We extract course listings, institution catalogues, MicroMasters details, pricing, and syllabus structures from edX. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Course Listings objects from edx.org. All fields typed and schema-versioned.
"course_id": "course-v1:MITx+6.00.1x+2T2024", "title": "Introduction to Computer Science and Programming Using Python", "institution": "MITx", "subject": "Computer Science", "level": "Introductory", "language": "English", "price_verified": 149.0, "enrollment_count": 1245000
| # | course_id | title | institution | subject | level | language |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Programmes & MicroMasters objects from edx.org. All fields typed and schema-versioned.
"programme_id": "micromasters-mitx-supply-chain-management", "title": "Supply Chain Management", "type": "MicroMasters", "institution": "MITx", "course_count": 6, "total_price": 1350.0, "skills_gained": "['Supply Chain Design', 'Inventory Management']"
| # | programme_id | title | type | institution | course_count | total_duration |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Institutions objects from edx.org. All fields typed and schema-versioned.
"institution_id": "mitx", "name": "Massachusetts Institute of Technology", "location": "Cambridge, MA", "course_count": 142, "programme_count": 18, "website_url": "https://web.mit.edu"
| # | institution_id | name | location | description | course_count | programme_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Instructors objects from edx.org. All fields typed and schema-versioned.
"instructor_id": "john-guttag", "name": "John Guttag", "title": "Dugald C. Jackson Professor of Computer Science and Electrical Engineering", "institution": "MIT", "course_count": 4, "bio": "John Guttag is a professor at MIT..."
| # | instructor_id | name | title | institution | bio | image_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Syllabus & Modules objects from edx.org. All fields typed and schema-versioned.
"course_id": "course-v1:MITx+6.00.1x+2T2024", "module_number": 1, "module_title": "Python Basics", "expected_duration": "4 hours", "content_type": "['Video', 'Reading', 'Exercise']", "learning_outcomes": "['Understand variables', 'Write basic loops']"
| # | course_id | module_number | module_title | expected_duration | content_type | learning_outcomes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our edX scraper handles the complex nested structures of online learning platforms: course hierarchies, programme bundles, dynamic pricing, and university metadata.
Extract titles, descriptions, enrollment counts, duration, effort estimates, and prerequisites for every course.
Map individual courses to parent structures like MicroMasters, Professional Certificates, and XSeries.
Capture the cost of the verified certificate track versus the free audit tier, including regional pricing variations.
Pull detailed module lists, weekly learning objectives, and assessment structures directly from the course outline.
Scrape instructor names, academic titles, biographies, and institutional affiliations across all courses.
Aggregate data at the university level to track total course output and programme offerings per institution.
Track student enrollment numbers over time to identify trending subjects and high-demand skills.
Extract the specific competencies and skills listed for each course to build job-market correlation models.
Capture course availability and transcript languages across global offerings.
Include premium offerings like university bootcamps and executive education programmes in the dataset.
Brief in. Clean data out.
Provide target institutions, subjects, or programme types. We design the extraction schema together.
We configure Scrapy and Playwright crawlers to navigate edX's single-page architecture and pagination.
Schema validation, null-rate checks, and nested-data resolution checks before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
edX relies on modern JavaScript frameworks and aggressive rate limiting. Here is how our infrastructure maintains constant throughput.
edX uses modern frontend frameworks that load course details dynamically. We use Playwright to execute JavaScript, wait for API calls to resolve, and extract the fully rendered DOM.
Aggressive scraping triggers CDN blocks. We distribute requests across residential proxies and normalise request headers to mimic standard browser behaviour.
Courses belong to multiple programmes. Our pipeline maintains relational integrity, outputting normalised tables that link courses, programmes, and institutions perfectly.
edX alters pricing based on the user's IP address. We route requests through specific regional proxies to capture accurate local pricing data.
Platform updates frequently break naive scrapers. We use fallback selector chains and monitor payload structures to ensure uninterrupted data delivery.
Platform operators analyse edX course structures, pricing, and enrollment volumes to identify gaps in their own offerings.
Universities track peer institution catalogues to benchmark their online presence and programme portfolio.
Enterprise training teams aggregate course data to build internal learning paths aligned with specific skill requirements.
Researchers analyse syllabus trends and instructor networks to map the evolution of emerging academic disciplines.
Content marketers extract course descriptions and learning outcomes to optimise educational lead generation portals.
Analysts correlate edX skill tags and enrollment metrics with job market demand to forecast future talent supply.
"edX aggregates the world's top universities into a single platform. Extracting this data reveals the exact skills and curricula driving the modern knowledge economy."
Mapping edX requires traversing complex programme-to-course hierarchies, executing JavaScript to render dynamic pricing tracks, and bypassing strict CDN rate limits. DataFlirt handles the proxy rotation and DOM parsing so your team can focus on curriculum analysis and market intelligence.
Everything supported by our edx.org scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles orchestration and deduplication. Playwright manages JavaScript execution for edX's dynamic frontend.
Pools of residential ISP proxies prevent rate limiting and enable accurate geotargeted price extraction.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management for complex nested scrapes.
Data delivered to where your team already works — no new tooling required.
About edx.org scraping, legality, and pipeline operations.
Ask us directly →Yes. The pipeline captures data across all partner institutions listed on the platform, mapping courses accurately to their respective universities.
Our schema uses relational IDs. A single course record will contain an array of programme IDs it belongs to, preventing data duplication while maintaining structural accuracy.
We use residential proxies to simulate requests from specific countries, ensuring the pricing data reflects local currency and purchasing power parity adjustments applied by edX.
Yes. We paginate through the review sections on course landing pages to extract text, ratings, and timestamps.
No. Video content and internal course materials are gated behind user enrollment and authentication. We only extract publicly available metadata, syllabi, and catalogue information.
We can configure pipelines to run daily, weekly, or monthly depending on your requirements for tracking new course launches and price changes.
Yes. We capture the specific metadata associated with premium offerings, including cohort start dates and application requirements.
Syllabus data is typically delivered as a nested JSON array within the course record, detailing modules, expected duration, and learning outcomes per section.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous monitoring of university course offerings, we build and operate the pipeline. Tell us what you need.
