Good intro, but I felt the introduction to python might have been better before this subject. Could have spent more time on the Labs, seemed more complicated if you didn't have any background in coding.

The course has very little material, it feels like it could be a chapter in a course. I learned a valuable and good methodology though. So the content is good, it just feels like very little contents.

Rather open ended. The main points should be crisper. For example, why is feedback not part of the final assessment , where the keywords repeated in the training and in the final assessment?

The last course jumped to python notebooks which has a lot of coding,while Python language is yet to be covered,I feel the notebook assignment should have followed after the Python module.

This course provides a clear approach to data science methodology. The lab exercises are difficult to open and use, but the case study presented in the instructional videos is informative.

very theoretical topic and hard to follow with case study give, The final assignment is also very unclear on what to expect. This course content needs to be redone by the instructors.

this course is helpful and the video aspect is fine.

how can we have access to the videos covered in each course? i need them for my personal revision. is very important to me please.

As I'm not familiar with US healthcare system, the case study given in this course is very confusing to me as there are many unfamiliar words and terms being thrown here and there.

The final assignment is not very specific. Its's largely open ended and left up to individual discretion. Please make it quiz based or project based, where results are definitive.

The core part of the methodology is not properly covered, it would have been better if the technical information such as tools and the modeling strategies are discussed in depth.

The course was OK but not up-to the Mark,the case study was quite tough to understand,the case study could have been something simpler so that the beginners can understand well.

Using a case study to illustrate the ideas is great. But it will be better if a less complicated example can be used to help following the concepts easier and better understood.

An easier and more relatable case study would exponentially increase understanding. The present one had complex medical terms and added a layer of complexity to the course.

It was very theoretical and, at times, a little boring as well. I was hoping for more of a hands on experience. But it was definitely very instructive and educational.

Since we are following a track, the previous two courses were basic and in this course we came across to lot of data science terminology without proper introduction.

I found it less instructive that module 2 (Data Science Tools). Also, I could not use properly the lab modules. I had some message error when I run the code cells.

The example provided was not good at all to follow on the concepts. i had a hard time following up with the video since very little time was spent on each concept.

The discussions were too introductory. This would be acceptable had there been links to resources which provided more detailed information on this important topic.

The final assignment is not clear

Data preparation and modeling quite confusing for someone with no prior knowledge in data manipulation and statistical background

COurse provides some initial theoretical information but the practical exercises are ofno use. Proper schedule of practical be followed and the instructions for

Its an important topic for data scientists, but wish it was taught in a more interesting way with multiple examples of different types instead of one case study.

Very short and filled with too much jargons. A much simpler case study would have been great instead of deep diving into the world of Life Science & Healthcare

I found the material here vague and difficult to follow at times. Which led to confusion particularly about what was expected with the peer graded assignment.

Well, when the previous courses in the specialization were a total waste of time, this one is adequate, but still not very usefull for data science itself.

The speaking is too fast and the slides don't help much to follow up the explanation. I found it is better just to read the transcript at my own pace :-)