This is a great course for the non-scientist to understand the scientific mechanism and models behind climate change. I requires quite a bit of work but the lecturer explains things extremely well. Overall, some of the quizzes could be better thought through but this is a very good MOOC.

Highly interesting, informative, challenging and time-consuming course, which has some frustrations when it comes to the exercises. I wish it was still maintained to iron out remaining quirks.

There appears to be no statement of accomplishment unless you buy the certificate.

The slow pace and the simple language helps to comprehend the climate science very effectively. Thanks a lot for that. Use of fundamentals with very little work load per week is fun!

Overall this is a good course. The drawbacks are that there are problems with week formulations of some questions. There is no support from course creators.

Good course! in hindsight i wish i bought the book at the beginning/there were some written summaries/pdfs of the key points.

Great content and very good teacher! I dont give it 5 stars because there is no teaching staff support at all.

1.Course lectures = very good. Occasionally too brief; more detail should be provided.

I appreciate the coursera .

An overall good course.

Great course!

Very good!

This course is addressing a critical subject matter. The material is great. I love the math.

But these are my personal metrics: A 4-star course has a high level of student collaboration and teaching staff participation, a 5-star course has very responsive teaching staff. I love learning the material but the learning environment is disappointing.

As the course progresses to the later weeks, the inconsistency between quizzes and lectures grows. Not greatly but enough to frustrate. Some quiz questions, with multiple choice options with only one correct answer, will not accept any answer. There are a few questions which are not covered in the video lecture (or covered in later weeks). Some answers can be found in the book (but the book supposedly is not required). Some answers can be googled but the answers vary wildly between sources.

Mathematical Thinking from Stanford is the gold standard.

It is a good course & I am grateful for any one you puts effort into making education available. The -2 stars is due to complete absence of moderators leading to a defunct discussion. Also be aware that there are a lot of peer graded assignments. Which I think work well, but do mean more work than a lot of courses. In addition you must pass all tasks, which meant I actually failed the course by getting the second top mark you can get on the last assignment despite passing all the others (a fair bit of work put in),

The course was a broad overview to different areas of climate science. At the end I understood a little more deeply the mechanics of global warming, but some things could have gone in more depth. I found the quantity of assignments/quizzes to be a bit unmanageable even though some were very short. I really liked the explainer assignments. When I first saw them i thought they were going to be a bit much, but actually they were extremely helpful as they forced me to process the information learned week to week.

The course was much more difficult than I imagined - it required more time than the explanation suggested. For me personally, the theoretical part was often not enough to go through tests with ease. The most frustrating part was the last assignment - the scoring was not well designed (you need 9 and 10 to pass, but can get either 7 or 10). On the other hand, the course was interesting and I learned a lot. With a little improvement, it would earn 5 stars.

The material is very interesting. There a a huge number of quizzes over poorly explained (or not at all explained) models, which would still be an interesting challenge if the models themselves had better explanations. This has been the most frustrating Coursera class I have taken, not because of the subject difficulty, but because of the lack of adequate resources for working with the models.

Databases very difficult to work with, and badly programmed.