The final project grading system is bad. Even you follow all the steps accordingly, you might not be able to find the same coefficients as required.

The updated course is well organized and useful for a workflow or a standard procedure of PySpark.

AI narrated slides with information that covers many topics with insufficient depth. The manner in which the final project is evaluated is just... ouch.

The course is too much focused on Apache. I would have appreciated more ML theory and practical examples.

Excellent learning experience - well-designed content and practical sessions.

As usually another IBM course is very bad. It gives a big list of things you have to memorize without explaining anything. Listening to the lecturer is very hard. She definitely needs to train story telling. The only good thing in these courses is a sillabus. That's why I continue to watch but I wish there would be a better course set on Data engineering topic.

After completing this course. I gained much more knowledge about Spark that apply in ML project. and other topics such as data engineering, ML lifecycle.

Liked the structure and labs. Good way to learn with handson experience.

This course gives good overview about ML with Spark.

Simple, understandable, and learnable :)

The course is hands on and very relevant

Very good introduction to Spark ML

Good course for beginners.

the lab are very helpful

Wonderful, Clear course

Great Course

Great!

GOOD

good

OK.

/

Spark is build to work with distributed systems. But here it looks like it is some strange twin of pandas. Some basics of Distributed Systems wanted with at least one at least theoretical examlple.

It good to learn basics and widen my knowledge in Data Engineering

excellent