We extract university profiles, master's and PhD program details, tuition costs, and admission criteria from Gradschools.com. 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 Program Listings objects from gradschools.com. All fields typed and schema-versioned.
"program_id": "PRG-847291", "title": "Master of Science in Data Science", "university_name": "New York University", "degree_type": "Masters", "subject_category": "Computer Science", "format": "On-Campus", "location": "New York, NY", "credits": 36
| # | program_id | title | university_name | degree_type | subject_category | format |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for University Profiles objects from gradschools.com. All fields typed and schema-versioned.
"university_id": "UNI-4021", "name": "New York University", "location": "New York, NY", "institution_type": "Private", "total_enrollment": 53284, "accreditation": "Middle States Commission on Higher Education", "programs_count": 142
| # | university_id | name | location | institution_type | total_enrollment | accreditation |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Complete list of extractable fields for Admissions Data objects from gradschools.com. All fields typed and schema-versioned.
"program_id": "PRG-847291", "gpa_requirement": "3.0 minimum", "gre_required": false, "letters_of_recommendation": 2, "statement_of_purpose": true, "application_fee": 110.0, "interview_required": false
| # | program_id | gpa_requirement | gre_required | gmat_required | letters_of_recommendation | statement_of_purpose |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Tuition & Funding objects from gradschools.com. All fields typed and schema-versioned.
"program_id": "PRG-847291", "resident_tuition": 48500.0, "nonresident_tuition": 48500.0, "cost_per_credit": 2100.0, "financial_aid_available": true, "assistantships_offered": true, "currency": "USD"
| # | program_id | resident_tuition | nonresident_tuition | cost_per_credit | financial_aid_available | assistantships_offered |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from gradschools.com. All fields typed and schema-versioned.
"keyword": "machine learning", "position": 3, "program_id": "PRG-847291", "program_name": "Master of Science in Data Science", "university_name": "New York University", "sponsored_listing": false, "online_badge": false
| # | keyword | subject_filter | position | program_id | program_name | university_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Gradschools.com scraper navigates deep subject taxonomies, handles dynamic location filtering, and normalises inconsistent academic data across thousands of university profiles.
Capture degree titles, credit requirements, curriculum descriptions, and program formats across Master's, PhD, and Certificate levels.
Extract institution type, total enrollment figures, accreditation details, and campus locations linked to every graduate program.
Track GPA minimums, GRE/GMAT requirements, recommendation letter counts, and application fees per program.
Capture resident vs non-resident tuition rates, cost per credit, and financial aid availability indicators.
Distinguish between fully online, hybrid, and traditional on-campus programs with specific location markers.
Identify promoted university placements within search results and category pages to understand advertising spend.
Maintain the exact category hierarchy from broad subjects down to niche specialisations for accurate categorisation.
Extract Fall, Spring, and Summer application deadlines to support recruitment and timing analysis.
Run pipelines monthly or quarterly to capture new program launches and updated tuition figures.
Brief in. Clean data out.
Specify target subjects, degree types, or geographic regions. We configure the extraction schema to match your requirements.
We deploy Scrapy crawlers with proxy rotation and session management to navigate Gradschools.com's dynamic filters.
Schema validation, tuition format normalisation, and null-rate checks ensure data consistency before delivery.
Structured JSON, CSV, or Parquet pushed directly to your S3 bucket, BigQuery, or Snowflake stage.
Gradschools.com presents structural challenges common to legacy directories. Here is how we maintain data integrity.
Gradschools.com frequently deploys modal overlays and forced lead-capture forms that block navigation. Our Playwright scripts automatically detect and dismiss these elements, ensuring uninterrupted traversal of program listings.
The directory features a mix of recently updated university profiles and legacy listings with missing fields. We apply normalisation rules to standardise tuition formats, degree abbreviations, and deadline structures across the entire dataset.
Filtering programs by specific states or formats relies on asynchronous JavaScript requests. We replicate these API calls directly or use headless browsers to ensure complete geographic coverage without missing paginated results.
Graduate programs are nested deep within subject hierarchies. Our crawlers map the full category tree before execution, ensuring no niche specialisation or sub-category is dropped during the extraction process.
Scraping tens of thousands of program pages triggers standard rate limits. We distribute requests across residential IP pools with randomised delays to maintain high throughput without triggering blocks.
Analyse program proliferation across specific subjects to identify gaps in online education offerings.
Universities track peer institutions' tuition rates, program formats, and admission requirements to position their own degrees.
Enrolment agencies map program density by region to optimise marketing spend and target underserved student populations.
Secondary directories and career portals enrich their own databases with structured program prerequisites and descriptions.
Financial analysts and policy researchers track the inflation of graduate tuition and fees across public versus private institutions.
Service providers targeting specific academic departments use program data to map university structures and identify key decision areas.
"Gradschools.com holds the definitive taxonomy of global graduate education, but querying tuition trends or admission shifts requires structured extraction."
Navigating inconsistent university listings, lead-capture overlays, and deeply nested subject categories requires dedicated crawler infrastructure. DataFlirt manages the proxy rotation, session handling, and schema normalisation so your data science team can focus on analysis rather than maintenance.
Everything supported by our gradschools.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.
Scrapy handles crawl orchestration and deduplication. Playwright manages JavaScript rendering, lead-form dismissal, and dynamic filter interactions.
We maintain pools of residential ISP proxies to avoid rate limits and IP bans during high-volume directory extraction.
Pipelines execute on AWS Lambda and ECS. Airflow manages scheduling, dependency execution, and SLA alerting with state stored in Postgres.
Data delivered to where your team already works — no new tooling required.
About gradschools.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available directory information is generally permissible under applicable law. DataFlirt targets only public program, university, and tuition data. We do not extract personal user data or bypass authentication walls. Clients must review the source website's Terms of Service and consult legal counsel for specific use cases.
Our extraction pipelines use Playwright to simulate browser environments. We configure automated routines to detect and dismiss modal overlays and lead-gen forms, ensuring the crawler reaches the underlying program data.
We typically run full directory refreshes on a monthly or quarterly cadence, aligning with academic update cycles. Delta reports can be generated to highlight newly added programs or changes in tuition.
Yes. University listings often present tuition inconsistently (e.g., per credit, per semester, per year). We apply normalisation rules to extract the numeric values and categorise them correctly in the final schema.
Yes. If the program or university is listed within the Gradschools.com directory, regardless of geographic location, our crawlers will extract the profile and associated details.
Yes. We deliver data in JSON, CSV, and Parquet by default, but we can map the output to your specific internal database schema before pushing to your warehouse.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full export of all Master's programs or continuous tracking of tuition changes across specific universities, we manage the infrastructure. Tell us your requirements.
