- 15 reviews
- 13 completed
Great, course. A well balanced mixture of theory, examples, labs and projects to test your skills in the 'real' world. The teacher explains the concepts very clearly. The course layout and order of topics is excellent. The difficulty is does not change overall. The free and open textbook is the best I've come across. It is packed with footnotes to datasets, and has more than enough examples and exercises to get you through the midterm and the exam. The teacher is very active on the forums and even organized a Google hangout session you can join. The focus of this course is definitely not on mathematical proofs, probability theory or working through problems analytically, but geared towards practical approaches using given formula's or R to get results on every day problems. Integrated into the course is the datacamp environment that helps you to learn the software R by playing with data. The nature of these task mirror the theory discussed that week. There are some glitches in this new environment (during the first run of this course), and the tasks are not really challenging. The quizzes are quite good. Many of the questions test your insight rather than ask you to do tedious algebra. The peer reviewed research projects are time consuming, great fun and an excellent way to get your hands wet with real data, R and doing some real data analysis. One of the best courses I've taken.
Interesting and useful knowledge. Not just for managers, but everyone with an interested in perspectives explained in terms of thinkers, philosophers, writers, poets and scientists. I find quite a few of his ideas original. With that I mean the way he uses well known philosophical concepts to explain or view daily phenomenon. Which include quite a few phenomena from the business world. Every week you are asked to do a peer assessed write-up related to the lectures of the past week. Not just to show you've watched the video's but also to express some of you own knowledge and creativity using the methods just taught.
The best courses I have taken. I have some prior experience but that is very rusty and definitely not as extensive as the topics in this course. The professor has a very clear way of explaining the topics. There is hardly any repetition and a good balance of proving and not proving concepts using math. The best aspect about this course are the exercises. They are a valuable learning tool in themselves. Most exercises use sub answers and the problem often progresses to a more complex problem yielding in depth understanding of the material. Many exercises can only be submitted once or twice which keeps me alert while learning and applying the knowledge. I consider this a good thing, but not everyone agrees on that. The Professor and TA's do an excellent job replying in the exercise forums where needed with the focus on increasing understanding. It is a difficult course. Most people put in more than 12 hours per week. The fora show both grateful students because of the rigor and depth of the course and on the other side of the spectrum complaints about the amount work, it being too much for people with a full time job and family. It starts of easy, but workload and complexity increase over time. I highly recommended this one, but make sure you allot some time to keep up.
A course with a light workload and an easy difficulty. Aside from that I think it's a fine course in its genre. Sort of an appetiser or side dish. The course does not delve all that deeply into the theory of critical thinking but invites you to become aware of common pitfalls when consuming information from a variety of sources. I think "Sceptical Reading of Online Articles" is a more apt title for this course.
The lectures take quite a lot of time compared to the quizzes. The difficulty level is not very consistent. Some of the lectures and quizzes are really boring and repetitive, but that could just be me. I find the topic itself is very interesting. And even though I'm not always enjoying every bit, I do notice when I mechanically work through the course material, the material sticks which helps me to apply it outside the course which is the reason I took the course in the first place.
I have experience in the field. Compared to most courses, this one is really short and easy. What I didn't like: * Ideas and opinions of the professor are sometimes stated as facts. * Non negligible parts of the quiz focussed on irrelevant details. * The course presented several ideas as a short lists of key principles. I believe the danger of doing it this way, is that you actually might believe those principles cover all cases instead of most cases or some cases. * Quizzes are uninspiring. * You can retry the quizzes as often as you please. The questions, there are 5 per quiz, do not change. What I did like: * Some of the examples that were given during the lectures to illustrate his ideas were interesting. All in all I didn't really learn anything new. If you have half a brain, some idealism, some passion and empathy, you can come up with the same conclusions.
I find the topic interesting. And the way the course touches each subject lightly is fine for me. The wide variety of models described, the examples given and the way the theory is explained is well executed. All in all the subject matter is quite easy. What I don't like is that sometimes the speed is brought to a halt because long lists of examples or numbers are summed up from the sheets that do not really help understanding the subject. This probably is just a personal annoyance. The quizzes seem to test if I can remember what the teacher said, instead of making me think and apply the knowledge I just acquired.
I read one of his books a while ago and was a little bit put off by the tone in which he preached his knowledge. This course is even worse for me. I am questioning his research from a statistical perspective, but my main gripe is more of a subjective nature. It is the way Dan Ariely presents himself and his ideas. Good science is not a popularity contest. I guess if you like his style as a platform for presenting ideas, than you'd have less difficulty finishing the course than I do. As far as the content goes, the way Dan Ariely blatantly presents most of his inferences and conclusions as a matter of fact as opposed to possible models given the circumstances of measurement is just wrong. Before you know it you believe it ;) Read Daniel Kahneman's Thinking fast and slow for a tad less pedantic and more professional overview of many of the same ideas. PS: I didn't go for the "with Distinction" track, so statements above is just about Ariely's lectures and not the reading assignments.
