The example in this course should not be a difficult one. Taking the example of the hospital was in my opinion a wrong choice as it distracts the student from learning the main objective of the course, Data Science. The second example used (of the cooking recipe) was a better choice and should've been used throughout this course.

CONS: I would really prefer more interactive lectures. The lectures tended to be boring and monotone. Also the case study content many times was difficult to grasp because it is very specific to hospital field.

PROS: The material covered is quite beneficial in understanding the overall data science process. It is a nice summary.

I thought the course was pretty thorough. Differences between AI automation and data science problem solving is not really explored. Also the main case study was a little out of date and not very well explained. I thought it was a course worth taking as the material around the earlier parts of the methodology were really good.

IBM Developer Skills Network was have connection issues during the lessons. I worked on this course at several different locations on two different PC environments. One PC was a corporate controlled windows system, and the other was personal windows system. These connection issues distracted from the course content.

If you could have explained the terms related to machine learning more and if you could have spend more time on understanding the Actual problem of the case study and then slowly built up the solution it would have been great course. I loved the organization of course but not the flow of the course. Thank You.

It provides a satisfactory overview of the data Science methodology, but the slides and the videos does not suffice the needs to fully understand the concepts and the Labs. Supplementary readings for this course are MANDATORY to understand and fill the knowledge gaps for several topics named in the videos.

This course would benefit from more real life examples, and more time spent on an overview of the methodology prior to looking in depth. How the stages would be applied is not explained very clearly. Having completed this course, I am not completely confident in my knowledge of the contents.

The course is very good. Using a 'case' is helpful to the process. The material presented is also very good, however, would be goog if it was avaliable for the students, even in PDF format. The transcriptions itself are not enough and I was expecting more from Coursera and IBM .

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.