STAT W4240

A series of coding assignments and a final project. The advising on the final projects is not so good, so be sure to choose a topic that you are _sure_ you can actually implement.

6 hw's + project + midterm + final all manageable. One caveat, do not cram the homeworks. They can get tricky since you are implementing them using programming skills not taught in the course.

Homework - We were supposed to have like 11 assignments, but due to how unorganized the course was, we only did 5. They had a theoretical component and a programming component in SAS. Midterm - multiple choice, and all the possible questions were given ahead of time. Final - like the midterm, but longer.

An extremely easy midterm and a more difficult final; 6 problem sets; term project

two exams: there's no point in studying. the questions are totally random and usually had nothing to do with the class material. one project: 60% of the grade. unless you spent days on coding, you'll not get a decent grade from it.

Frank Wood is idiosyncratic to say the least. Check out his homepage and you'll see a portrait of him behind which is a background that makes him look enshrined in light. As previous reviewers have said, Wood spends most classes copying his notes, which are notably nearly _identical_ to those of the text, onto the blackboard. He also derides applications of data mining (he's not training you to become a mining 'technician') though he sometimes admits he doesn't understand the (difficult) math that makes this stuff possible. On the first day of class, he seemed to brag about how much of the class was going to drop his course, as if that constituted some signal as to the quality of the the course. The bottom line here is that you need _serious_ stat background to get all you can out of the course. Given that the math is difficult, getting a sense of the intuition the motivates results is useful and perhaps even necessary, but he doesn't offer this. Developing intuition is paramount in abstract math courses; data mining is no different and Wood ought to see this. Students are then left to develop intuition on their own. But this is difficult because the examples he gives in class are usually the same examples from the text. Indeed, Data Mining, offered to both undergrads and masters (even phd students) is likely a tough course to teach: What can or should Wood assume that you kno…

Chris Volinsky is what you would want out of a professor for a course such as Data Mining. He takes an extremely broad subject matter and distills its component parts without sacrificing technical rigour for the most part. His notes are extremely thorough and credible given his industry background (won Netflix prize couple years back). What this course really excels in though is giving you the toolbox to go off on your data mining tangent when you see fit and create something out of nothing. Just be sure you know some scripting language coming in to operate on the data sets, which get increasingly massive as the semester goes on. These could be any one of R, Matlab, Python, etc it really doesnt matter as the course is not taught via implementation.

Professor Rabinowitz is a very nice professor. However, in class, he is extremely unorganized and he is not the best at explaining concepts. There were many times when he would try to explain a concept again only for the MA students (and handful of undergrads) to stare blankly back at him. Furthermore, his homeworks were very confusing. He would constantly have to update the problems because they were not clear. They were also quite challenging and required going to office hours to understand how to solve the problems (even the TAs did not understand sometimes). Despite these failings, though, Dan really does make an effort. He really cares about the students and holds lots of office hours to help students. During reading week, he held at least three final review sessions, each of which were a few hours long. I think that he doesn't usually teach Data Mining, hence his inexperience. However, if you seek help, Dan will go the extra mile to help you out. It's not likely that Dan will teach Data Mining any time soon again, but in case he does, I suggest taking advantage of office hours, since Dan is very helpful then. Also, there is a huge overlap with Statistical Machine Learning in terms of content.

I loved this class and this professor. He brings a fascinating perspective, combining industry experience (he works for AT&T) and academic training which gives him a lot of cool stories to tell as well as knowledge about current trends in the wider data mining community. He also won the Netflix Prize (google it if you don't know what it is) and he spends a lot of time telling us about that, which was very cool. He's also a big sports fan so get ready for lots of sports examples (though he won't test you on them). He's generally a big data/stats nerd and often brings in cool examples of research of visualizations. He tends to go for breadth rather than depth and generally doesn't get too technical. I liked this approach -- I'd rather get exposed to more subjects and dive into them more on my own if I'm interested -- but I can understand if someone disagrees. He goes through a general introduction to data mining and then discusses: data visualization, cross-validation techniques, regression, classification, clustering, text mining, web mining, neural nets and support vector machines, ensemble methods, bayesian methods, recommender systems, and social networks. I would imagine that some of the later topics could be changed if he teaches the class again. In terms of a programming background, it's really very important to know R (or be willing to spend time learning it). You can al…

Stay away!!! This professor is a terrible teacher, does not like to help to his students even during his office hours (he made me feel as if I am wasting his time and did not answered some of my questions), and is not consistent with his grading. His lectures were waste of time. He used powerpoint slides that have lots of graphs, but he did not explain what the underlying data are or what the axes on a plot are. Also, he spent last half of the semester teaching how to code in Phyton. If I want to learn Phyton, I would have taken a class from the computer science department. Half of the students stopped coming to class after the midterm, because they have realized that the classes are no use and the questions in the exam had nothing to do with what we covered in class. I'll recommend everyone to run away from this professor. It's a pity too; I was really looking forward to taking a good data mining course.