Having quite a lot of software development experience, I'm quite used to using logic. The material is well presented and fairly easy for my taste. The course notes act like an accompanying textbook and are well written with a few (not too many) exercises. I did the first run of this course and there were a few hiccups in grading of the quizzes and the size of the images, but they sorted it out nicely. I'm rather impressed by the quality of their first run, especially since they did not follow the usual MOOC way of doing things. They had the students explain about logic in the final exam and the peer assessment part of the course. I didn't like the topic of proof trees which. Since it is an intermediate between truth tables and using a programming language or other tool, I found some of it very tedious and not informing. I really liked the fact that I could choose one or more topics for the peer assessment part of the course. Philosophy (called vagueness) was interesting, but language was too vague for me. Digital circuits was easy and Prolog quite hard to get my head around. I can really recommend this course.
I am in awe. Robert Ghrist mentioned in the forum he put in 18 months of solid work into this course and it shows. There are many ways to present calculus and his way is not the easiest route, but I believe it has many advantages over the other methods I've seen. Ghrist has a consistent way of explaining the material. And the lectures are a real joy. Colorful formula's dance across the screen with helpful animations. There are enough good examples. The fora are active and helpful. In most topics there are a few interesting bonus material video's which make the course less dry. It does take some self discipline to actually learn all the material by heart and to do enough homework to prepare for the next quiz. Very enjoyable.
It's a very easy course. It touches an assortment of topics followed up with some easy quizzes. Don't expect to get a complete history or overview. No in depth coverage of a certain philosopher or idea. I guess this course is meant to to wet your appetite for philosophy. The value of this course is in the proposed reading materials and the rather active fora. It's nice to discuss the philosophy topics on there with people who to a certain degree share the same a vocabulary given by the course. Great for beginners. Maybe too light or boring for people familiar with the subjects discussed.
My first MOOC. The course really allowed me to get a deep understanding of the underlying math of some of the technologies I am somewhat familiar with. The math at the end of the course, number theory, was quite new to me and therefore quite challenging. The course develops a nice rhythm and many concepts keep coming back which was pleasant for the most part. The most fun and learning came from the programming assignments. The numerous failure attempts followed by some readable decrypted output gives a wonderful victorious feeling.
I dropped pretty early, so my review is only about the first few weeks into the course. During the lectures I noticed the ideas about creativity did not stroke with my own. I opted out of the course, not adding their ideas to my won. The material and the method the material was taught did not resonate with me. It felt more like a self-help book for the confidence and Facebook like atmosphere for the 'fun' than an actual course. The course could probably be very good to achieve certain personal goals. And could probably be very interesting if you are into that content and style. And certainly fun for those who like the approach. But I'm dropping this one.
I'm dropping this course after 5 weeks. There are good bits about this course, but you can probably read about those in other reviews. I'll focus on the bad bits. First of all, it does not make sense to follow this kind of quality course after the high quality courses "Data Analysis and Statistical Inference" on coursera and MIT's "Introduction to Probability" on Edx which cover similar topics, but in much greater depth and with much more rigour. I'm sure Boris Mirkin is very knowledgeable, but i.m.h.o. he lacks the educational skills. Sometimes the language he uses gets in the way. At other times he explains bits left and right without a clear route of understanding things with increasing complexity. There are times he does not explain why certain concepts exist or how they are used. The slides often contain too much text, numbers and formula's to be clear and informative. I found I had a better understanding when I skipped his lectures and went straight to wikipedia or some other resource. Because of that, it irritated me that he used terms and definitions in a unique ways, so that I couldn't use wikipedia or online searches to study. If I look up Quetelet Index, all I get are references to BMI (Body Mass Index), but Boris uses that term for a general statistic. Another example is the use of "odds-ratio" that differs from its official definition. The programming assignments are quite easy. You can use any language or tool you like. You download a dataset and upload the answer within 5 minutes. When the answer is incorrect, you can download a new dataset and repeat until correct. The environment works. It's a bit specific about white space and punctuation. I do like the idea, but I would prefer to download from within the program using some API as some other programming courses offer. The quizzes are so so. You are asked to show you've understood the definitions and to calculate certain statistics. When doing the peer assessments, make sure to read the grading criteria, because the question does not always comply with them. I did the first run so there is room for improvement. Still I believe there are better ways to learn about the topics that are covered by this course.
When I compare Jim Fowlers course "Calculus Two: Sequences and Series" to Prof Ghrist course "Calculus: Single Variable" (the part that deals with series and sequences), I notice teach the problem completely different even though the content is quite similar. While Ghrist is all about explaining it once and doing homework for hours to have you master it, Jim Fowler will actually take you by the hand and show you the ropes step by step. While Ghrist is all about memorizing and allowing few or one attempt to score, Fowler will give you hints all up to the answer in this course and allows you many as many attempts as you'd like. Since I find it quite a hard topic I'm glad Fowler explained everything so clearly so I could pass Ghrist's course more easily.