We extract course details, tuition fees, instructor credentials, and degree tracks from Harvard Extension. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery 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 Details objects from extension.harvard.edu. All fields typed and schema-versioned.
"course_id": "CSCI-E-104", "title": "Data Structures and Algorithms", "subject_area": "Computer Science", "credit_hours": 4, "format": "Online", "term": "Fall 2026", "tuition_fee": 3100.0, "prerequisites": "['CSCI-E-10A']"
| # | course_id | title | subject_area | description | credit_hours | format |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Instructors objects from extension.harvard.edu. All fields typed and schema-versioned.
"instructor_id": "INST-4921", "full_name": "Dr. Jane Smith", "academic_title": "Adjunct Professor", "department": "Computer Science", "courses_taught": "['CSCI-E-104', 'CSCI-E-105']", "email_address": "jsmith@extension.harvard.edu", "linkedin_url": "linkedin.com/in/janesmith"
| # | instructor_id | full_name | academic_title | department | biography | courses_taught |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Degree Tracks objects from extension.harvard.edu. All fields typed and schema-versioned.
"degree_id": "ALM-CS", "title": "Master of Liberal Arts, Computer Science", "field_of_study": "Computer Science", "total_credits_required": 48, "estimated_tuition": 37200.0, "average_duration_years": 3.5, "core_courses": "['CSCI-E-104', 'CSCI-E-119']"
| # | degree_id | title | field_of_study | total_credits_required | core_courses | elective_options |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Schedules & Deadlines objects from extension.harvard.edu. All fields typed and schema-versioned.
"course_id": "CSCI-E-104", "term": "Fall 2026", "start_date": "2026-09-02", "end_date": "2026-12-18", "meeting_times": "Tuesdays 7:40 PM EST", "registration_deadline": "2026-08-25", "withdrawal_deadline": "2026-10-15"
| # | course_id | term | start_date | end_date | meeting_times | registration_deadline |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Certificates objects from extension.harvard.edu. All fields typed and schema-versioned.
"certificate_id": "CERT-DS", "title": "Data Science Certificate", "category": "Technology", "credit_minimum": 16, "cost_estimate": 12400.0, "format": "Online", "required_courses": "['CSCI-E-109A', 'CSCI-E-109B']"
| # | certificate_id | title | category | required_courses | credit_minimum | cost_estimate |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our scraper navigates the complex Harvard Extension course search interface, extracting deeply nested academic requirements, schedules, and faculty profiles with full JavaScript rendering.
Extract course titles, descriptions, credit hours, and subject areas across all academic terms.
Capture faculty credentials, biographies, and map them to specific course sections.
Parse text-based prerequisite requirements into structured dependency graphs.
Extract per-course tuition costs, registration fees, and material expenses.
Normalise meeting times, term start dates, and registration deadlines into standard ISO formats.
Extract core requirements, electives, and admission criteria for ALM and ALB degree programmes.
Identify online, on-campus, and hybrid course delivery methods.
Extract available syllabus links, required reading lists, and grading criteria.
Detect new courses, retired programmes, and schedule changes across academic years.
Brief in. Clean data out.
Specify target terms, subject areas, or degree tracks. We design the extraction schema together.
We configure Playwright crawlers to navigate the dynamic course search filters and pagination.
Schema validation, null-rate checks, and prerequisite graph verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or BigQuery dataset on agreed cadence.
Academic data structures are notoriously irregular. Here is how we standardise Harvard Extension's catalogue into queryable records.
The course search interface relies on client-side rendering. We deploy full Playwright browser sessions to interact with dropdowns, trigger search events, and capture XHR responses containing the raw catalogue data.
University websites frequently redesign their layouts between semesters. Our extraction logic relies on semantic DOM parsing and API endpoint interception, minimising breakage when the frontend changes.
Prerequisites are often written as unstructured text strings. We apply regex patterns and NLP models to extract specific course codes and logical operators, delivering a clean dependency array.
We maintain a state index of the catalogue. Subsequent crawls only emit records for new courses, modified schedules, or updated tuition fees, preventing duplicate data ingestion.
Our QA pipelines enforce logical constraints: end dates must follow start dates, credit hours must fall within standard ranges, and required courses must exist in the master catalogue.
Course aggregators ingest catalogue data to build unified search engines for continuing education programmes.
Other universities track tuition pricing, new course offerings, and certificate structures to remain competitive.
Analysts track the growth of specific subject areas, such as Data Science or Management, to gauge industry demand.
Advising platforms use prerequisite graphs and schedule data to generate automated degree completion plans.
Enterprise HR departments map available courses to internal skill requirements for employee tuition reimbursement programmes.
Registrar offices at other institutions use syllabus metadata and credit hours to evaluate transfer equivalencies.
"Harvard Extension's catalogue is a goldmine for continuing education trends, but the underlying data model is deeply nested and difficult to query at scale."
Extracting academic data requires navigating complex prerequisite trees, dynamic course filters, and term-specific schedule variations. DataFlirt handles the extraction complexity, delivering normalised course graphs so your engineering team can focus on building products.
Everything supported by our extension.harvard.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.
Scrapy handles crawl orchestration and deduplication. Playwright handles JavaScript rendering and interaction with React-based search filters.
Custom parsing logic standardises irregular text fields into relational graphs, mapping courses to instructors, prerequisites, and degree tracks.
Pipelines run on AWS ECS. Airflow handles scheduling for term-based catalogue refreshes. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About extension.harvard.edu scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available course catalogues and tuition information is generally permissible. DataFlirt targets only public, non-authenticated data. We do not attempt to bypass student login walls or extract personal student records.
We use Playwright to render the page, interact with the React-based dropdowns, and intercept the underlying JSON API responses, ensuring we capture the complete dataset without relying solely on DOM parsing.
We typically configure pipelines to run weekly during registration periods and monthly during active semesters, capturing schedule changes and new course additions.
Yes. We navigate the programme pages to extract total credit requirements, core courses, elective options, and estimated costs for both degrees and certificates.
We extract syllabus metadata, textbook requirements, and links to publicly available PDF syllabi. We do not extract content hosted inside the authenticated Canvas portal.
Yes. We provide a sample run covering a specific subject area or certificate track so you can validate the schema and data quality before committing to a production pipeline.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-time catalogue export or continuous term monitoring, we scope, build, and operate the pipeline. Tell us your requirements.
