# Aladia-Discovery-Scraper (`getdataforme/aladia-discovery-scraper`) Actor Extract detailed course data from Aladia with this powerful scraper. Search by keywords to gather titles, descriptions, instructors, categories, skills, metrics, and more. Ideal for market research, competitive analysis, and content aggregation.... - **URL**: https://apify.com/getdataforme/aladia-discovery-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** Other, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## Aladia Discovery Scraper The Aladia Discovery Scraper is a powerful Apify Actor designed to extract detailed course information from the Aladia platform. It enables users to search for courses by keywords and retrieve comprehensive data on titles, descriptions, instructors, categories, and more. This tool is ideal for educators, researchers, and businesses seeking to analyze educational content trends and opportunities. ### Features - **Comprehensive Data Extraction**: Scrapes rich course details including metadata, categories, skills, requirements, and performance metrics. - **Flexible Search**: Supports keyword-based queries to find relevant courses efficiently. - **Customizable Limits**: Allows setting maximum items to scrape, with options for unlimited extraction. - **High Reliability**: Built on Apify's robust infrastructure for consistent and error-free scraping. - **Structured Output**: Delivers clean JSON data ready for integration into databases, analytics tools, or reports. - **Fast Performance**: Optimized for speed, handling large volumes of data without compromising quality. - **Ethical Scraping**: Respects website terms and avoids overloading servers, ensuring sustainable use. ### Input Parameters | Parameter | Type | Required | Description | Example | |-------------|---------|----------|-----------------------------------------------------------------------------|------------------| | searchQuery | string | Yes | Keyword to search for courses by title (e.g., 'ai', 'machine learning', 'python'). | "machine learning" | | maxItems | integer | No | Maximum number of courses to scrape. Set to 0 for unlimited. | 50 | ### Example Usage #### Input JSON ```json { "searchQuery": "ai", "maxItems": 100 } ```` #### Output JSON ```json [ { "id": "6847f668e3793dcbae597791", "title": "Exploring Fairness in Machine Learning", "description": "This course introduces students to the ethical dimensions of machine learning (ML), with a particular focus on applications in international development.\nAttribution: This course is adapted from Exploring Fairness in Machine Learning for International Development, originally developed by Dr. Richard Fletcher, Prof. Daniel Frey, Dr. Mike Teodorescu, Amit Gandhi, and Audace Nakeshimana, and published by MIT OpenCourseWare under a Creative Commons BY-NC-SA 4.0 License.", "type": "on-demand", "status": "published", "visibility": "public", "color": "#6F1D1B", "language": "en", "spaceId": "6847162b0af49caf63a61ccc", "productId": "684928a5e3793dcbae5c6f9c", "createdAt": "Tue Jun 10 2025 09:10:00 GMT+0000 (Coordinated Universal Time)", "updatedAt": "Wed Feb 25 2026 06:52:02 GMT+0000 (Coordinated Universal Time)", "publishedDate": "2025-06-11T06:57:19.310Z", "owner": { "id": "6847155b0af49caf63a61b0d", "name": "Rambod Rahmani", "email": "rambodrahmani@aladia.io", "followers": 400, "bio": "Senior Software, AI & Data Engineer πŸš€πŸŽ“" }, "categories": [ { "id": "67163a6d7e90861c1982cd54", "name": "Artificial Intelligence" } ], "subCategories": [ { "id": "67165ae77e90861c19847251", "name": "AI Ethics" }, { "id": "67165ae77e90861c1984724a", "name": "Machine Learning" }, { "id": "67165ae77e90861c1984724b", "name": "Natural Language Processing" } ], "skills": [ "Computer Programming", "Python Programming", "Algorithms", "Machine Learning Algorithms", "Applied Machine Learning" ], "chapterCount": 5, "chaptersDuration": 7271, "lectureCount": 14, "requirements": [ "Basic knowledge of machine learning principles, including familiarity with algorithms, training data, and supervised learning.", "A strong interest in ethical and social issues, particularly in the context of technology and development.", "Access to a computer with internet connectivity to watch video lectures and download resources.", "English reading and listening proficiency, as course materials are presented in English.", "A willingness to engage in reflective analysis, including participation in case-based and scenario-based learning activities." ], "objectives": [ "Explain the ethical