# DataCamp Scraper (`crawlerbros/datacamp-scraper`) Actor Scrape DataCamp's full content catalog - 750+ courses, 30+ career tracks, and 250+ hands-on projects. Filter by topic, technology, and skill level across Data Science, Python, ML, SQL, and more. - **URL**: https://apify.com/crawlerbros/datacamp-scraper.md - **Developed by:** [Crawler Bros](https://apify.com/crawlerbros) (community) - **Categories:** Automation, Developer tools, Integrations - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## DataCamp Scraper Scrape DataCamp's full content catalog — 750+ courses, 30+ career tracks, and 250+ hands-on projects across Data Science, Python, Machine Learning, SQL, and more. No login required. ### What does DataCamp Scraper do? This actor extracts structured data from DataCamp's public content catalog. Retrieve course details including ratings, enrollment counts, instructors, topics, and technologies. Filter by skill level, topic, and technology to find exactly the content you need. ### Input Parameters | Parameter | Type | Description | Default | |-----------|------|-------------|---------| | `mode` | Enum | Content type: `courses`, `paths`, `projects` | `courses` | | `topic` | Enum | Topic filter (Machine Learning, Data Visualization, Python Programming, etc.) | All | | `technology` | Enum | Technology filter (Python, R, SQL, Tableau, etc.) | All | | `skillLevel` | Enum | Difficulty: Beginner, Intermediate, Advanced | All | | `searchQuery` | String | Text search in title and description | — | | `maxItems` | Integer | Maximum number of records (1–2000) | 50 | #### Modes - **courses** — Browse DataCamp's full course catalog with ratings and enrollment data - **paths** — Browse career tracks (structured learning paths). Note: DataCamp's tracks listing has no per-track topic/skill-level data, so `topic` and `skillLevel` filters are ignored in this mode; only `technology` and `searchQuery` apply. - **projects** — Browse hands-on data science projects ### Output Fields #### Course Records (`recordType: "course"`) | Field | Type | Description | |-------|------|-------------| | `title` | String | Course title | | `url` | String | Direct URL to the course | | `description` | String | Short course description | | `topic` | Array | Topic areas (e.g., Machine Learning, Data Manipulation) | | `technology` | Array | Technologies used (e.g., Python, SQL, R) | | `skillLevel` | String | Difficulty: Beginner, Intermediate, or Advanced | | `estimatedHours` | Number | Course duration in hours | | `rating` | Number | Average rating (out of 5) | | `ratingCount` | Integer | Number of ratings | | `enrolledCount` | Integer | Number of students started | | `completionsCount` | Integer | Number of students who completed the course | | `launchDate` | String | Date the course was launched (YYYY-MM-DD) | | `hasPractice` | Boolean | Whether the course includes interactive practice exercises | | `mobileEnabled` | Boolean | Whether the course is available in the DataCamp mobile app | | `instructor` | String | Primary instructor name | | `instructors` | Array | All instructor names (if multiple) | | `isFree` | Boolean | Whether the course is free | | `sourceUrl` | String | Source catalog URL | | `scrapedAt` | String | ISO timestamp | | `recordType` | String | Always `"course"` | #### Path Records (`recordType: "path"`) | Field | Type | Description | |-------|------|-------------| | `title` | String | Career track title (e.g., "Data Analyst in SQL") | | `url` | String | Direct URL to the track | | `description` | String | Track description | | `technology` | Array | Primary technology | | `estimatedHours` | Number | Total time to complete | | `courseCount` | Integer | Number of courses in the track | | `sourceUrl` | String | Source catalog URL | | `scrapedAt` | String | ISO timestamp | | `recordType` | String | Always `"path"` | #### Project Records (`recordType: "project"`) | Field | Type | Description | |-------|------|-------------| | `title` | String | Project title | | `url` | String | Direct URL to the project | | `description` | String | Project description | | `topic` | Array | Topics covered | | `technology` | Array | Technologies used | | `skillLevel` | String | Difficulty level | | `estimatedHours` | Number | Estimated completion time | | `rating` | Number | Average rating | | `ratingCount` | Integer | Number of ratings | | `enrolledCount` | Integer | Number of learners who started the project | | `completionsCount` | Integer | Number of learners who completed the project | | `launchDate` | String | Date the project was launched (YYYY-MM-DD) | | `instructor` | String | Primary author/instructor name | | `instructors` | Array | All author names (if multiple) | | `sourceUrl` | String | Source catalog URL | | `scrapedAt` | String | ISO timestamp | | `recordType` | String | Always `"project"` | ### Example Output ```json { "title": "Introduction to Python", "url": "https://www.datacamp.com/courses/intro-to-python-for-data-science", "description": "Master the basics of data analysis with Python in just four hours.", "topic": ["Programming"], "technology": ["Python"], "skillLevel": "Beginner", "estimatedHours": 4.0, "rating": 4.83, "ratingCount": 8956, "enrolledCount": 127114, "instructor": "Hugo Bowne-Anderson", "isFree": false, "sourceUrl": "https://www.datacamp.com/courses-all", "scrapedAt": "2026-06-30T12:00:00+00:00", "recordType": "course" } ```` ### Use Cases - **EdTech research** — Analyze DataCamp's curriculum, ratings, and instructor data - **Competitive analysis** — Compare DataCamp offerings with Coursera, Codecademy, etc. - **Course discovery** — Find top-rated Python/ML/SQL courses with enrollment data - **Market analysis** — Understand which data science topics are most popular - **Content auditing** — Catalog all available courses by technology or topic ### Frequently Asked Questions **Does this require a DataCamp account?