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.

Enjoyed learning about apache spark and optimizations in distributed data processing. I still feel like I've only been introduced to spark. Maybe if there was a Spark 2 course? I would like more familiarity with this tool.

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.

Very practical course. You can quite freely apply the course material to the programming assignments. I feel like I really learnt Spark in details.

The spark internal details was quite descriptive for few topics. Need to add more topics mostly related to transformation and spark submit flow

Introduction to kmeans or asking to read about kmeans would have helped. I found programming exercises more difficult then some other courses.

Somewhat specific, hard to reuse knowledge but do recommend if you're someone who works with Spark or even just work with someone who does.

It would be great if there are 2 assignments covering dataframes and datasets spanning week3 & week4 instead of week 3 with no assignment

Way Much Better Presentation than the previous 2 courses in this Specialization!!!

Dr Heather and M. Odersky are really good professors!!!

With this course, I surely improved my knowledge about Spark... But I am still thinking that Spark is an overly intricate framework.