implications of deploying machine learning systems, especially in low-resource or development-focused contexts.", "Identify common sources of bias in machine learning algorithms and datasets.", "Analyze real-world case studies to assess how fairness, equity, and accountability are addressed β€” or overlooked β€” in ML deployments.", "Apply a structured pedagogical framework to critically evaluate and teach fairness concepts in ML.", "Formulate strategies to mitigate ethical risks, such as bias and misuse, when planning or implementing ML-based interventions.", "These goals are derived from and adapted based on course materials from MIT OpenCourseWare, RES.EC-001, Spring 2020, under the CC BY-NC-SA 4.0 license." ], "purchases": 355, "attendees": 356, "attendancePercent": 100, "globalRating": 5, "totalReviews": 2, "paymentModel": "free", "price": 0, "thumbnailFileId": "68484351e3793dcbae5bd1cc", "trailerFileId": "684846dbe3793dcbae5be2cd", "trailerDuration": 111 } ] ``` ### Use Cases - **Market Research**: Analyze trending courses in AI and tech to identify market gaps. - **Competitive Intelligence**: Monitor competitors' course offerings and pricing strategies. - **Content Aggregation**: Build databases of educational resources for platforms or apps. - **Academic Research**: Study course structures, skills taught, and instructor profiles. - **Business Automation**: Automate data collection for reports on e-learning trends. - **Personal Learning**: Discover courses matching specific interests like "python" or "machine learning". ### Installation and Usage 1. Search for "Aladia Discovery Scraper" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters. 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). ### Output Format The Actor outputs a JSON array of course objects. Each object includes fields like `id`, `title`, `description`, `owner` (with instructor details), `categories`, `skills`, `requirements`, `objectives`, `purchases`, `globalRating`, and more. This structured data allows easy parsing for analysis, with timestamps in UTC and ratings on a 5-point scale. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! *** Unlock comprehensive course data from Aladia with our scraper! Extract titles, descriptions, instructors, categories, skills, and metrics effortlessly. Perfect for market research, competitive analysis, and content aggregation. Start scraping todayβ€”try it free on Apify! (148 characters) # Actor input Schema ## `searchQuery` (type: `string`): Keyword to search for courses by title (e.g. 'ai', 'machine learning', 'python'). ## `maxItems` (type: `integer`): Maximum number of courses to scrape. Set to 0 for unlimited. ## Actor input object example ```json { "searchQuery": "ai", "maxItems": 100 } ``` # Actor output Schema ## `overview` (type: `string`): No description # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/aladia-discovery-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = {} # Run the Actor and wait for it to finish run = client.actor("getdataforme/aladia-discovery-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call getdataforme/aladia-discovery-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/aladia-discovery-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Aladia-Discovery-Scraper", "description": "Extract detailed course data from Aladia with this powerful scraper. Search by keywords to gather titles, descriptions, instructors, categories, skills, metrics, and more. Ideal for market research, competitive analysis, and content aggregation....", "version": "0.0", "x-build-id": "7YFbvyoabTc0p1FZg" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~aladia-discovery-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-aladia-discovery-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~aladia-discovery-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-aladia-discovery-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~aladia-discovery-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-aladia-discovery-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "searchQuery" ], "properties": { "searchQuery": { "title": "Search Query", "type": "string", "description": "Keyword to search for courses by title (e.g. 'ai', 'machine learning', 'python').", "default": "ai" }, "maxItems": { "title": "Max Items", "minimum": 0, "type": "integer", "description": "Maximum number of courses to scrape. Set to 0 for unlimited.", "default": 100 } } }, "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 } } } } } } } } } } ```