We extract course metadata, instructor profiles, skill tags, syllabus structures, and learner reviews from LinkedIn Learning. 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 Metadata objects from linkedinlearning.com. All fields typed and schema-versioned.
"course_id": "urn:li:learningCourse:123456", "title": "Python for Data Science Essential Training", "level": "Intermediate", "duration_minutes": 145, "rating": 4.8, "viewer_count": 142050, "ceu_eligible": true
| # | course_id | title | url | description | level | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Instructor Data objects from linkedinlearning.com. All fields typed and schema-versioned.
"instructor_id": "urn:li:instructor:7890", "name": "Lillian Pierson", "headline": "Data Strategist and CEO", "total_courses": 14, "total_learners": 250431, "top_skills": "['Data Science', 'Python']"
| # | instructor_id | name | headline | profile_url | total_courses | total_learners |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Syllabus & Modules objects from linkedinlearning.com. All fields typed and schema-versioned.
"course_id": "urn:li:learningCourse:123456", "module_title": "1. Data Structures", "module_order": 1, "video_count": 5, "module_duration": 22, "quiz_count": 1, "exercise_files_included": true
| # | course_id | module_title | module_order | video_count | module_duration | chapters |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Skills & Taxonomy objects from linkedinlearning.com. All fields typed and schema-versioned.
"course_id": "urn:li:learningCourse:123456", "primary_skill": "Data Analysis", "secondary_skills": "['Machine Learning', 'Statistics']", "software_tools": "['Python', 'Jupyter', 'Pandas']", "category": "Technology", "sub_category": "Data Science"
| # | course_id | primary_skill | secondary_skills | software_tools | industry_tags | role_tags |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Learning Paths objects from linkedinlearning.com. All fields typed and schema-versioned.
"path_id": "urn:li:learningPath:4567", "path_title": "Become a Data Scientist", "total_duration": 1420, "course_count": 12, "difficulty_level": "Advanced", "courses_included": "['urn:li:learningCourse:123456', 'urn:li:learningCourse:654321']"
| # | path_id | path_title | description | total_duration | course_count | courses_included |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our LinkedIn Learning scraper handles every layer of the platform: course catalogues, instructor profiles, skill taxonomy mapping, and syllabus extraction. We manage the session handling and anti-bot circumvention.
Title, description, level, duration, release date, and view counts. Scraped at the course level with full taxonomy mapping.
Capture instructor names, headlines, total learners, course counts, and biographies directly from their author pages.
Extract primary skills, secondary tags, software tools, and role alignments associated with every course and learning path.
Module titles, video lengths, chapter counts, and quiz presence. We map the entire learning structure for structural analysis.
Extract curated learning paths, including total duration, difficulty levels, and the exact sequence of required courses.
Capture aggregate star ratings and total viewer counts to measure course popularity and learner engagement over time.
Identify courses eligible for Continuing Education Units (CEU), academic credits, and professional certification preparation.
Run one-off bulk exports or configure continuous pipelines to track new course releases and updated content weekly.
Extract course availability and translated metadata across different language locales supported by the platform.
Brief in. Clean data out.
Provide category URLs, instructor profiles, or keyword sets. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and CAPTCHA handling for linkedinlearning.com.
Schema validation, null-rate checks, and taxonomy verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
LinkedIn invests heavily in scraping detection. Here is how we stay resilient and why teams choose managed infrastructure over DIY.
LinkedIn's bot detection operates on TLS fingerprints, browser headers, and IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management.
LinkedIn Learning relies heavily on internal Voyager APIs for dynamic content. We intercept and replicate these API calls directly, bypassing slow DOM rendering for faster, structured data extraction.
LinkedIn changes its DOM and API payload structures frequently. Our selector strategy uses multiple fallback chains per field so a layout change does not break your data pipeline overnight.
For large course catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost, storage bloat, and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, taxonomy drift, and coverage drops. SLA uptime is contractual.
Online learning platforms monitor course catalogues, instructor recruitment, and syllabus structures to benchmark their own offerings.
Human resources teams map external training modules against internal competency frameworks to build custom learning paths.
Analysts track the emergence of new software tools and skills in course metadata to forecast industry skill requirements.
Content acquisition teams identify top-performing instructors based on viewer counts, ratings, and course output.
Machine learning teams use structured course and skill relationships to train natural language models for HR tech applications.
Investors track category growth, course release velocity, and viewer engagement to evaluate the corporate training market.
"LinkedIn Learning holds the definitive taxonomy of professional skills and corporate training trends. Extracting that schema requires bypassing aggressive anti-bot layers."
Most teams underestimate the investment required. Reliable LinkedIn scraping requires residential proxies, full JavaScript rendering, CAPTCHA handling, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our linkedinlearning.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. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About linkedinlearning.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law, reinforced by the hiQ v. LinkedIn ruling. DataFlirt targets only public, non-authenticated course and instructor metadata. We do not extract personal user data or circumvent authentication walls for video downloads. Clients should review platform terms and consult legal counsel.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate spikes in real time and trigger pool rotation automatically.
We can extract transcripts only if they are exposed on the public, unauthenticated version of the course page. Gated content hidden behind a premium subscription wall is not supported.
Yes. We extract curated learning paths, including their metadata, total duration, and the ordered sequence of course IDs required to complete the path.
Full catalogue refreshes at a weekly cadence complete within a 12-24 hour window depending on scale. Delta runs for new course releases can be scheduled daily.
Our smallest packages start at a defined category list or 5,000 courses with monthly delivery. For full catalogue extraction, we price based on volume and delivery frequency.
Yes. We provide a sample run of up to 100 courses as part of the pre-engagement scoping process so you can validate schema fit and data quality before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off course catalogue dump or a continuous skill-monitoring feed across thousands of learning paths, we scope, build, and operate the pipeline. Tell us what you need.
