We extract Nanodegree structures, syllabus modules, pricing tiers, instructor credentials, and student reviews from Udacity. 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 Nanodegrees objects from udacity.com. All fields typed and schema-versioned.
"course_id": "nd013", "title": "Self Driving Car Engineer", "level": "Advanced", "estimated_duration": "5 months", "weekly_effort": "10 hours", "price_monthly": 399.0, "rating": 4.6, "review_count": 1204
| # | course_id | title | slug | level | estimated_duration | weekly_effort |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Syllabus Modules objects from udacity.com. All fields typed and schema-versioned.
"course_id": "nd013", "module_index": 1, "title": "Computer Vision", "description": "Learn to use cameras to find lane lines and track vehicles.", "lesson_count": 8, "project_title": "Advanced Lane Finding", "estimated_time": "3 weeks", "skills_applied": "['Python', 'OpenCV']"
| # | course_id | module_index | title | description | lesson_count | project_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Instructors objects from udacity.com. All fields typed and schema-versioned.
"instructor_id": "inst_842", "name": "Sebastian Thrun", "title": "Founder", "company": "Udacity", "bio": "Sebastian is an educator, programmer, robotics developer and computer scientist.", "courses_taught": "['nd013', 'cs373']", "linkedin_url": "https://linkedin.com/in/sebastianthrun"
| # | instructor_id | name | title | company | bio | image_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews objects from udacity.com. All fields typed and schema-versioned.
"review_id": "rev_9921", "course_id": "nd013", "rating": 5, "date": "2023-11-14", "review_text": "Excellent deep dive into computer vision and path planning.", "helpful_votes": 12, "graduation_status": "Graduated"
| # | review_id | course_id | student_name | rating | date | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Tiers objects from udacity.com. All fields typed and schema-versioned.
"course_id": "nd013", "tier_name": "Pay As You Go", "monthly_price": 399.0, "upfront_price": 1595.0, "discount_pct": 20, "currency": "USD", "scraped_at": "2023-12-01T10:00:00Z"
| # | course_id | tier_name | monthly_price | upfront_price | discount_pct | features_included |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our scraper bypasses React rendering overhead to extract clean JSON payloads directly from Udacity's backend APIs, delivering precise curriculum and pricing data.
Title, duration, effort, difficulty level, and core skills extracted for every program.
Extract module titles, lesson counts, and capstone project details for comprehensive curriculum mapping.
Capture monthly subscription rates, upfront discounts, and enterprise tier pricing.
Name, corporate affiliation, biography, and professional background for all course creators.
Extract prerequisite skills and target competencies for every program.
Star ratings, review body text, graduation status, and helpful vote counts.
Capture specialised course tracks designed for corporate upskilling.
Extract cross-sell and up-sell course linkages within the platform.
Run weekly or daily pipelines to detect new course launches and syllabus modifications.
Brief in. Clean data out.
Provide course categories, specific Nanodegree URLs, or skill keywords. We design the extraction schema together.
We configure Scrapy crawlers, session management, and pagination handling for udacity.com.
Schema validation, null-rate checks, and syllabus structure verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Modern single-page applications require deep network inspection. Here is how we extract data reliably.
Udacity relies on React and Next.js for rendering course pages. We intercept the underlying build ID and JSON payloads to extract structured data directly from the hydration state, bypassing brittle DOM parsing.
The main course catalogue loads via GraphQL queries. We replicate these requests with appropriate headers and pagination cursors to extract the complete catalogue without rendering overhead.
Udacity frequently tests different pricing models and discount structures based on geolocation and user session. We use fixed residential proxies to normalise pricing data across specific target regions.
Frontend layouts change, but backend data models remain stable. By targeting the Next.js data props, our extraction pipelines survive cosmetic UI updates.
We monitor for dropped fields, such as missing syllabus modules or null pricing data, alerting our operations team before bad data reaches your warehouse.
Track course launches, syllabus updates, and pricing changes across Udacity to inform your own curriculum development.
Map the skills taught in premium Nanodegrees against job market demand to identify emerging technology trends.
Compare Udacity enterprise course offerings, durations, and skill outcomes against other platforms for vendor selection.
Populate course discovery engines and review aggregators with up-to-date Udacity catalogue data.
Monitor subscription costs, promotional discounts, and regional pricing disparities for competitive benchmarking.
Identify industry experts and corporate practitioners teaching specialised technical courses for recruitment.
"Udacity holds the blueprint for modern technical upskilling. Accessing their curriculum data at scale provides an immediate map of enterprise technology trends."
Extracting course data from modern React applications requires deep network inspection and API interception. We bypass brittle DOM scraping by targeting the underlying data structures, ensuring your pipeline remains stable even when the frontend layout changes entirely.
Everything supported by our udacity.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
We bypass HTML parsing entirely by intercepting GraphQL queries and Next.js hydration payloads, ensuring perfect schema alignment.
Residential IPs allow us to capture accurate regional pricing and subscription tiers without triggering bot detection.
Hash-based change detection ensures you only process new courses or syllabus modifications, reducing downstream compute costs.
Data delivered to where your team already works — no new tooling required.
About udacity.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We extract all public syllabus data including module titles, lesson counts, project descriptions, and estimated completion times.
Our pipelines run on a schedule. Each run captures the current price, allowing you to build a time-series dataset of promotional discounts and tier adjustments.
Instead of parsing the DOM, we intercept the Next.js hydration state and GraphQL API responses. This provides a clean, structured JSON payload directly from their backend.
Yes. We route requests through residential proxy pools in your target country to capture localised pricing and course availability.
No. We only extract publicly available course catalogue data, syllabi, pricing, and reviews. We do not access gated student submissions or proprietary video content.
For a catalogue of Udacity's size, daily or weekly runs are standard. We can configure the cadence based on your specific monitoring requirements.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or a continuous syllabus monitoring feed, we scope, build, and operate the pipeline. Tell us what you need.
