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.

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.

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.

In this course, you are asked to apply your Scala knowledge to a more practical project.

However, I found the tasks rather confusing and lost track of the actual purpose of the application. In the end I just implemented everything function by function. So it felt more like in exercise in understanding the goals of the course instead of a Scala training.

If the goal is to develop an application that feels a bit more real-world, it would be good to have more vivid explanations of the business logic, for example in videos and not just as text.

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.

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! :)

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.

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.

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.

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.

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

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.

The capstone project has done a fantastic job of drilling in some of the important fundamentals taught in the rest of the courses. It is definitely worth taking if you've done the rest of the courses.

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.

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.

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

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

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

An excellent well paced climax to the Functional Programming specialism

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

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

This capstone project is a nice end of Scala specialisation!

Nice course which cement your knowledge of scala in action!

very gooooooooooooooood!

well organized!

thanks! :)

see you !