We extract course structures, video transcripts, assignment metadata, and faculty intelligence from NPTEL. 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 Catalogue objects from nptel.ac.in. All fields typed and schema-versioned.
"course_id": "noc23_cs14", "title": "Introduction to Machine Learning", "discipline": "Computer Science", "institute": "IIT Madras", "duration_weeks": 12, "enrollment_count": 45021
| # | course_id | title | discipline | institute | instructors | duration_weeks |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Video Lectures objects from nptel.ac.in. All fields typed and schema-versioned.
"lecture_id": "lec_01", "course_id": "noc23_cs14", "week_number": 1, "module_name": "Linear Regression", "video_title": "Gradient Descent", "duration_seconds": 3402, "transcript_available": true
| # | lecture_id | course_id | week_number | module_name | video_title | youtube_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Faculty Profiles objects from nptel.ac.in. All fields typed and schema-versioned.
"faculty_id": "fac_iitm_042", "name": "Prof. Balaraman Ravindran", "department": "Computer Science", "institute": "IIT Madras", "courses_taught": "['noc23_cs14', 'noc22_cs11']", "research_interests": "Reinforcement Learning"
| # | faculty_id | name | designation | department | institute | bio |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Syllabus & Modules objects from nptel.ac.in. All fields typed and schema-versioned.
"course_id": "noc23_cs14", "week_number": 4, "module_title": "Support Vector Machines", "topics_covered": "['Margin maximization', 'Kernel trick']", "assignment_type": "MCQ", "assignment_deadline": "2023-08-25T23:59:00Z"
| # | course_id | week_number | module_title | topics_covered | reading_materials | assignment_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Transcripts & Notes objects from nptel.ac.in. All fields typed and schema-versioned.
"course_id": "noc23_cs14", "lecture_id": "lec_01", "language": "English", "format": "PDF", "page_count": 14, "download_link": "https://nptel.ac.in/content/syllabus_pdf/106105152.pdf", "file_size_bytes": 1450231
| # | course_id | lecture_id | language | format | text_content | download_link |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our NPTEL scraper navigates the entire academic portal: course catalogues, video modules, faculty directories, and syllabus documents. Built with DOM normalisation and document parsing out of the box.
Title, discipline, institute, duration, and enrollment statistics scraped across all active and archived NOC courses.
Extract YouTube IDs, duration, and module mapping for every video lecture in the NPTEL repository.
Download links and text extraction for English and translated transcripts across all video assets.
Name, department, institute affiliation, and historical course load for thousands of NPTEL instructors.
Week-by-week module breakdown, topic lists, and reading material references parsed into nested JSON.
Exam dates, registration deadlines, certification criteria, and grading weightage captured per course.
Map courses back to coordinating institutes like IIT Madras, IIT Bombay, and IISc Bangalore.
Capture primary and secondary discipline tags, AICTE approval status, and course level indicators.
Run weekly pipelines to detect new course announcements, deadline extensions, and newly uploaded lecture materials.
Brief in. Clean data out.
Provide target disciplines, institutes, or specific course IDs. We design the extraction schema together.
We configure Scrapy crawlers, session management, and parsing logic for nptel.ac.in.
Schema validation, null-rate checks, and sample transcript parsing before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
NPTEL contains decades of legacy course data mixed with modern application structures. Here is how we normalise the chaos.
NPTEL relies heavily on client-side rendering for course structures and video lists. We execute full Playwright sessions to hydrate the DOM, capturing content that static HTTP requests miss entirely.
Older NPTEL courses use entirely different HTML structures than recent NOC courses. Our selectors use multi-layer fallback chains to normalise legacy data into a modern, unified schema.
Transcripts are often locked in PDF format. We integrate OCR and text extraction pipelines to convert static PDF documents into queryable text blobs attached to the lecture metadata.
Video metadata is embedded via YouTube players. We extract native video IDs and parameters directly from the DOM, avoiding restrictive external API quotas.
We maintain a hash index of last-seen values per course. Subsequent runs only push diffs, reducing compute cost and storage bloat for large historical catalogues.
Platforms index NPTEL courses to build comprehensive technical education search engines and recommendation systems.
Researchers analyse syllabus evolution, topic frequency, and faculty output across premier Indian engineering institutes.
L&D teams map NPTEL modules to internal competency frameworks for structured employee upskilling programs.
AI teams ingest structured transcripts and technical notes to fine-tune domain-specific language models.
Counselling services track enrollment trends and certification criteria to guide student course selection.
Private universities monitor NPTEL syllabus depth and structure to benchmark their own engineering curricula.
"NPTEL represents the largest open repository of engineering curricula in India. Structuring this syllabus data powers the next generation of technical education tools."
Most teams underestimate the complexity of academic data extraction. NPTEL courses span multiple decades, resulting in fragmented HTML structures, embedded PDFs, and inconsistent metadata. DataFlirt normalises this chaos into a clean, queryable schema so your engineers can focus on building products, not parsing legacy DOM trees.
Everything supported by our nptel.ac.in 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 the NPTEL web application.
Custom middleware handles PDF downloading, text extraction, and OCR to convert static course notes into structured text fields.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. State stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About nptel.ac.in scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available course catalogues and metadata from NPTEL is generally permissible for non-commercial or fair use. We only extract public data and do not bypass authentication walls. Clients should review NPTEL terms of service for specific use cases.
Yes. We scrape both active NOC courses and the historical archive of legacy NPTEL video courses, normalising the data into a single schema.
No. We extract the metadata, YouTube video IDs, duration, and module mapping. Downloading raw video files at scale violates YouTube terms of service.
Our pipeline downloads the PDF files and runs them through a text extraction layer, delivering the transcript content as clean text strings alongside the metadata.
We typically run NPTEL pipelines on a weekly cadence to capture new course announcements, syllabus updates, and deadline extensions.
Yes. Every course record includes the coordinating institute, such as IIT Madras or IISc Bangalore, allowing you to filter by origin.
Our selectors use multi-layer fallback chains. If a layout change breaks the primary selector, our monitoring alerts us immediately and we deploy a fix within hours.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off course catalogue dump or a continuous syllabus monitoring feed across all disciplines. Tell us what you need.
