I think that they should define more the specific concepts of all the states of the methodology, and then make references to "hypothetical" cases. Personally, I lost more trying to understand the examples and I had to go to find more specific information in other sources.

I really enjoyed the information in the course. Despite having all the information necessary to pass, I do not feel like the course went into enough detail on some of the topics in order to make them understandable. I think this course should be more in-depth than it is.

Although the course does provide a high-level overview of the IBM Data Science Methodology, I would say it does so at a very basic level that does not really help you prepare for any real-world on-the-job application. I can only hope the coming modules go in more depth.

The course was good but I feel the materials need to be updated. I do not think the videos get down to the nitty-gritty of the concepts. To complete this course, I still had to use external content a lot more than I did in previous courses to get proper understanding.

The concepts discussed in the video lectures are not clear enough. Also the case/example used in the video lectures have complicated terms and requires some subject matter knowledge. However, the labs are very well designed which helped in the understanding process.

There were issues with Jypter notebook not working. I also felt for some of the steps such as Data Understanding, Data Preparation etc the descriptions of the stages were too similar which let to confusion. See Week 2 Forum messages. Overall i enjoyed it nonetheless

Grateful if more explanation or more cases can be given. Also, I found that the final assignment has a mistake, making the total available scores changed from 10 to 9, which force us to make a "perfect answer" to get almost a full mark (the passing mark is 8 marks).

I think the case study is too hard to understand. The analogy using cooking it's good, however, the case study using hospital problem it's really hard for me to understand. Maybe it could improve if it's using like general case study that everyone would understand.

The examples were confusing at some point. The videos could've expanded the concepts more so that the differences between each stage of the Data Science Methodology becomes clearer. The case study in the video is also confusing for someone in a non-medical field.

Pretty good course, but could have been a little more challenging. I'm glad I learned the process, but I'm ready for some more hands-on work. This was getting close- I think the final projects, especially for week 3, were good practice to apply what I learned.

Appreciate the content so far. This can be though made more in-depth when it comes to hands on. Including graded level hands on practice can enhance the learning experience the students get from this course.

Thanks for enabling us with all through the course.

Additional readings needed! Although, I was able to gain a superficial understanding of all stages of the Data Science Methodology, additional readings, particularly on Analytic Approach, Modeling and Evaluation, would have been greatly helpful.

I understand that this video want to give a full image of Data Science. But its case study including too much low-frequency vocabulary / terminology, which is an obstacle to beginner. And some reading material 's exercise is not very necessary...

I found this course to be a little bit too vague and theoretical, and hence, difficult to understand sometimes. I personally prefer to study and work with hands-on and applied aspects of Data Science instead of theory and vague definitions.

The Videos provided a good overview of the process. But felt like they were extremely short for the concept they were covering. I would have liked them to be a bit longer and illustrated like the ones from the What is Data Science? course.

There's a lot of reading. If there's more hand-on training, that would be great, The tools provided to do exercise sometimes didn't work. I have to loaded several times to complete my work. That delayed my time to complete the course,

I had a few issues with the IBM cloud that could not be addressed quickly. As a result I completed the course without being able to do the all the assignments, so I missed out and did not get all the value I should have from this course.

Videos are somewhat confusing. They are not target to the current topic but also states about other topics as well in the same video, which makes it difficult to understand and easy to loose track of what is being taught in the video.

Presents you a good overview according the main topics of data science methodology. The case study is a good example to illustrate to content. But with respect to my experience the labs are very limited concerning the learning effect.

The narrator was quite fast and I could not engage with the video lectures so well on this course. Consider review the Hospital case study too, which is quite complex when trying to understand the new concepts on the methodology.

The course is a bit confusing for a beginner, the concepts should be clearly explained, the prediction model and the descriptive model should be taken in different videos.The learner should understand the difference clearly.

It would nice if case study and lab example would be same to follow more deeper understanding between video class and example .The web scraping example is nice and should be in presentation for understanding purpose.

Difficult being able to apply the final test to the complete module objectives. Though it was good foundation, felt it should be split into several workable modules/stages instead of all 10 methodology steps at once.

All example and cases are related to hospital. Every single subject with different case would be highly appreciated and helpful to others. Hospital case were kind of confusing as i am NEW BEE for data science.

Methodology course should be done at the end of the whole certificate course or at least when the student has a better understanding of all the statistical methods available (regression, machine learning..)