Mining Massive Datasets

Provided by:
8/10 stars
based on  7 reviews
Provided by:
Cost FREE
Start Date TBA

Course Details

Cost

FREE

Upcoming Schedule

  • TBA

Course Provider

Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4715 reviews

Course Description

This class teaches algorithms for extracting models and other information from very large amounts of data. The emphasis is on techniques that are efficient and that scale well.
Reviews 8/10 stars
7 Reviews for Mining Massive Datasets

Ratings details

  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars

Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.

Sort By
Borys Zibrov profile image
Borys Zibrov profile image
8/10 starsCompleted
  • 9 reviews
  • 8 completed
4 years, 8 months ago
First of all, I didn't see a button to leave a review on course talk, so if you have the same problem as me, try link http://www.coursetalk.com/coursera /mining-massive-datasets/review. I found this course very challenging (about 10 hrs of work a week or perhaps more) and pretty much theoretic. You'll get tons of useful theory, and very little practice. All quizzes and finals are about doing math calculations, and not about coding systems that do the job. Of course, it would be very nice to have both but it would require more time. There was only one (!) programming assignment on LSH, which was absolutely optional. Content is superb. There's a free book available on mmds.org which is very cool. I talked about this course being a theoretic one above, but I've got to say that this theory is extremely useful (should be at least) in practice. Course deals with tough subjects, like what to do if you are implementing a recommender system... First of all, I didn't see a button to leave a review on course talk, so if you have the same problem as me, try link http://www.coursetalk.com/coursera /mining-massive-datasets/review. I found this course very challenging (about 10 hrs of work a week or perhaps more) and pretty much theoretic. You'll get tons of useful theory, and very little practice. All quizzes and finals are about doing math calculations, and not about coding systems that do the job. Of course, it would be very nice to have both but it would require more time. There was only one (!) programming assignment on LSH, which was absolutely optional. Content is superb. There's a free book available on mmds.org which is very cool. I talked about this course being a theoretic one above, but I've got to say that this theory is extremely useful (should be at least) in practice. Course deals with tough subjects, like what to do if you are implementing a recommender system for instance, and have lots and lots of data that won't fit into memory. How to avoid disk thrashing? This IS tricky, isn't it? And things like that. You know what statistics is, for sure, you know (or heard at least) SVMs and NNs and CF and stuff like that, but probably you didn't try to implement it on web scale, did you? :) For absolute beginners in machine learning (experience ~ none), this course might be useful as well (perhaps with more work to do), because brief introductions to the corresponding topics are given. Now, few BUTs. I can't give this course 5 stars, and I was doubtful about 4 stars even, because organization was terrible. Missing pages on coursera, missing links (i.e. to find a way to see exam results you had to guess the right link !!), course structure was not intuitive at all. I don't know why, but they changed the order of the videos completely, with some weird references to the future lectures all the time (only they didn't say it would be in the future lectures). Instructors are great dudes and know stuff they teaching about, but some of them just can't teach. Jeff Ullman is a cool guy, to be sure, but his lectures were particularly dull... how to put it: content was superb, but uninterestedness of the lecturer and droning voice just put me to sleep in no time :) In this regard I love Jure Leskovec the best, he seemed passionate at least. So, great content, organization was terrible, learned so much (but not in depth, because to learn those things in depth you need more than 10 hrs of work a week for 3 months, be sure), and lecturers were so-so.
Was this review helpful? Yes3
 Flag
Rohan Kapur profile image
Rohan Kapur profile image
6/10 starsTaking Now
  • 1 review
  • 0 completed
3 years, 10 months ago
This course is probably one of the best data mining courses on Coursera, but it has a few flaws. Firstly, it's difficult. This is NOT a flaw, but something to note. It's a Stanford graduate level course that, per the instructors, "has not been watered down at all". This results in many people complaining in the discussion forums that the course has grown "legs", and can be quite stressful. Advice to ANYONE taking the course: make sure you have a lot of prior Linear Algebra knowledge. It's much more rigorous than the Linear Algebra used in something like Andrew Ng's Machine Learning course. However, there's also a great book that's published with the course. It's a wonderful reference when you're stuck on something. With that being said, the quizzes are not hard. A lot of the quizzes are normally very short, and do not require much insight into the material. A lot of them are just simple mathematical calculations that are somewh... This course is probably one of the best data mining courses on Coursera, but it has a few flaws. Firstly, it's difficult. This is NOT a flaw, but something to note. It's a Stanford graduate level course that, per the instructors, "has not been watered down at all". This results in many people complaining in the discussion forums that the course has grown "legs", and can be quite stressful. Advice to ANYONE taking the course: make sure you have a lot of prior Linear Algebra knowledge. It's much more rigorous than the Linear Algebra used in something like Andrew Ng's Machine Learning course. However, there's also a great book that's published with the course. It's a wonderful reference when you're stuck on something. With that being said, the quizzes are not hard. A lot of the quizzes are normally very short, and do not require much insight into the material. A lot of them are just simple mathematical calculations that are somewhat in the smaller processes of the algorithms in the lectures. On top of this, there's no programming exercises, which are usually helpful for gaining insight on these topics. I think, however, there are a few set of lectures which are extremely