We extract university profiles, admission statistics, GPA requirements, SAT/ACT score ranges, and the complete test prep blog corpus. 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 College Profiles objects from prepscholar.com. All fields typed and schema-versioned.
"university_name": "Stanford University", "location": "Stanford, CA", "acceptance_rate": 4.3, "avg_gpa": 3.96, "avg_sat": 1505, "avg_act": 34, "tuition_in_state": 56169.0
| # | university_name | location | acceptance_rate | avg_gpa | avg_sat | avg_act |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Admissions Requirements objects from prepscholar.com. All fields typed and schema-versioned.
"university_name": "Stanford University", "application_deadline": "2027-01-05", "early_decision_deadline": "2026-11-01", "application_fee": 90.0, "common_app_accepted": true, "recommendation_letters_req": 2, "interview_req": "Optional"
| # | university_name | application_deadline | early_decision_deadline | application_fee | common_app_accepted | coalition_app_accepted |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for SAT/ACT Statistics objects from prepscholar.com. All fields typed and schema-versioned.
"university_name": "Stanford University", "sat_25th_percentile": 1440, "sat_75th_percentile": 1570, "act_25th_percentile": 32, "act_75th_percentile": 35, "sat_reading_avg": 740, "sat_math_avg": 765
| # | university_name | sat_25th_percentile | sat_75th_percentile | act_25th_percentile | act_75th_percentile | sat_reading_avg |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Blog Corpus objects from prepscholar.com. All fields typed and schema-versioned.
"post_id": "ps-blog-8492", "title": "How to Get a Perfect 1600 on the SAT", "author": "Allen Cheng", "publish_date": "2020-04-15", "category": "SAT Strategies", "word_count": 4520, "tags": "['SAT', 'Perfect Score', 'Study Guide']"
| # | post_id | title | author | publish_date | category | tags |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Financial Aid & Costs objects from prepscholar.com. All fields typed and schema-versioned.
"university_name": "Stanford University", "total_cost_attendance": 78898.0, "average_financial_aid": 58472.0, "percent_receiving_aid": 65, "room_and_board_cost": 17860.0, "books_supplies_cost": 1300.0, "fafsa_code": "001305"
| # | university_name | total_cost_attendance | average_financial_aid | percent_receiving_aid | room_and_board_cost | books_supplies_cost |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our PrepScholar scraper handles the extraction of complex HTML tables, normalises admission statistics, and parses a decade of blog content into clean, queryable datasets.
Capture acceptance rates, GPA requirements, and campus details for thousands of institutions.
Extract 25th and 75th percentile SAT and ACT scores, broken down by section.
Scrape full-text articles, author metadata, publish dates, and categorisation tags from the test prep blog.
Track in-state vs out-of-state tuition, room and board costs, and average financial aid packages.
Monitor Early Action, Early Decision, and Regular Decision deadlines across all listed universities.
Extract required application materials, recommendation letter counts, and interview policies.
Detect changes in admission statistics and tuition costs as universities update their reporting each academic year.
Parse complex, inconsistent HTML tables used for score distributions into flat, relational schemas.
Run one-off bulk exports of the entire college database or configure monthly pipelines for updates.
Brief in. Clean data out.
Provide target university lists, blog categories, or specific data points. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and HTML table parsers for prepscholar.com.
Schema validation, null-rate checks, and data normalisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Educational sites often feature inconsistent DOM structures across older content. Here is how we ensure data quality.
PrepScholar has published content for over a decade. Older blog posts use different HTML structures than modern ones. Our pipelines use fallback chains and heuristic parsing to extract author, date, and content regardless of the template version.
Admission statistics and score ranges are frequently embedded in complex HTML tables. We use custom parsers to flatten these tables into strict, typed JSON schemas, converting string ranges into discrete integer fields.
To prevent IP bans during full-site crawls, we route requests through distributed proxy pools with randomised delays, ensuring high success rates without triggering security blocks.
University data changes annually. We maintain hash indexes of previous extracts and alert on significant statistical anomalies to ensure the latest admission cycles are captured accurately.
Educational data contains mixed types. We cast tuition to floats, dates to ISO-8601, and normalise boolean flags for application requirements before delivery.
College counselling and application platforms integrate admission statistics to build student matching algorithms.
AI companies extract the vast corpus of test prep strategies and grammar rules to train educational models.
Analysts track tuition inflation, acceptance rate trends, and application requirement shifts across US universities.
Tutoring services identify target demographics based on regional SAT/ACT performance averages.
Researchers analyse long-term trends in standardised testing requirements and holistic admission policies.
Scholarship search engines populate their databases with university-specific financial aid statistics and costs.
"PrepScholar holds the definitive corpus of US college admission statistics and test prep strategies, but it requires structured extraction to be analytically useful."
Scraping educational content and admission tables requires handling inconsistent DOM layouts across a decade of blog posts, parsing nested HTML tables, and monitoring for annual statistic updates. DataFlirt manages the extraction logic so your engineers can build applications, not parsers.
Everything supported by our prepscholar.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 high-concurrency crawl orchestration, link extraction, and retry logic for the static portions of the site.
We deploy bespoke parsing modules to target specific table structures, ensuring accurate mapping of admission statistics regardless of layout variations.
Pipelines run on AWS ECS. Airflow handles scheduling and dependency management. All extracted data is validated against strict JSON schemas before delivery.
Data delivered to where your team already works — no new tooling required.
About prepscholar.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information, such as blog posts and college statistics, is generally permissible under applicable law. DataFlirt targets only public, non-authenticated data. We do not extract paid course content or circumvent authentication walls.
University admission statistics typically update annually. We can configure pipelines to run monthly or quarterly to catch incremental updates, or perform a full refresh at the start of the academic application cycle.
Yes. Our crawlers traverse pagination and archive links to extract the complete historical corpus of test prep articles and strategies.
Our extraction schema applies strict type casting. If a university profile lists tuition as a range or includes text notes, our parsers clean the string and output a normalised numeric value.
Yes. We can deliver the data as flat CSVs or relational SQL inserts, mapping universities to their respective admission requirements and financial aid statistics.
We start with a defined scope, typically a full extract of the college database or the blog corpus. Contact us with your specific data requirements for a quote.
Yes. We provide sample extracts of up to 50 university profiles or 100 blog posts during the scoping phase so you can validate the schema and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full export of university admission statistics or a continuous feed of test prep content, we build and operate the pipeline. Tell us what you need.
