We extract course catalogues, module-level syllabi, university credentials, pricing tiers, and placement metrics from upGrad. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
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 upgrad.com. All fields typed and schema-versioned.
"course_id": "UG-MBA-LJM", "title": "Master of Business Administration (MBA)", "category": "Management", "university_partner": "Liverpool Business School", "duration_months": 18, "learning_format": "Online", "accreditation_type": "WES Recognised"
| # | course_id | title | category | university_partner | duration_months | learning_format |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Syllabus & Modules objects from upgrad.com. All fields typed and schema-versioned.
"course_id": "UG-DS-IIITB", "module_title": "Predictive Analytics and Machine Learning", "duration_weeks": 6, "topics_covered": "['Linear Regression', 'Logistic Regression', 'Decision Trees']", "skills_acquired": "['Statistical Modelling', 'Python Programming']", "tools_taught": "['Python', 'Scikit-Learn', 'Pandas']", "assessment_type": "Case Study"
| # | course_id | module_id | module_title | duration_weeks | topics_covered | skills_acquired |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Financing objects from upgrad.com. All fields typed and schema-versioned.
"course_id": "UG-MBA-LJM", "base_price": 450000.0, "currency": "INR", "emi_available": true, "emi_starting_at": 12500.0, "emi_duration_months": 36, "scholarships_available": true, "application_fee": 2000.0
| # | course_id | base_price | currency | emi_available | emi_starting_at | emi_duration_months |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Placement Outcomes objects from upgrad.com. All fields typed and schema-versioned.
"course_id": "UG-DS-IIITB", "highest_ctc": "73 LPA", "average_ctc_hike_pct": 57, "placement_rate_pct": 85, "hiring_partners": "['Amazon', 'Microsoft', 'Fractal', 'MuSigma']", "top_transition_roles": "['Data Scientist', 'Machine Learning Engineer']", "career_support_type": "Dedicated Career Coach"
| # | course_id | highest_ctc | average_ctc_hike_pct | placement_rate_pct | hiring_partners | alumni_transitions |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Faculty & Mentors objects from upgrad.com. All fields typed and schema-versioned.
"course_id": "UG-MBA-LJM", "name": "Dr. Sarah Jones", "designation": "Professor of Marketing", "organization": "Liverpool Business School", "bio": "20+ years of experience in digital marketing strategy and consumer behaviour.", "image_url": "https://upgrad.com/images/faculty/sarah_jones.jpg", "linkedin_url": "https://linkedin.com/in/sarahjones"
| # | course_id | faculty_id | name | designation | organization | bio |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our upGrad scraper navigates complex React applications to extract deeply nested curriculum data, pricing matrices, and university partnership details with perfect structural fidelity.
Extract every active programme, bootcamp, and degree offering across all categories including Data Science, Management, and Technology.
Capture module titles, weekly topics, required tools, and project assignments nested inside accordion components.
Map each programme to its accrediting university, capturing rankings, alumni status, and certification details.
Extract base tuition fees, zero-cost EMI tiers, application fees, and regional pricing variations across markets.
Capture highest CTCs, average salary hikes, transition roles, and lists of hiring partners associated with specific cohorts.
Extract instructor names, industry affiliations, academic backgrounds, and LinkedIn profiles linked to each course.
Track application deadlines, cohort start dates, and seat availability indicators for upcoming intake cycles.
Extract visa support details, campus transfer requirements, and post-study work right information for international tracks.
Monitor changes in curriculum, pricing, or university partnerships over time with delta-only exports.
Brief in. Clean data out.
Provide target categories or specific programme URLs. We design the extraction schema together.
We configure Playwright crawlers, handle Next.js hydration, and manage session states for upgrad.com.
Schema validation, null-rate checks, and nested syllabus structure verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Modern EdTech platforms use dynamic rendering and complex state management. Here is how we extract clean data from upGrad.
upGrad relies heavily on client-side rendering. We run full Playwright browser sessions to wait for React hydration, ensuring dynamic content like pricing calculators and syllabus accordions are fully loaded before extraction.
A short bootcamp page has a different DOM structure than a two-year Master's degree page. Our extraction logic uses adaptive fallback chains to normalise data across entirely different page templates into a single consistent schema.
Course curricula are deeply nested within multiple UI layers. We programmatically expand all UI components to capture the complete hierarchy of modules, weeks, and individual topics without missing hidden text.
upGrad displays different pricing and cohort dates based on IP geolocation. We use residential proxies to simulate requests from specific regions, allowing you to track international pricing parity.
For ongoing monitoring, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Universities and competing EdTech platforms analyse upGrad syllabi to identify skill gaps and design competitive course offerings.
Strategy teams monitor tuition fees, EMI structures, and scholarship availability across different programme categories and regions.
Investors and analysts track the expansion of university partnerships and new category launches to evaluate EdTech market growth.
Corporate training providers identify popular enterprise skills and target companies listed as upGrad hiring partners.
Researchers aggregate CTC hikes and transition roles to evaluate the actual ROI of online degrees versus traditional education.
Academic institutions identify top-rated industry mentors and adjunct faculty teaching specialised technology courses.
"upGrad aggregates premium university curricula and placement outcomes, making it the definitive dataset for tracking professional education trends in India."
Extracting educational data requires parsing complex, deeply nested React applications and handling inconsistent page structures across different university partnerships. DataFlirt manages the extraction infrastructure so your product and research teams can focus on curriculum analysis and market intelligence.
Everything supported by our upgrad.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.
Playwright handles Next.js hydration, JavaScript rendering, and complex DOM interactions required to expand hidden syllabus content.
We maintain pools of residential ISP proxies to bypass rate limits and capture geo-specific pricing structures reliably.
Pipelines run on Kubernetes clusters. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About upgrad.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from upGrad, such as course descriptions, public syllabi, and pricing, is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract gated student content, proprietary video lectures, or personal user data.
Our extraction schema uses adaptive fallback chains. If a field is missing in one template, the scraper checks alternative DOM paths. All outputs are normalised into a single consistent JSON structure regardless of the source page layout.
Yes. Our Playwright crawlers programmatically interact with the page to expand all accordion elements, ensuring every module, week, and sub-topic is captured in a nested JSON array.
Yes. We can run pipelines on a scheduled cadence and use hash-based diffing to alert you only when a course price, EMI structure, or application deadline changes.
No. We only extract publicly available marketing and curriculum data. We do not bypass authentication walls to access the learning management system (LMS) or proprietary course materials.
Our minimum engagement typically covers a full extraction of the public course catalogue delivered weekly or monthly. Contact us with your specific requirements for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off curriculum dump or continuous pricing intelligence across all programmes, we scope, build, and operate the pipeline. Tell us what you need.
