Udemy Scraper: Course Prices, Ratings & Students in 2026

Every course page is a market report

A Udemy course page says more than its sales pitch. The price shows what a skill sells for, the student count shows how many people bought it, the rating and review count show whether they were satisfied, and the last update date shows whether the instructor still maintains it. Read across a whole topic, those numbers reveal which skills are crowded, which are underserved, and who leads each subject.

Udemy’s interface is built for choosing one course, not for comparing a hundred. Structured extraction closes that gap.

What you can extract

40+ fields per course:

Basics: title, headline, full description, URL
Pricing: price, amount, currency, paid or free
Social proof: average rating, review count, student count
Content: lectures, quizzes, total and video hours, instructional level, full curriculum
Instructor: name, title, profile URL and image
Classification: category, subcategory, topic, locale
Badges: bestseller, highly rated, new, certificate, closed captions
Learning design: objectives, prerequisites, requirements, target audiences, caption languages
Dates: published, last updated, created

The technical challenge

Udemy is easy to browse and awkward to collect from:

Choosing the entry point: category pages such as /courses/development/ load their courses dynamically, while topic pages such as /topic/python/ carry pre-rendered course data. Pick the wrong one and you end up rendering every page in a headless browser for data that was available more cheaply elsewhere.
Depth per course: a listing card shows a title, a price and a rating. Objectives, requirements and the curriculum live on the course page itself, so a complete record means reading every course page in full.
Resilience: at volume, some requests fail. Proxies, retries and a run that moves on to the next page instead of crashing are what keep a dataset complete.

DIY or a ready-made Actor?

Our Udemy Scraper on Apify takes keywords, which it converts to topic pages, or direct topic and course URLs, with a maxItems cap on the total. Every course comes back complete, description, objectives, requirements and curriculum included, with nothing to switch on. It reads about one course every 3.5 seconds, proxies are included, and failed pages are retried automatically. Pay-per-result at about $0.0025 per course.

A practical workflow

List your topics: the skills you teach, sell training for, or hire for; each keyword maps to a Udemy topic page.
Extract the courses: combine several keywords in one run to cover a whole field, and cap the total with maxItems.
Analyze the market: plot price against rating and student count, and flag courses with high enrollment but an old update date. LLM-powered extraction clusters objectives and curricula into the sub-skills each topic actually covers.
Act: scope your next course around the gap, price it inside the band that sells, or build a training shortlist.

Use cases that pay off

Instructor research: benchmark competing courses on length, lecture count, rating and recency before you record.
Edtech market analysis: pricing bands, enrollment and rating distributions, topic by topic.
Content gap analysis: popular topics where the leading courses are outdated or poorly rated.
Training curation: top-rated, recently updated courses with certificates for upskilling programs.
Localization planning: caption languages and locale show which courses already reach which audiences.

The legal boundaries

Public catalog data: course pages are public listings; the scraper collects what Udemy shows any visitor.
Instructors are people: names, titles and profile images become personal data under GDPR once stored; keep only what your analysis needs.
Analyze, don’t republish: descriptions and curricula are the instructors’ work. Aggregate insights are defensible; copying course content is not.