We extract course catalogues, certification requirements, pricing tiers, and schedule availability from Kaplan. 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 Meta objects from kaplan.com. All fields typed and schema-versioned.
"course_id": "KAP-CFA-L1", "title": "CFA Level I Prep Course", "category": "Financial Services", "sub_category": "CFA", "format": "Live Online", "duration_hours": 120, "certification_body": "CFA Institute"
| # | course_id | title | category | sub_category | format | duration_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Plans objects from kaplan.com. All fields typed and schema-versioned.
"course_id": "KAP-CFA-L1", "base_price": 999.0, "currency": "USD", "discount_price": 849.0, "installment_available": true, "installment_terms": "3 payments of $283", "pass_guarantee_eligible": true
| # | course_id | base_price | currency | discount_price | installment_available | installment_terms |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Schedules & Cohorts objects from kaplan.com. All fields typed and schema-versioned.
"course_id": "KAP-CFA-L1", "cohort_id": "CFA-L1-2024-Q3", "start_date": "2024-07-15", "end_date": "2024-11-20", "delivery_format": "Live Online", "timezone": "EST", "enrollment_deadline": "2024-07-01"
| # | course_id | cohort_id | start_date | end_date | delivery_format | timezone |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Syllabus Details objects from kaplan.com. All fields typed and schema-versioned.
"course_id": "KAP-CFA-L1", "module_number": 1, "module_title": "Quantitative Methods", "module_description": "Time value of money, probability, and statistical concepts.", "hours_required": 15, "assessment_type": "Multiple Choice Quiz"
| # | course_id | module_number | module_title | module_description | learning_outcomes | hours_required |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Instructor Profiles objects from kaplan.com. All fields typed and schema-versioned.
"instructor_id": "INST-8492", "name": "Sarah Jenkins", "title": "Senior CFA Instructor", "courses_taught": "['CFA Level I', 'CFA Level II']", "credentials": "['CFA', 'MBA']", "review_count": 342
| # | instructor_id | name | title | biography | courses_taught | rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Kaplan scraper parses nested course taxonomies, dynamic pricing based on location, and complex schedule widgets. We handle the JavaScript rendering and regional routing automatically.
Extract titles, descriptions, categories, and certification bodies across all Kaplan verticals: test prep, professional, and language programmes.
Capture base prices, discounts, and currency variations across different regions using geo-targeted proxies.
Extract start dates, end dates, timezones, and delivery formats for live online and in-person cohorts.
Parse module titles, learning outcomes, and required hours for detailed curriculum mapping.
Extract instructor biographies, qualifications, and assigned courses to build faculty databases.
Capture student ratings, text reviews, and success stories associated with specific courses.
Map prerequisite courses and progression pathways for multi-level certifications like CFA or ACCA.
Scrape kaplan.com, kaplan.co.uk, and regional subdomains using a unified extraction schema.
Run daily or weekly pipelines to detect new course launches, price changes, or schedule updates.
Brief in. Clean data out.
Provide target categories, certification bodies, or regional domains. We design the extraction schema together.
We configure Playwright crawlers, regional proxy routing, and JavaScript interaction flows for Kaplan's dynamic widgets.
Schema validation, null-rate checks, and schedule format normalisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Kaplan relies on complex front-end frameworks and regional routing. Here is how we extract clean data reliably.
Kaplan displays different courses and pricing based on the visitor's location. We use residential proxies mapped to your target markets to ensure accurate price capture.
Course schedules and cohort availability are loaded dynamically via complex JavaScript widgets. We run full Playwright browser sessions to hydrate these components before extraction.
Kaplan's course hierarchy spans multiple levels. Our crawlers recursively traverse this taxonomy to maintain accurate category and sub-category mapping for every course.
We maintain a hash index of last-seen values for prices and schedules. Subsequent runs only push diffs, reducing downstream processing load.
Every run emits structured logs. We alert on schema drift, missing price fields, and coverage drops, responding before your downstream systems are affected.
Education providers monitor Kaplan's course catalogue, pricing, and new programme launches to benchmark their own offerings.
HR and L&D teams aggregate schedule and pricing data to optimise corporate training budgets and cohort enrollments.
Analysts track regional course availability and pricing variations to identify underserved markets for professional education.
Test prep companies track Kaplan's discount cycles, installment terms, and base pricing to adjust their own revenue models.
Recruitment platforms use syllabus and prerequisite data to map skills required for specific professional certifications.
ML teams use structured syllabus and learning outcome data to train educational recommendation models.
"Kaplan holds the blueprint for global professional certification and test prep, but aggregating that curriculum data requires infrastructure, not just a script."
Extracting schedule availability and location-based pricing from Kaplan requires handling complex JavaScript payloads, session state, and regional proxy routing. DataFlirt manages this pipeline end-to-end so your data team receives structured updates without maintaining custom crawlers.
Everything supported by our kaplan.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 retry logic. Playwright handles JavaScript rendering and interaction flows for complex schedule widgets.
We maintain pools of residential ISP proxies across multiple regions to ensure accurate capture of location-based pricing and course availability.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About kaplan.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available course catalogues, pricing, and schedules is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract personal student data, circumvent authentication walls to access paid test prep materials, or violate GDPR. Clients should review Kaplan's ToS and consult legal counsel for specific use cases.
We route requests through residential proxies located in your target regions. This ensures the pricing, currency, and course availability returned by Kaplan's servers matches what a local user would see.
Yes. Kaplan often loads cohort dates and availability via JavaScript after the initial page load. Our Playwright integration waits for these network requests to complete and extracts the fully rendered schedule data.
For standard course catalogues, we typically run weekly or bi-weekly refreshes. If you are monitoring specific high-value courses for price drops or schedule changes, we can configure daily pipeline runs.
Yes. We extract public instructor biographies, credentials, and the lists of courses they teach, which is useful for building faculty intelligence databases.
No. DataFlirt does not scrape content behind authentication walls. We only extract publicly accessible marketing, pricing, and syllabus information.
Our packages start at a defined category list or regional domain with weekly delivery. Contact us with your specific data requirements for a scoped quote.
Yes. We provide a sample run of up to 100 courses as part of the pre-engagement scoping process 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 catalogue dump or continuous price monitoring across multiple regions, we scope, build, and operate the pipeline. Tell us what you need.
