The tutor needs to be more involving.

the course content requires an update

Could have made it more interesting

Not great quality of video content

CHF case study was the worst part

The case study was hard to follow

More explanation would be great.

Falta mas ejemplos descriptivos

concentrated largely on theory.

it should be more descriptive

very nice course knowledgeble

Too many narrative exercises

Its very very theoritical.

Should be more interactive

too much simplicistic.

Too basic and too easy

The journey was well

A little boring...

good start.

Very boring

Nice course

bit boring

This course was fairly disappointing. Apart from the actual steps of the methodology, it does not properly teach the concepts mentioned in the course. For instance, the ROC curve used in the case study: I actually understood how it works from the forum, because one of the admins was kind and has given a very professional and well explained answer. I wouldn't say this course is a waste of time, but I believe it addresses superficially most concepts. I am a firm believer in explaining only a couple of things and doing them very well. The labs are bridging some gaps, so extra points for that. The chosen case study is not thoroughly explained - it uses methods that we are not given any context for and only the very obvious elements are explained. The parts addressing the case study need a serious revision. If you are not following the Data Science Specialization, I would recommend you find a better course on Data Science Methodology - this course is not it.

On the plus side, I did like the final assignment: yes, it is theoretical, yet it helps you really revise all that you've learned in the course.

Several errors throughout the course. 1. Module 2 - default Jupyter cell output does not show Cuisine column mislabeling and inconsistency as is stated in the text. 2. Module 3 - This question is barely english. "For predictive models, a test data set, which is similar to but independent of the training set, is used to determine how well the model predicts outcomes—using a training or test. A test data set happens during which stage in Foundational Data Science Methodology?" 3. Module 3 - "Which of the following statements describes how data scientists refine the model after the initial deployment and feedback stages?" Apparently the correct answer is "By incorporating information about participation and possibly refining with detailed pharmaceutical data." Note that the question does not refer to the case study but asks generally about Data Science Methodology, but the answer talks about pharmaceutical data...

Concepts are well explained. Case study is instead confusing and requires additional knowledge and experience (i.e.modelling section).

Sometimes topics are repeated in different sections making it difficult to understand if a task should be completed in a phase or in the next one (i.e. training sets are repeated in both data preparation and modelling).

Lab is not so useful, because it consists in executing python code without a complete understanding.

This course is fundamental to understand the methodology for data science, however I had to look at the videos multiple times to get an overview and I still feel I'm not familiar with it.