySchool" }, "schoolSlug": { "title": "School slug", "type": "string", "description": "Udacity school slug such as `programming`, `artificial-intelligence`, or `data-science`.", "default": "programming" }, "programUrls": { "title": "Program URLs", "type": "array", "description": "Direct Udacity program URLs such as https://www.udacity.com/course/ai-programming-python-nanodegree--nd089", "default": [], "items": { "type": "string" } }, "difficultyLevel": { "title": "Difficulty level", "enum": [ "", "Beginner", "Intermediate", "Advanced" ], "type": "string", "description": "Optional exact difficulty filter.", "default": "" }, "semanticType": { "title": "Program type", "enum": [ "", "Course", "Degree" ], "type": "string", "description": "Optional exact semantic type filter.", "default": "" }, "freeOnly": { "title": "Free only", "type": "boolean", "description": "Only emit programs that Udacity marks as free.", "default": false }, "fetchDetail": { "title": "Fetch program detail", "type": "boolean", "description": "Follow each program URL for richer detail fields like instructors, partner, and reviews.", "default": true }, "maxItems": { "title": "Max items", "minimum": 1, "maximum": 200, "type": "integer", "description": "Hard cap on emitted Udacity programs.", "default": 20 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```