- 16 reviews
- 13 completed
Newly made videos for this installment of the course show a real improvement over the videos from the other course I took with him. Although the subject really demands more study, this time it seems the videos won't be a problem. Thank you a real lot. (UPDATED: new videos are available only for the first week and part of the second. Next installment's students will have it better.)
This is unfortunately the last module with Roger Peng in Coursera's Data Science Specialization track. I like the complexity of this one and how it builds upon previous courses. I also like the fact that the course has two practical assignments, even if they are in subsequent weeks and time has been short. But good course to get the gist on how to publish live data.
UPDATE 3: Improvement was minimal, but at least this time I finished. Hope the third installment gets better. The course is not hard; what happens is that the presentation is really confused. Lots and lots to improve there. UPDATE 2: Seems like they got the message and next edition will have reformulated material. I sincerely hope the instructor works a lot on improving the videos. It's an important class in the track, and he is also responsible for another very important mathematical class. From this month on, I'm taking one course a month instead of three. UPDATE: I dropped out of the course. The videos are too long and confusing for the limited time I have. Next month I'll try again, but this time I'll take just this course, not another two in the specialization. I'd rather get a better structured statistics course, but this one is part of the specialization track I decided to complete. I seriously hope next installment will improve. Here goes what I wrote one week or so ago: The good: Caffo knows what he's talking about. And this course even instigated me to go study more calculus fundaments. The bad: the instructor is quite confused, going back and forth in the screen. His presential class might be better to follow. Videos are long, sometimes with interesting but ultimately arcane discussions on how no one knows why square brackets are used instead of parenthesis in formal math notation. All this, plus the changes in the form of the quizzes midway through the second week, makes the course more confusing for students who, like me, are subject-matter specialists without stronger math foundations. On the forums, students with stronger math foundations find the course superficial; students with really poor math foundations have been describing it as "hardcore math". The truth lies in the middle of the way: the concepts are intelligible enough, just the way they are presented gets easily confusing - especially in days when my time is limited and I just want to advance a video or two before I rush to work. The ugly: I have to admit that at a certain point I stopped trying to watch the videos and took them as an indication of where to look for the concepts in the book "OpenIntro Statistics", where they are more concisely and directly explained. I miss some more deft use of R also in the classes. When Caffo uses it to explain some point (very sparsely and surprisingly), it's usually in a confusing way also. He could definitely use some presentation tips from his colleague Roger Peng.
I dropped out of the first edition of this course to retake in the second, which I finished. It's not that the course is particularly difficult in content, but there's a lot of useful content crammed into a small amount of lessons. And then comes the programming assignment, which had confusing instructions - and was worth 40% of the credits, which sounded like too much for me. Life intervened and I didn't have enough time to complete it. Next month I'll dedicate more time to it, but I do wish instructions were more accurate. It was the third time I postpones the conclusion of a course by professor Leek. First two times were in his "Data Analysis" course. Again, excellent content, really useful, but lots of useful things crammed into a tight schedule. Too much to chew on with little time. When you get two months to absorb, though, it's really gratifying.
The first two weeks deepen part of what mr.Peng used in his "Computing for Data Analysis" course. He's a great explainer. If you follow the videos up with doing step-by-step the shown code, you'll get the whole basis to do it on your own. Initially, I got a little worried about having two course projects. They are challenging enough, though - they don't require more than you should know by the end of the first week, in the first project, and by the end of the second week, in the second. (In other courses, with other professors, the project was on the third week and you had to use concepts you'd only have on that week.) I didn't give it five stars yet because I'm yet to see how mr.Peng explains clustering. I've seen it with a more confused professor before and that was the point I dropped the course. Watch this space.
Too easy, just videos. It could be a good article or even a great book - I've read some on the subject. Nice theme, though.
This was the first MOOC I took, when it was not self-paced. Professor Widom is great, and the way it was organized showed me it was possible to take an online course with a real sense of difficulty. You took the programming challenges and they either worked or not. You could not fumble your way.
This was a good course, but too concentrated. I like the way they extended it in the specialization track.
Professor Kretschmer is quite a showman in making complex concepts clear. I had read about product differentiation strategies in Hal Varian's "Information Rules" (which is, actually, in the recommended bibliography). It's amazing how professor Kretchmer can summarize whole chapters of information in short and entertaining videos. In an end-of-course assessment I did of a poorly presented class at Coursera, I pointed Kretschmer's videos as good examples of how videos could be paced. Even if his showmanship is not for every professor (gladly; it would sound fake if it was mandatory), many can learn a lot from how carefully Kretschmer scripts his videos. Even his pauses seem to be planned.
I work with this, but the professors managed to point us to interesting reading on the economic framework to analyze what happens in media markets. Quick and invaluable.
Great course. Just dropped because it begun in busy months. The videos are very well edited, with synthetic and authoritative content.
Nice course. Good pacing, and great explanations of quite sophisticated concepts in applied game theory. The stunning production of the videos helped Tobias drive his points home. Quizzes were challenging in the exact measure.
I like the course. Not for it being specially challenging - it wasn't - , but for giving us food and room for thought. Hank is a great explainer. Should he offer an advanced version of the course, I'll be there.
Took it twice, finished it once. Good course, made me begin to plan on dropping Excel altogether. Since I took it from the first installment, it's nice to see how much Peng developed as an online instructor. His early videos were nearly shy. Now, on "R Programming", he seems as comfortable as if he was on a classroom.
It's an introductory course, not challenging at all. I completed it in one week - unsurprising, for it comprises little more than the first week of "Data Analysis", the course Leek gave before.
I like Peng's didactics. He knows how to pace content. This course is an improvement in relation to Computing for Data Analysis (which I took twice, one of them successfully). Assignments are challenging, but feasible.