It was surprisingly fast-paced. There were a few intuitive leaps, including a bad data reference on the final project, that were potential stumbling blocks, but I feel more confident having overcome them.

It is a good course for beginners in the domain of Apache Spark and Apache Spark ML. Programming assignments could have been better if they were applied to "Big Data" and not on toy datasets.

Again a nice course that introduce you on Apache Spark Usage. Just a little suggestion, if you could insert a little tweak on how pass from spark to pandas and vice versa.

Enjoy :)

Teaching was clear and understandable. Only feedback would be I hope the lab work would be more hands on because I'm worried I don't pick up the concepts unless I type them out.

Depending on the student, this can either be an easy or a difficult course. Some parts needs update, and it would be great if there are more explanation on the algorithms.

Nice introduction to Big Data processing, No coding skill required. A little more focus on the theory would be nice as the Python coding exercises are a little redundant.

Great tutorial overall.

Room for improvement: Fix the differences int the definition of kurtosis and skew between vide, test, examples (preferable the scipy definition).

It was a very interesting and skillful course. Thanks to IBM and Coursera for such a wonderful course. Special thanks to Mr. Romeo Kienzer for explaining it so well.

There are some issues with the normalization of the distribution moments. Everything else is good material to learn how to use apache-spark for the first time.

Great notebooks. But the videos are getting old and are a bit obsolete compared to the contents in notebooks. I would have also appreciated more theory.

Learning material is pretty good for getting started with Apache SparkML. Everyone who leaps into Scalable Machine Learning this is one of your choice

I found this course incredibly beneficial. Moving forward, I would like to see a bit more explanation of concepts and few extra workable examples.

Course was nice and avoided peer-graded assignments (which I appreciate) but code was written in Python 2 which led to un-maintained code.

In some videos, it shows one thing in the video and then there is a prompt to follow another one. It gets a little bit confusing there.

I found difficult to understand the concepts, for sure I must have to review the class.

Thanks for the dedication in helping us.

Itamar

for the last assignment we should have got the opportunity to code in the notebook instead of just running it and reporting results.

He has good knowledge. Though his language is ok , He covered very important topics in very short span of time with high quality

Nice course with real details and opportunities to practice. We just need some more private study to cement skills learnt.

I felt the week 3 and 4 were rushed a bit. But everything else was well done. It was like a well defined "pipeline" : )

Nice course. I really understand big data and how to manipulate data in data centers. I can use better Apache Spark.

Explanations could be a little more detailed. Felt like I was missing chunks of information while watching videos.

very systematic way to learn ApacheSpark (esp pyspark). It would be helpful to include more hands on excercise

This course gives you a basic idea behind the pyspark. If you are a beginner so this course for you.

Instructor pronunciation is not the best for someone who are not usually listening explain so fast.

Cool course with a slow paced start and then interesting examples to work with Apache Spark ML.