The course has been forcefully put inside the IBM AI Engineering Professional Course, and does not fit in. The course instructor fails to explain the details in an effective way. Overall this course is not designed to be a part of this specific specialization.

Maybe I do not have knowledge about Python, but a lot of things were not understandable for me. In addition, parts of the course are still shown in an older, out-of-fashion version of Watson. The shown code is not working in todays version

This course and assessment method is not standard enough. It should have more practical exercises such as RDD programming, ML, SQL. Course project should have related to developing mini big data application with RDD to ML prediction.

The course should be reorganized. The video are taken from different courses and is not fluid follow it. There is very little in programming with functional programming. There are many concepts but not well explained. Not advided!

The course teaches important concepts and skills on how to scale your machine learning algorithms - but it is in a desperate need of an overhaul to fix the numerous errors in the videos and workbooks.

Did not like it that much, I´m not clear about spark and big data use cases for ML. The themes were variated and did not follow a line or path of learning.

Not clear at all.

A lot of things are not explained, or explained in a confusing way. I learn more by researching what things meant than from lectures

I dont like the instructor very much. I feel like his explanations are not the best and everything was just too fast.

It's very difficult for a beginner (like me) to understand the whole science behind the concepts in Apache Spark

The content is quite old and full of mistakes. It would be great if the course material quality is improved.

Explanation not satisfactory and exercise also not so good.

too much issue in setup all these exercises.

focus too much on practical skills than the balance of concepts and implementations

confusing to follow

It was fine, but i couldnt understand clearly what the instructor want to explain.

The course should give more in-depth assignments and also more explanation.

There is a lot of audio problem and content is also not updated.

This course needs more spark towards the student.

Thanks.

Videos are not informative.

A lot of errors in lecture.

So many errors in the codes. Especially the ones the instructor is showing us in lecture (his files run on python 2.7 and i'm running on python 3.6- has not updated some programs to run on python 3.6 with spark 2.3). He doesn't specify which file at the beginning of the video, nor does he have a link to the sample code he is showing us, nor does he specify which file to insert a spark session and to where can we find that specific file in GitHub. It's a huge confusion for a person who has zero programming knowledge and It took me a lot of time to fix the errors in the codes to get back on the lecture. I am utterly disappointed with this section. Didn't have any issue with the last session of this course. I wasted a lot of time. I'm utterly disappointed with this course.

However I must appreciate his lecturing is excellent. I was able to fully understand the theoretical part he explained. I did however fail to quickly understand the programming aspect due to multiple errors in the code.

In a word - Abysmal, the first indication of the quality of this course is the intro video which the lecturer filmed in his car!! Very unprofessional - he explains that he doesn't work from the office much, but surely he could put some effort in...

Things don't get better, a number of in-video quizzes simply offer 2 possible answers - "OK" and "OKOK", the majority of lectures are simply the lecturer typing code with little explanation.

And don't get me started on the quality of his code - possibly some of the messiest code I've ever seen, inconsistent style, massive blocks of empty lines....

If it wasn't for the fact I am doing the specialisation, then I wouldn't touch this course.

At the end of the course the only thing i could say is for a professional certification this course falls short of all parameters. Be it courseware, assignments, the way it was delivered etc were all too high level and didnt really make me enjoy it. The least i say about the instructor's english slang which was very difficult to understand and the coding part was too fast.

The big data methodology and its applications has some really good use cases which i think needs to be covered as part of the course and the course in itself has to be revamped at its best.

I reiterate for a professional certification this falls short and i was disappointed.

This course has a lot of room for improvement (not to say plainly it's a waste of time). The video lectures are useless. They consist on the instructor coding some lines to show basic commands in Apache Spark. An introductory course on Apache Spark would be much more useful than this one. The basic stuff on regression and classification methods is really poor as well. The Quizzes and practice exercises only teach some basic Spark functionalities, which could be the only somewhat useful elements of the course. Avoid this unit if you can, I just took it because I enrolled in the AI Professional certificate on Data Engineering.

This was a poorly written course that did not explain much of the Spark fundamentals. It was really hard to understand the instructor's line of thought, added with bad instructions and poor resolution videos (tried changing video settings too). In addition although IBM Watson was supposed to be free somehow this ended up as a metered service (different from Course 1). Instructions again were not updated. This course needs a complete revamp.

Learn about the diffs between rel-databases, no-sql and blobs on disk. Learn (again!) what mean, stdev, median and kurtosis mean. Plotting with MatPlotLib, brought as the 1st world wonder.

Intro to Spark and your final challenge: execute a provided notebook and just copy-paste the results.

No need to think, just know how to apply CTRL-C & CTRL-V in the end.

Al in all, can be done on a rainy Sunday afternoon instead of 4 weeks.