It's very well put together. It just requires a great deal of time dedication specially because blocks of time should be meaningul to not lose the context present in one's mind.

Thanks for designing the course.

Very fun and enjoyable project!

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

Capstone project is challenging specially because of the performance standards. I enjoyed solving the problems

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

Interesting project and fun to work on.

As with many of the other courses, could benefit from providing a few tests. Even after writing property-based tests using ScalaCheck there were some grader results that I was unable to reproduce.

In the end, still worth the time and effort invested.

The perfect end of the course. It manages to recap all the concept, the libraries and the exercises that has been done during all other courses. It is a good point to remember all the things that one has been made, and a good point to review all the things that were not so clear.

Buggy grader

Most of the case, it is hard to deal with big data. This course provides an opportunity to handle big data and visualize the handled result on UI. Also, as you learned from FPD in Scala course, UI is developed with Scala.js

For a lot of sections one couldn't really use Spark, said sections were restricted to translating Spark datastructures to Scala collections and working with those.

Milestones 1-3 was OK but milestone 4 require a much more efficient solution. And the only info grader gives you is the message about timeout. It would be better if requirements for milestones 1-3 were harder to fullfill or milestone 4 would not depend on previous code. But it depends and if your solution is inefficient it's very painful to search the problem all around the code. It's offered to use spark and other tools in this project but using spark is also painful. I get inconsistent result from grader with occasional OutOfMemory exceptions, but this project cries to be implemented with spark. I've learned something during this course but it was too stressfull and painful.

I enjoyed this course, but I believe there are some errors in the grading code related to interpolating colors. I raised an issue about it on the week 5 forum but never got a response. The project was interesting, but it would have been nice if there was a way that we could have been a bit more free in how we designed the interfaces. If you were trying to use something like a spark RDD it made it pretty cumbersome, having to always fit everything to an Iterable at the end. ALSO, I tried to add a library when I was doing this to add the shapeless library to this to add some interesting features which would have made the code a lot more general. I spent days getting it to work, only to realize you library dependencies are not actually imported when you submit, it only runs with the base ones that were pre-defined. A warning about that would have been nice.

Good Spark / Big Data project.

Provides a good example of real problem project focussed on Spark and big data analysis.

I really enjoyed this course - it goes from the very basics of Spark and tests your ability regarding Scala knowledge.

The capstone project is really well structured, but at times it felt like the instructors could give more information concerning some definitions around the project, i.e. how all of the case classes from the models file interconnect or relate to each other. By the end of the project it would be helpful explaining how the Layer class was actually structured as well.

In reality my opinion of this course is 4.5 out of 5 - I think the instructors made an admirable job trying to convey most of specialization concepts into the capstone, so congratulations :-). If not for some of the aspects above, I would rate it 5/5.

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.

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.

Lots of ambiguity in the course assignment, especially the 5th week one. Also response from teaching staff is very poor.

All the exercise are poor explained and It's need a hard math background. It's not a scala or functional course, It's a math exercise problem with scala.

Very cool to see the entire application fit together eventually. However, feel sometimes the explanation for the exercise could have used more details/examples.

too much UI

In my opinion the course is designed very poorly. Firstly, I was expecting the Capstone to cover different aspects of the rest Scala courses more or less equally. But in fact this course is 80% about math and java performance tuning and only 20% about very basic Scala. Secondly, due to the poorly designed given skeleton methods it is very difficult and cumbersome to write a performance effective solution. In order to generate real images I had to write two implementations - one for the grader give me 10/10 and another one that really works and is capable to generate images effectively. Of course I had to get rid of all the given method signatures and reassemble the pieces in different way. E.g. performing more computation in Spark, combining several steps of computation in one, preferring stream style processing with less memory allocation etc. Actually I have found myself spending more time fighting with the given skeleton and the grader, rather than writing a workable solution.

Also, if I were designing this course I would rather go from the top to bottom when slicing the implementation into layers, steps and weeks. So the first step would be to implement a UI using mocks, then replace some mocks with the real code and so on down to the Spark level, reading the CSVs and proving the real data for the solution. This way the student would clearly understand how the methods being implemented are used, giving him much more input for creating a good design, rather than just blindly implementing some methods that _somehow_ will be used in the next week etc.

The first couple of assignments I found harder than the rest, and also the best bits. I found myself enthusiastically telling my colleagues about the data files and the spark code to join them. After that it got a bit too simple perhaps. And a little contrived. Some of the curried functions you had to fill in were way different from what I'd have written if I'd been staring at an empty screen and even after the course I've still not learned why I'd want to use this setup. The Signal bit in the last assignment was just plain silly, I think. It remains completely unclear why you'd want to use Signals here, other than that you want to refer to materials from the reactive programming course, and the sample code for the update doesn't actually work because the update method is protected.

Would have liked more tests provided with the capstone to supplement often vague specifications. Feedback from autograder is often obscure as well as it refers to some input parameters of the randomized test - hard to take guidance in code improvement from it.

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.