We extract undergraduate courses, postgraduate listings, entry requirements, tariff points, and provider profiles from UCAS. 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 ucas.com. All fields typed and schema-versioned.
"course_id": "C-123456", "title": "Computer Science with Artificial Intelligence", "provider_name": "University of Leeds", "ucas_code": "G4G7", "study_mode": "Full-time", "duration": "3 Years", "location": "Main Campus", "start_date": "2025-09-22", "qualification": "BSc (Hons)"
| # | course_id | title | provider_name | ucas_code | study_mode | duration |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Entry Requirements objects from ucas.com. All fields typed and schema-versioned.
"ucas_code": "G4G7", "a_level_req": "AAA including Mathematics", "ib_req": "35 points overall", "btec_req": "D*DD in relevant subject", "tariff_points_min": 144, "tariff_points_max": 152, "gcse_req": "5 GCSEs at grade 4 (C) or above", "interview_required": false
| # | ucas_code | a_level_req | ib_req | btec_req | tariff_points_min | tariff_points_max |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fees & Funding objects from ucas.com. All fields typed and schema-versioned.
"ucas_code": "G4G7", "uk_fee": 9250.0, "international_fee": 26500.0, "fee_year": "2025/26", "placement_year_fee": 1850.0, "financial_support_available": true, "scholarships_url": "https://www.leeds.ac.uk/scholarships", "living_costs_est": 11500.0
| # | ucas_code | uk_fee | international_fee | fee_year | placement_year_fee | financial_support_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Provider Profiles objects from ucas.com. All fields typed and schema-versioned.
"provider_id": "P-789", "provider_name": "University of Leeds", "region": "Yorkshire and The Humber", "city": "Leeds", "campus_count": 1, "student_satisfaction_pct": 82.4, "graduate_employment_pct": 89.1, "accommodation_guarantee": true
| # | provider_id | provider_name | region | city | campus_count | student_satisfaction_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Clearing Data objects from ucas.com. All fields typed and schema-versioned.
"ucas_code": "G4G7", "course_title": "Computer Science", "provider_name": "University of Leeds", "clearing_status": "Open", "available_spots": 12, "hotline_number": "0800 123 4567", "lowered_tariff_points": 128, "update_timestamp": "2025-08-14T09:15:00Z"
| # | ucas_code | course_title | provider_name | clearing_status | available_spots | hotline_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our UCAS scraper handles the full course catalogue: dynamic search filters, entry requirements, tariff calculators, and high-frequency Clearing updates. We manage the session persistence and normalisation required to extract clean data.
Extract module structures, assessment methods, duration, study modes, and qualification types across undergraduate and postgraduate listings.
Capture A-Level, BTEC, and IB requirements alongside minimum and maximum UCAS tariff point thresholds for every course.
Monitor course availability and lowered entry requirements during the August Clearing period with sub-15 minute latency.
Extract domestic UK fees, international tuition rates, placement year costs, and financial support availability per academic year.
Aggregate university details including location, campus facilities, student satisfaction metrics, and graduate employment statistics.
Scrape upcoming open day schedules, virtual tour links, and booking URLs across all registered UK higher education providers.
Capture degree apprenticeship opportunities, employer details, salary expectations, and application deadlines.
Extract specialised requirements for master's programmes, PGCE courses, and performance audition criteria for conservatoires.
Run weekly full-catalogue refreshes or configure high-frequency pipelines for real-time vacancy monitoring during admission cycles.
Brief in. Clean data out.
Provide target universities, subject areas, or regions. We design the extraction schema together.
We configure Scrapy and Playwright crawlers to navigate UCAS search filters and dynamic pagination.
Schema validation, null-rate checks, and fee outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
UCAS aggregates data from hundreds of universities, resulting in inconsistent page structures and heavy rate limits. Here is how we maintain data quality.
UCAS employs strict rate limiting during peak admission cycles. We distribute requests across a pool of UK residential IP addresses, regulating request velocity to avoid IP bans and ensure consistent data delivery.
Course search results and clearing filters rely heavily on client-side rendering. We execute full browser sessions to trigger lazy-loaded content and navigate complex filtering states that static HTML parsers miss.
While UCAS hosts the data, universities format their course descriptions differently. We apply post-extraction regex and NLP parsing to normalise entry requirements and module lists into a strictly typed schema.
During A-Level results day, course availability changes by the minute. Our Kubernetes cluster auto-scales crawler pods to achieve sub-15 minute refresh rates across targeted Clearing vacancies.
We maintain a hash index of last-seen values for the 35,000+ courses. Subsequent runs only push diffs, reducing compute cost and providing a clean changelog of tariff or fee adjustments over time.
Universities benchmark entry requirements, tuition fees, and new course offerings against peer institutions to optimise their own portfolios.
Education platforms and career portals absorb structured UK course data to power their own recommendation engines and student advisory services.
Admissions teams monitor real-time competitor vacancies and lowered tariff points during August to adjust their own Clearing thresholds.
International recruitment agencies map available programmes, entry criteria, and international fees to match prospective students with suitable universities.
Think tanks and researchers analyse tuition fee trends, subject availability, and regional education distribution over multiple academic years.
Publishers build programmatic local education portals and ranking guides using normalised course and provider profile data.
"UCAS holds the definitive dataset for UK higher education, but extracting normalised course requirements across 395 distinct university templates requires dedicated infrastructure."
Universities update course structures, tariff points, and tuition fees continuously. During the August Clearing period, vacancy statuses change by the minute. DataFlirt provides the proxy networks, schema normalisation, and concurrency required to turn fragmented university pages into a queryable relational database.
Everything supported by our ucas.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 and complex search filter navigation on the UCAS portal.
We maintain pools of UK residential ISP proxies to bypass rate limits and geographic restrictions, ensuring consistent access during peak traffic periods.
Pipelines run on Kubernetes for sustained workloads and AWS Lambda for burst scaling during Clearing. Airflow handles scheduling and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About ucas.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available course listings, tariff requirements, and university profiles is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract personal applicant data, personal statements, or individual offers. Clients should review UCAS terms of service and consult legal counsel for specific use cases.
During the August Clearing period, we deploy burst compute resources to achieve sub-15 minute latency on availability status changes for a predefined list of target courses or competitor institutions.
Yes. Where universities publish distinct tuition fees for UK domestic students and international students, we extract both values and normalise them into separate schema fields.
While UCAS standardises some fields, detailed course descriptions often vary by provider. We use multi-layer fallback chains and regex pattern matching to extract structured data like module lists and assessment weightings from unstructured text blocks.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per course for tariff points and fees from the date your pipeline is commissioned.
Our smallest packages start at a defined list of universities or subject areas with weekly delivery. For full-catalogue extraction or high-frequency Clearing monitoring, we price based on compute volume and delivery cadence.
Absolutely. We provide a sample run of up to 500 courses or 10 university profiles as part of the pre-engagement scoping process, allowing you to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of all undergraduate courses or continuous monitoring of Clearing vacancies, we scope, build, and operate the pipeline. Tell us what you need.