** No. This actor scrapes only publicly available catalog information — no subscription needed. **How many courses are available?** DataCamp has 750+ courses, 30+ career tracks, and 250+ projects. Set `maxItems` high to get the full catalog. **Can I filter by both topic and technology?** Yes. Combine `topic` (e.g., Machine Learning) and `technology` (e.g., Python) to narrow results. **What technologies are available?** Python, R, SQL, Tableau, Power BI, Excel, Scala, Julia, Shell, Git, Snowflake, Databricks, Azure, Google Cloud, and Java. **What topics are available?** Applied Finance, Artificial Intelligence, Cloud, Data Engineering, Data Literacy, Data Management, Data Manipulation, Data Preparation, Data Visualization, Data Warehouse, Exploratory Data Analysis, Importing & Cleaning Data, Leadership, Machine Learning, Other, Probability & Statistics, Programming, Python Programming, and Reporting. **How fresh is the data?** Data is fetched live from DataCamp's public website on each run. Ratings and enrollment counts reflect the current state. # Actor input Schema ## `mode` (type: `string`): What content to scrape. ## `topic` (type: `string`): Filter by topic/domain. ## `technology` (type: `string`): Filter by programming technology or tool. ## `skillLevel` (type: `string`): Filter by difficulty level. ## `searchQuery` (type: `string`): Text search to filter results by title or description. ## `maxItems` (type: `integer`): Maximum number of records to return. ## Actor input object example ```json { "mode": "courses", "maxItems": 20 } ``` # Actor output Schema ## `items` (type: `string`): Dataset containing all scraped DataCamp content 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": "courses", "maxItems": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("crawlerbros/datacamp-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 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); }); // 📚 Want to learn more 📖? Go to → 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": "courses", "maxItems": 20, } # Run the Actor and wait for it to finish run = client.actor("crawlerbros/datacamp-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "mode": "courses", "maxItems": 20 }' | apify call crawlerbros/datacamp-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=crawlerbros/datacamp-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "DataCamp Scraper", "description": "Scrape DataCamp's full content catalog - 750+ courses, 30+ career tracks, and 250+ hands-on projects. Filter by topic, technology, and skill level across Data Science, Python, ML, SQL, and more.", "version": "1.0", "x-build-id": "ntQzzYjlS440bNyCH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/crawlerbros~datacamp-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-crawlerbros-datacamp-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/crawlerbros~datacamp-scraper/runs": { "post": { "operationId": "runs-sync-crawlerbros-datacamp-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/crawlerbros~datacamp-scraper/run-sync": { "post": { "operationId": "run-sync-crawlerbros-datacamp-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "mode": { "title": "Mode", "enum": [ "courses", "paths", "projects" ], "type": "string", "description": "What content to scrape.", "default": "courses" }, "topic": { "title": "Topic", "enum": [ "", "Applied Finance", "Artificial Intelligence", "Cloud", "Data Engineering", "Data Literacy", "Data Management", "Data Manipulation", "Data Preparation", "Data Visualization", "Data Warehouse", "Exploratory Data Analysis", "Importing & Cleaning Data", "Leadership", "Machine Learning", "Other", "Probability & Statistics", "Programming", "Python Programming", "Reporting" ], "type": "string", "description": "Filter by topic/domain." }, "technology": { "title": "Technology", "enum": [ "", "Python", "R", "SQL", "Tableau", "Power BI", "Excel", "Scala", "Julia", "Shell", "Git", "Snowflake", "Databricks", "Azure", "Google Cloud", "Java" ], "type": "string", "description": "Filter by programming technology or tool." }, "skillLevel": { "title": "Skill Level", "enum": [ "", "Beginner", "Intermediate", "Advanced" ], "type": "string", "description": "Filter by difficulty level." }, "searchQuery": { "title": "Search Query", "type": "string", "description": "Text search to filter results by title or description." }, "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Maximum number of records to return.", "default": 50 } } }, "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 } } } } } } } } } } ```