Mining Massive Datasets
- Free to audit
- Paid certificate
- Advanced
- 7 weeks
Mining Massive Datasets is the StanfordOnline edX course taught by Jure Leskovec, Anand Rajaraman and Jeff Ullman, based on their textbook of the same name, which the page says can be downloaded free by arrangement with the publisher; the content closely matches Stanford's CS246. The major topics are MapReduce systems and algorithms, locality-sensitive hashing, algorithms for data streams, PageRank and web-link analysis, frequent-itemset analysis, clustering, computational advertising, recommendation systems, social-network graphs, dimensionality reduction and machine-learning algorithms. The page states that the course is intended for graduate students and advanced undergraduates in computer science and expects prior courses in data structures, algorithms, database systems, linear algebra, multivariable calculus and statistics; its FAQ says workload varies but ten hours a week is a good guess.
edX lists it as self-paced, advanced level and seven weeks at five to ten hours a week. Auditing is free with time-limited access and no graded work or certificate; the verified track with an edX certificate costs $149.
What you’ll learn
- Design MapReduce systems and algorithms for very large data
- Find similar items with locality-sensitive hashing and process data streams
- Compute PageRank and analyse web-link and social-network graphs
- Apply frequent-itemset analysis, clustering and dimensionality reduction
- Build recommendation systems and understand computational advertising
- Use machine-learning algorithms at scale
Who it’s for
Computer-science students and engineers with algorithms, databases and linear algebra behind them who want large-scale data mining methods.
Instructors
- Jeffrey D. Ullman Professor of Engineering, Emeritus
- Jure Leskovec Associate Professor of Computer Science
- Anand Rajaraman Instructor
Course details
- Skills covered
- PageRank
- Machine Learning
- MapReduce
- Dimensionality Reduction
- Text Mining
- Link Analysis
- Algorithms
- Prerequisites
- The course is intended for graduate students and advanced undergraduates in Computer Science. At a minimum, you should have had courses in Data structures, Algorithms, Database systems, Linear algebra, Multivariable calculus, and Statistics.
- Subtitles
- 1 language
On edx.org
15,851 enrolled, as shown on the course page on 20 Oct 2026. These are edx.org’s own figures, not ours.
Source: Stanford Online (opens in a new tab) · Verified · Report a change
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