This course should be called "Groundhog's Day: A Redundant Repetition of Concepts Related to Fake Data And Ethics." It could have been done in 3 modules. And I don't really see how it was addressing data optimization at all. No data processing course should be so focused on generating fake data that it becomes the main focus of the course. I have taken many courses on Coursera, and this one is bad enough that I might rethink my future investments. Not that the intent is bad, but the content was more than overly redundant, the quiz question confusing and sometimes the expected answers were logically the wrong answers when weighed against the other options. And the course had more to do with fake data and ethics than data processing or optimization. Do better Microsoft. This does not make me feel like Data Processing or Optimization were addressed at all. It feels like a bait and switch.

Please include more practical on synthetic data generation, data cleaning and feature engineering. It was hell lot of theories especially from Ethical considerations of GenAI.

very theoretical, sometime repetitive.

very repetitive and very low number of examples

Excelente super bien explicado

All datasets are removed

Need more practical tasks

An unnecessarily long course where the same ideas are repeatedly recycled across the individual videos and reading lessons. The materials were very likely largely created using AI. The ideas could be summarized into a reasonably sized presentation. The information contained in the course absolutely does not match the time allotted.

Most of the content is just repeat from course 1. Very repetitive and redundant with unnecessarily long reading material. The videos have almost no practical or hands on demonstrations, just the instructor speaking and an occasional word on a slide. A large part of this course doesn’t even go into genAI, it just talks about all the data processing techniques that can be found in any data analysis course. A big waste of time and money in my opinion and only sat through it for the specialisation.