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I'm currently pursuing this course on Coursera. We are currently only halfway through the course but I think I can share a few thoughts on the course. Pros -

I'm currently pursuing this course on Coursera. We are currently only halfway through the course but I think I can share a few thoughts on the course. Pros - Faculty: Like most MOOCs, MMDS is taught by one of the best faculty from the field. I've been an avid follower of Anand Rajaraman's blog [0] before I joined this course and I have to say the enthusiasm of the faculty is infectious and their expertise with the material is markedly evident. - Difficulty: MMDS is a CS graduate level course (CS246) from Stanford. That means the topics are not trivial, the lectures are dense and you as a student are expected to invest significant time into understanding the material. Since this is hard, grasping the concepts and getting the quiz right is quite gratifying. Few lectures from every week are tagged as advanced and students who view and answer all advanced questions get a certificate of distinction [ not quite relevant but might provide the necessary incentive / motivation to a few students] - Material: The syllabus and the topics covered in this blog are extremely relevant for any one aspiring to work in the data mining / machine learning field. Having done Andrew Ng's ML course, this course acts a perfect supplement and covers a lot of practical aspects of implementing the algorithms when applied to massive data sets. For example, a recent lecture talked about how the BFR algorithm[1] for finding clusters works better than k-means for a very large dataset. - Book: The accompanying MMDS book is just awesome and the lectures build upon the content and examples from it. For someone who finds the book a bit too challenging (probably because your math is a bit rusty) the lectures make the material quite approachable. Cons - Theoretical: The course is primarily theoretical in both its presentation and exercises. This is not to say that algorithms are presented without examples, but that the examples are trivial and do not illustrate the issues with implementing or applying various algorithms in real-life datasets. - Programming Assignments: In sharp contrast to Andrew Ng's course, there are no compulsory programming assignments. The exercises are all quizzes which check how well you have understood the concepts. There is just one programming assignment which is also optional. Overall, I'm really liking the course. The professors emphasize citing industry examples wherever necessary (the PageRank algorithm and accompanying Google's implementation was covered for 3 lectures), which is a welcome change from other CS courses. Along with the book, I believe the course is a wonderful primer to the field of Data Mining. [0] - http://anand.typepad.com/datawocky/ http://anand.typepad.com/datawocky/ [1] - http://www.dmi.unict.it/~apulvirenti/agd/BFR98.pdf http://www.dmi.unict.it/~apulvirenti/agd/BFR98.pdf

radiowave 12y ago
Agreed. I'm taking the course at the moment, and enjoying it quite a bit, but for me it is rather too dense - I could do with it having been spread over twice as many weeks, so I probably won't complete it in time, but that's no big deal.
garyrob 12y ago
Thanks for your review. One question. You say: "Difficulty: MMDS is a CS graduate level course (CS246) from Stanford." But the post itself says: "The book, like the course, is designed at the undergraduate computer science level with no formal prerequisites. " Any thoughts about the discrepancy?
- krat0sprakhar 12y ago
  In my opinion, MOOCs tend to underplay pre-requisites. The content in the initial classes lead to a bit of furore amongst the students on exactly the same topic. Evidently, a lot of students found the notation and mathematics (e.g. Algebra, matrices, eigenvalues, calculus) very hard to understand. In response to this, one of the faculty - Jeff Ullman categorically stated in the forums that this course is taught to graduates in Stanford (CS 2xx is grad level) and an undergraduate course in mathematics is pre-requisite. Although most of the mathematics covered in the course is covered in a typical undergrad class, IMO the overall content is quite advanced and graduate class worthy. That being said, the forums (and the book) are quite helpful, and given you put in enough time, you will sail through. Hope this answers your question!

radiowave 12y ago

Agreed. I'm taking the course at the moment, and enjoying it quite a bit, but for me it is rather too dense - I could do with it having been spread over twice as many weeks, so I probably won't complete it in time, but that's no big deal.

garyrob 12y ago

Thanks for your review. One question. You say: "Difficulty: MMDS is a CS graduate level course (CS246) from Stanford." But the post itself says: "The book, like the course, is designed at the undergraduate computer science level with no formal prerequisites. " Any thoughts about the discrepancy?

krat0sprakhar 12y ago
In my opinion, MOOCs tend to underplay pre-requisites. The content in the initial classes lead to a bit of furore amongst the students on exactly the same topic. Evidently, a lot of students found the notation and mathematics (e.g. Algebra, matrices, eigenvalues, calculus) very hard to understand. In response to this, one of the faculty - Jeff Ullman categorically stated in the forums that this course is taught to graduates in Stanford (CS 2xx is grad level) and an undergraduate course in mathematics is pre-requisite. Although most of the mathematics covered in the course is covered in a typical undergrad class, IMO the overall content is quite advanced and graduate class worthy. That being said, the forums (and the book) are quite helpful, and given you put in enough time, you will sail through. Hope this answers your question!