difficult to understand. Week 2 is renown on the discussion forums to be the hardest portion of the course, most specifically because Jeffrey Ullman (who is an amazing Computer Scientist!) is a very monotone, rather boring and un-motivational lecturer (he directly reads off a transcript). He doesn't give many examples and his lectures are VERY long (30 minutes at one time), which leaves things rather abstruse. Sometimes, his train of thought can be near impossible to follow. This is where the MMDS book comes in handy. Luckily, Jure and Anand are great lecturers who make things concise, easy, and fun to understand. The content is GREAT. That's definitely an important thing to note. The topics they explore are rather interesting, and relevant. But the course moves very fast. There's a LOT of content to go through, usually two chapters of the MMDS book per week. The lectures can be quite long, and it makes things rather tiring. I think the biggest problem with this course is the lack of structure. I don't know why I'm learning what I'm learning. I don't know how it even fits back into Data Mining as a whole, or what is Data Mining? I don't know how single sets of lectures relate to the next set (it seems as if they do not at all). Weeks jump from one to another without any coherence. Why or when I would I use this in my startup or project? How do these topics relate together? There's no coherence or underlying motif to the course. The videos within the weeks lack coherence too. There are times when material from past lectures are randomly brought back up, and it seems as if random videos introducing new topics that aren't even explained into depth until WEEKS later are inserted at random points in the course. I've been able to retain very little of this course. I find that, each week, I almost certainly forget the intricacies and sometimes even the bigger pictures of the past week. This reflects on the sheer volume of content and the lack of effective structure of the course. MMDS has been mentally stimulating, but definitely needs an overhaul.
Was this review helpful? Yes1
 Flag
Kristina Šekrst profile image
Kristina Šekrst profile image
10/10 starsCompleted
  • 102 reviews
  • 102 completed
3 years, 10 months ago
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!
Was this review helpful? Yes0
 Flag
Jeff Winchell profile image
Jeff Winchell profile image
8/10 starsTaking Now
  • 91 reviews
  • 66 completed
4 years, 5 months ago
The other reviewers are right. Jeffrey Ullman is a poor lecturer. That is apparent in less than a minute of watching him. I'm trying now to just read the transcript and look at the slides so I don't fall asleep listening to him.
Was this review helpful? Yes0
 Flag
Shekhar Sivaraman profile image
Shekhar Sivaraman profile image
7/10 starsCompleted
  • 2 reviews
  • 2 completed
4 years, 8 months ago
The Cons: (Hint: I am a pessimist) Jeffrey Ullman, comes off as a robotic monotone speaker doling off essays from his book which is also available freely online. His lectures are a drag and do not be surprised if you cannot understand his lectures. There is no doubt however that he is probably the most accomplished of the 3 lecturers on the topic. John Nash (A beautiful mind), likewise was a brilliant mathematician but no one would argue that he was a terrible teacher. Pros: Anand and Jure do a very good job of their lectures though and that serves for the 3.5 stars I have mentioned in this review. The tests are horrible. there were a lot of mistakes in the answers which had to be rectified multiple times. I am sure this will be taken care of in the future. I believe that the Course Quizzes and Assignment material could have been much much better. That would have easily driven up the review by half a star at least.... The Cons: (Hint: I am a pessimist) Jeffrey Ullman, comes off as a robotic monotone speaker doling off essays from his book which is also available freely online. His lectures are a drag and do not be surprised if you cannot understand his lectures. There is no doubt however that he is probably the most accomplished of the 3 lecturers on the topic. John Nash (A beautiful mind), likewise was a brilliant mathematician but no one would argue that he was a terrible teacher. Pros: Anand and Jure do a very good job of their lectures though and that serves for the 3.5 stars I have mentioned in this review. The tests are horrible. there were a lot of mistakes in the answers which had to be rectified multiple times. I am sure this will be taken care of in the future. I believe that the Course Quizzes and Assignment material could have been much much better. That would have easily driven up the review by half a star at least. I would still suggest the course for a theoretical introduction to many of the algorithms in Massive Machine learning.
Was this review helpful? Yes1
 Flag
M Cloney profile image
M Cloney profile image
4/10 starsCompleted
  • 3 reviews
  • 2 completed
4 years, 8 months ago
I was really expecting to learn more practical information and less theory. At least in the basic track, the lectures were occasionally interesting, but the sheer volume of the material and the differing styles of the lecturers made it hard to really tie everything together effectively. Having said that, anyone who managed to get the certificate with distinction has my respect. Everyone's entitled to their own opinion, but with a thread on the course forums entitled "Week 2 Lecture Is Really Bad, What Can We Do About It?" with a NET of 102 up-votes at the time of this writing, I know I'm not the only one who felt this class needed a major overhaul.
Was this review helpful? Yes1
 Flag
Richard Taylor profile image
Richard Taylor profile image
10/10 starsCompleted
  • 29 reviews
  • 28 completed
4 years, 8 months ago
This course is a real gem. The best about it is the content and how it is presented, the topics are modern, the instructors know the last stuff about the topics and the lectures are really excellent. Then there's the usual set of weekly quizzes and a final exam. I didn't found any organizational quirks or problems in how the topics were presented. I'm really happy content such as this is made available to the general public online the value is incredible. Highly recommended to anyone interested in modern algorithms and topics about large datasets.
Was this review helpful? Yes0
 Flag

Rating Details


  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars

Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.