Great lectures but the exercises felt contrived sometimes. Especially the exercise on PCA didn't really seem to provide that much insight into the data or illustrate the usefulness of the algorithm (especially when compared to the parallel programming exercise which had a great use for PCA). However the last exercise was good and forced the student to really explore the spark API. Learned so much from this!

I thought this was a decent course. I enjoyed the exercises and thought it gave a good introduction to Spark. Some of the lectures in Week4 were a bit long and the material needed to complete the Week4 exercise wasn't in the lectures. It would have been nice to have a 'conclusion' lecture wrapping everything up instead of just ending the course on a DataSet's lecture.

Generally a really good introduction to Spark. What I found disturbing though were the very imbalanced difficulties of the excercises and the rather uninformative test messages that did not help for the implementation. There was no course where I had to search for other peoples suggestions in the forums.

Still the course was good -- I would do it again!

Gives a really solid overview of the foundations of the Spark programming model, where it came from, and how latency affects this model for a distributed cluster. Explains well the key differenced between RDDs, Datasets, and Dataframes. Thought the videos were unnecessarily long and could do with "sharpening" them up a bit.

This course helped me understand Scala and Spark main operations as well as their use cases. Moreover, exercise directions for Week 1, 2 and 4 should be more clear. To all students, you don't need to install Hadoop to complete this course. I recommend you to use IntelliJ over Eclipse.

Very good course! Practical and industry-useful. Would be great only if there were a bit more programming assignments, with more fine grained structure, so that one could practice more in simple things, not only trying to fill out ??? marks left alone. Overall happy that participated!

Course Assignments consumed more time than anticipated, as they required the knowledge from upcoming week's video lectures. Had it not been for someone mentioning about this in discussion forums, it would've consumed more time for me to complete the assignments

A nice course to start with learning basics of Spark with Scala, however it has missing things like broadcast variables, what are tasks/executors in Spark etc. The course is mainly around how to do distributed execution using scala via Spark, not the vice-versa.

On a scale of 1 to 10 with 10 being the most familiar with Scala that you can be, it is very helpful to be at least a 6 or 7 for this course to code everything efficiently. Concepts covered here are very helpful though and it is a useful introduction to Spark.

Great course, but week 4 leaves out some key Spark SQL concepts that you need to finish the last project, such as the use of when(). Also, the part about DataSets is gone through rather quickly and without nearly as much detail as RDD and DataFrames.

Excellent in depth explanation of RDD and the API.

Heather is super informative and the material is being passed in a practical and explanatory way.

Hopefully there will be more courses like this one about Spark Streaming and Machine Learning.

It is a great course however the exercises make you waste time trying to figure out how the grader works. You really need to read the instructions word by word then go to the discussions to figure out from others questions the pitfalls.

It felt short at 4 weeks! I wish it was longer and presented an assignment with each new concept-cluster.

Great information and I appreciated Dr. Miller's efforts to simplify the newly taught concepts and present with concrete examples.

Assignments are challenging but reasonable and can be completed in the estimated time. The assignments seem a little out of sync with the course, though. Material taught in week 2 was recommended to be used on the week 1 assignment.

Excellent course, RDD, DataFrame, Dataset are better discussed in this course than most of Spark books. SparkSQL is light however. The missing star is because some code suggested by the course is more imperative than functional.

Dr. Miller's lectures are clear and concise. An excellent intro to Spark! This would have gotten a 5 star rating from me, if not for the unfortunate inclusion of the awful kmeans problem from the Parallel Programming class.

Informative. Although, it a week course on architecture of Spark (especially YARN mode), explaining Spark Jobs, Stages & Tasks would be nice addition. Thank you for sharing knowledge and a wonderful learning experience!

I learned a lot and I really enjoyed the course. What I would improve - reference material from upcoming weeks should be organized (or at least added as recommended reading) if it helps the current week assignment.

The exercises were below the standard of previous courses. Also the instructions on exercises could have been better. Lost a lot of time figuring out as a new bee in Spark.

some of the questions are unnecessarily specific (i.e. needs to be rounded to 1 decimal and sorted exactly for it to work)

but otherwise, great lecturer and great content

although spark part is taught nicely, it also takes a lot of time to understand the sql part and remember a lot of sql operations as a zero background man in sql

Great course, I learned a lot through the course. However, some of the lectures are quite long and could do with being broken down in to more smaller segments.

Great Sparks introduction. Still sometime unsure about the distributed vs local : should I compute this or that locally ? Or in a distributed manner ...

Quite insightful as a first or second approach to Spark. After being introduced to Spark dataframes, what's the value of Scala API over the Python one?

Very good introduction to spark. The assignment would be better if they were more targeted at spark, the underlying working of spark, efficiency etc.