This is the hardest course of this Specialization, but I enjoyed it anyway. Thanks!

Thanks to the staff for any help, especially during a busy end of year

Very fun and enjoyable project!

Thanks for designing the course.

Great course!

very gooooooooooooooood!

well organized!

thanks! :)

see you !

Im not finished yet, but its really nice one. Much place to play with data and optimization beyond actual course requirements. Lot of freedom in implementation wich is great. Ive already learned a lot and planning to spend time playnig with Observatory project further just for fun and experience. Thank you so much for this opportunity! :)

Thanks

I love functional programming! that's fun and an great expirience!

Incredibly well put together. The capstone project is a great exercise in assimilating all the knowledge from the other related courses in function programming package. I like that I had to learn about interesting algorithms like the Mercator projection and inverse-distance weighting.

Outstanding set of courses.

Just to counter comments about grader stability and lack of grader feedback - I never encountered this in any of the 5 courses.

Great Hands on

Excellent

Excellent way of ending this specialization. This course covered almost everything was taught during the other 4 courses and gave a concrete real world usage of it. It helps to see the knowledge in action in a very cool way. Definitely take this course, the feeling of satisfaction at the end alone makes it worth the effort.

Phenominal course! Well organized, good pace, great combination of "practical theory" with examples that illustrate the concepts. and very clear well-thought out explanations by Dr. Miller. Best Spark class I've seen, and best class in this series (sorry, Dr. Odersky).

Awesome project!. It is not only wrapping all the previous concepts in a real use case, but also introducing a lot of different algorithms! Calculate deviations, spatial interpolation, great-circle distance, inverse distance weighting, etc.

Good course, some of the assignments could have been more explicit with expectations in cases where specific implementation details matter but the forums were helpful in that regard anyways.

Let's start with cons. Not often, but one have confusing exercise explanation(method docs contradict with assigment text), sometimes explanation is unclear in general. Too much geo math and in general course exercises look like "realize some formulae" definition. Grading tests output basically useless and it's sad. Tests must be the main point that explains proper way to right implementation and at some point being spec explanation.

But some pros for me exist. Geospatial is not one of my biggest-fav-domain, but it was interesting to dive into the new fields. The next thing is the oportunity to work with some kind of real world data processing scala application, it was quite interesting.

I give it four stars and a half. At the end my feelings tell me that I rather like this course than not. Yes, it has some certain rough edges, but this course help you to level up your scala and spark skills and to fill one's hand.

In the end I want to give some advices. If grader falls with out of memory, try to optimize extraction code. Spark is not always the right tool for all cases. Also, Spark has awesome web ui where you could watch what happens and where is main bottlenecks. Write your own tests... even simple test can help to find out bugs that you could think is not expected. That's all. Thanks for the good course.

week 2,3 very hard......

Well structured. Interesting subject.

Great course and specialization! Many thanks!

A lot of fun and a working project at the end with which to experiment with various parallelism strategies.

Kind of a long homework, quite open-ended in the sense that implementations are not guided step by step by the instructor. Some of the tasks can be approached in different ways and can be quite challenging at time.

Learn a lot about functional programming and spark, it inspires me how the data could be process in big data world.

Thank you! Learned a lot!