PDF Mining of massive datasets

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Book: Mining of Massive Datasets, 2nd Edition, free download

Sign up. Overview Syllabus Related Courses Reviews. Overview We introduce the participant to modern distributed file systems and MapReduce, including what distinguishes good MapReduce algorithms from good algorithms in general. Tags usa.

[Coursera] Mining Massive Datasets (Stanford University) (mmds) - Academic Torrents

Browse More Data Mining courses. 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.


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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. Was this review helpful to you? 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. Jure on the other hand uses a lot of examples and is easy to follow even from an undergrad.


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Overall it is a time consuming course, expect to need around hours per week. In the end, you do learn quite a few stuff and it is a good lecture to take. I am in favor of the instructors' choice of offering it as it is in Stanford. Something that could help in the course is to split the content in 10 weeks instead of 7 and add mandatory programming exercises. They help a lot in learning stuck and remembering them for a long time. Many useful topics in large scale data processing algorithms are covered including mapreduce, pagerank, networks and graph analysis, streaming algorithms, just to mention a few.

The level is advanced undergrad or postgrad, with some chapters covering topics in research papers published within the last decade. But the material is very useful and rewarding. Exercises are comprehensive and the forums are very useful for checking your understanding.

Lecture 37 — Counting 1 's (Advanced) - Mining of Massive Datasets - Stanford University

Very interesting course covers a lot of topics. 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. People who viewed this item also viewed.

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Project seminar on Mining Massive Datasets

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