We extract course metadata, tuition pricing, professional certificate requirements, and faculty directories from Stanford Online. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your schedule.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Course Catalogue objects from online.stanford.edu. All fields typed and schema-versioned.
"course_code": "CS224N", "title": "Natural Language Processing with Deep Learning", "department": "Computer Science", "tuition_fee": 4200.0, "currency": "USD", "credits": 3, "format": "Online"
| # | course_code | title | department | format | tuition_fee | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Programs & Degrees objects from online.stanford.edu. All fields typed and schema-versioned.
"program_name": "Artificial Intelligence Professional Program", "degree_type": "Professional Certificate", "school": "School of Engineering", "total_units": 15, "estimated_cost": 21000.0, "time_to_complete": "1 to 2 years"
| # | program_id | program_name | degree_type | school | total_units | estimated_cost |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Faculty Profiles objects from online.stanford.edu. All fields typed and schema-versioned.
"name": "Christopher Manning", "title": "Professor of Computer Science and Linguistics", "department": "Computer Science", "courses_taught": "['CS224N', 'CS224U']", "credentials": "Ph.D., Stanford University", "profile_url": "https://online.stanford.edu/instructors/christopher-manning"
| # | faculty_id | name | title | department | bio_text | courses_taught |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Syllabus Data objects from online.stanford.edu. All fields typed and schema-versioned.
"course_code": "CS224N", "module_number": 1, "module_title": "Introduction and Word Vectors", "description": "Introduction to NLP and deep learning.", "duration_hours": 4, "assignments": "['Assignment 1: Word Vectors']"
| # | course_code | module_number | module_title | description | learning_objectives | reading_materials |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Enrollment Logistics objects from online.stanford.edu. All fields typed and schema-versioned.
"course_code": "CS224N", "start_date": "2026-09-21", "end_date": "2026-12-11", "registration_deadline": "2026-09-14", "application_required": true, "financial_aid_available": false
| # | course_code | start_date | end_date | registration_deadline | prerequisites | grading_basis |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our pipeline handles the complexities of university course platforms. We parse nested degree requirements, track tuition changes, and normalise faculty data across disparate department pages.
Title, description, syllabus, credits, and formatting options scraped at the individual course level.
Capture base tuition, per-unit costs, technology fees, and certificate pricing with currency normalisation.
Extract complex prerequisite chains, elective options, and core unit requirements for graduate and professional programs.
Link course codes to instructor profiles, capturing academic titles, biographies, and departmental affiliations.
Monitor term start dates, application windows, and registration cutoffs to keep your internal calendars accurate.
Map courses back to their origin school, such as the School of Engineering or Graduate School of Business.
Identify new course additions, retired curriculum, and tuition adjustments without processing the entire catalogue.
Navigate dynamic React-based search interfaces to ensure complete coverage of all paginated results.
Resolve internal links between programs and individual course pages to build relational datasets.
Brief in. Clean data out.
Provide target departments, degree types, or specific URLs. We map the required data fields.
We configure crawlers to handle pagination, dynamic content loading, and schema variations across Stanford subdomains.
Automated checks ensure correct tuition parsing, valid dates, and complete prerequisite chains.
Data arrives in your warehouse or object store via scheduled exports in your preferred format.
Academic websites often mix legacy CMS structures with modern frontend frameworks. We normalise this inconsistent data.
Course search interfaces rely heavily on client-side rendering. We use Playwright to execute JavaScript, trigger filters, and wait for network idle states before extracting the DOM.
Different schools within the university often use distinct page templates. Our extractors apply cascading selector chains to find tuition and credit information regardless of layout variations.
Programs contain courses, and courses have multiple instructors. We generate unique identifiers to maintain these relationships, delivering normalised tables ready for SQL joins.
We respect university infrastructure by controlling concurrency, implementing exponential backoff, and rotating IPs to avoid triggering security firewalls.
Syllabus descriptions often contain irregular whitespace, HTML entities, and formatting artifacts. We clean and standardise all text fields before delivery.
Analyse pricing models, course formats, and emerging subject areas to benchmark against top-tier university offerings.
Map professional certificates and executive education programs to internal skills matrices for employee development.
Train machine learning models on high-quality syllabus structures and learning objectives to generate educational content.
Populate course discovery platforms with accurate tuition, duration, and prerequisite data directly from the source.
Track new program launches and faculty acquisitions across competing elite engineering and business schools.
Cross-reference faculty profiles and course availability to validate academic claims and professional background.
"Elite university catalogues contain the blueprint for modern professional education, but extracting that structure requires navigating decades of legacy web architecture."
Parsing academic data is rarely straightforward. Course pages vary by department, tuition models are complex, and prerequisites are often buried in unstructured text. DataFlirt applies strict schema validation to ensure you receive clean, queryable records instead of messy HTML dumps.
Everything supported by our online.stanford.edu scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Playwright handles complex frontend frameworks, ensuring all client-side data is fully loaded before extraction begins.
Residential IPs distribute requests geographically, preventing rate limits and ensuring uninterrupted access to public directories.
Every record passes through strict type checking. Null rates and formatting anomalies trigger alerts before data reaches your warehouse.
Data delivered to where your team already works — no new tooling required.
About online.stanford.edu scraping, legality, and pipeline operations.
Ask us directly →Yes. DataFlirt extracts only publicly available information such as course descriptions, public tuition figures, and faculty directories. We do not bypass authentication walls, nor do we access protected student data or internal learning management systems.
We maintain a library of selectors specific to different university templates. Our pipeline automatically detects the page structure and applies the correct extraction logic, ensuring consistent output regardless of the source layout.
Yes. We parse the text outlining core units, elective options, and prerequisite chains, structuring this information into queryable arrays and boolean flags.
For academic catalogues, we typically recommend weekly or monthly runs, as university curriculum changes relatively slowly. However, daily schedules can be configured if required.
We begin tracking tuition changes from the moment your pipeline is activated. Over time, this builds a comprehensive historical dataset of pricing adjustments.
Our monitoring systems detect schema drift immediately. If extraction fails due to a layout update, our engineering team patches the selectors within our SLA window, often before the next scheduled delivery.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop manually copying course details. We build and maintain the infrastructure to deliver structured university data directly to your systems.
