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Stanford University

Mining Massive Datasets

Stanford University via edX


The course is based on the text Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman, who by coincidence are also the instructors for the course.

The book is published by Cambridge Univ. Press, but by arrangement with the publisher, you can download a free copy Here. The material in this on-line course closely matches the content of the Stanford course CS246.

The major topics covered include: 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.

Taught by

Jure Leskovec, Anand Rajaraman, Jeff Ullman and


4.5 rating, based on 24 Class Central reviews

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  • Anonymous

    Anonymous completed this course.

    This is a course with interesting content but that is somewhat lacking in pedagogy.
    The course has a lot of good content, notably from J.Ullman, but course sessions are very long, pedagogy is not optimal.
    The course is a huge time investment with dense content all along the 7 weeks or so. If you can get over this it will be very rewarding but not everyone has that kind of time available.
    That course would probably be better off cut in smaller chunks or offered as a self-paced course.

    Also the fact the course doesn't offer verified certificate will make think twice before investing so much time in it.
  • Anonymous

    Anonymous completed this course.

    I found the lecture to be of medium difficulty for the post-grad student and I would expect it to be rather hard for an undergrad. The content is offered in two paces; the lectures of Prof. Ullman are hard to follow, as he browses quickly through m…
  • HChan

    HChan completed this course, spending 10 hours a week on it and found the course difficulty to be hard.

    Excellent course by the authors, covering the content of the book of the same name It is the MOOC version of Many useful topics in large scale data processing algorithms are cov…
  • Aliaksandr Bely completed this course, spending 7 hours a week on it and found the course difficulty to be hard.

    Very interesting course covers a lot of topics. It is rather difficult and takes a lot of time (only lectures usually take around 3 hours/week and it's hard to watch them faster than 1.25x). The only disappointment for me was lectures taught by prof Ullman, was very hard to fallow his monotonic reading, other two lecturers have strong accents but were much more alive and understandable.
  • Kristina Šekrst completed this course and found the course difficulty to be hard.

    I loved this course, and I'm recommending it to everyone. It's hugely time consuming, however, there are two tracks - the basic one and the advanced one. Since the basic one was tough for me, I'm looking forward to taking this course again, and try out the advanced one, since I had no time for it in the first run of this course. I'm glad to see it had more future runs, it surely deserves it. All the instructors were great, and I loved the way how they explained difficult concepts with various analogies and illustrations. The forum discussions were great, but the course lacks some programming assignments to try to see these approaches in practice, or perhaps to make it a bit longer. Great, great job!
  • You-Cyuan Jhang completed this course, spending 6 hours a week on it and found the course difficulty to be medium.

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