Hard to understanding content in this section. Especially where the tutor give an example of case study. If you want to do some revision fro this course. Please explain it in more general because for people who didnt have Stastic or IT , is not easy to understand. And also for final assignemtn. Could you please make some example how to finish it ? because i dont know to serve the answer like what exactly you want

Content was excellent in providing a framework to understand the process. Unfortunately, the tools used were completely inadequate. None of them functioned, course "TA's" frequently said problem was fixed, but it wasn't. Eventually, I just gave up on the ungraded exercises, but that meant I didn't actually learn anything beyond what I could have gotten by reading a book.

It was great to learn about the methodology and the process that goes into building a model. However, the video lectures felt like something that was quickly thrown together without any passion; extremely boring with a monotone voice, uninteresting slides, and a core example that was boring and felt uninspired.

Can't download the transcript for studying. Only get subtitles. A lot of information to learn. Found questions on the tests that were not in the material (I went back through the videos after the tests and no mention of some of the questions). Hope the rest of the courses are more complete.

Sometimes methodology can get verbose and abstract, but this content was quite good. The outline of topics and methodologies could have been a little tighter.The English is not so good and there are lots of spelling and grammar errors. There are also bad links for things like images.

The videos contained what felt like a lot of information that would have been bettered digested as a written lesson. I believe the course would have been better if all the information in the videos was also available as a pdf, to really be able to look at the slides while reading.

Doesn't fit as one of the starting courses for the data science certificate series: touching on a lot of jargons that will only be covered later. Plus, without writing the code and performing the analysis one can benefit very limitedly from this course.

The narrator speaks like he is reading the text. Content and especially case study is not easy to understand and somehow the method for teaching makes it more difficult. When you are trying to have full screen, resolution of the pictures are poor.

The topic is very interesting, the examples though are not sufficient. It would be helpful to exntend the lesson with many examples, some simple and some complicated, in order to give better knowledge and understanding. It is not very engaging.

the content of this course is not well designed for the beginners, it actually requires a lot of knowledge in data science field in order to fully capture this lecture. however this course is supposed to be an introductory course (I think).

The material was not engaging. The quizzes had questions with answer choices that were longer than the actual question and were all correct answers except for the addition, subtraction of one word or in one case a single Roman numeral.

The example topic is not easy to understand for people work outside hospital, it is better to use some common examples such as email and credit cards that more easily understand the flow. Too much technical terms and explain too fast.

Needlessly complicated, and could be offered in much simpler examples, the expressions and case study is very irritating to understand due to complex description. Also the context of the course could be offered in much simpler way.

It is a bit too fast for the beginner students to fully grasp the idea. I mean of course, the topics of methodology looks fairly straight forward but the explanation and example used in those topics are not explained in detail.

The methodology presented is useful, and can be a good reference for those new to the subject. However, it is not always clearly explained, and as in previous courses too many technical terms are employed without definitions.

The sites have changed so following the directions of this course were very confusing and hard to understand. The update from Data Science Experience to IBM Watson studio did not relate to the video demonstrated in this class

The final assignment had me ham strung. You are supposed to do each of the 10 steps at the end, with very little guidance on HOW. The there are specific methods shown but what if they don't fit your final assignment example?

I feel that the course is not vary interactive and in some cases it seems to be more like a commentary than an interactive session. Due to that its difficult to understand the basics of some of the important concepts.

Jargon was used interchangeably and material was not presented clearly as to what stage it belonged in (data understanding vs data prep and what could be done at each step. Similarly with the types of analysis. ) .

The videos are good. All in all it is still the lowest quality educational experience i have ever taken part in. Peer-graded assignments should not be part of a professional certificate. I am disappointed in IBM.

Not as engaging as previous courses. Feels like too much concentration on the healthcare case study, rather than the concepts. More of the key points should be on the slides (for the visual learners among us...)

No hay suficiente información y el "instructor" no es el mejor en esta sección. En general este apartado fue muy aburrido pero no por el material sino por el tipo de videos sin instructor y solo diapositivas.

The delivery is so dull.

The slides are not enough attracting and sometimes contain a lot of information.

Quizzes are so accessible to the point that you don't need to listen to the course.

Absence of practice.

I really like the methodology proposed and introduced in this course. However, the whole idea can be summarized in less than 2 pages, which should be a section (a week) in a data science course (~13 weeks).

Videos get a bit boring. I would prefer less videos and more reading about the methodology. I google IBM Data Science Methodology and got more useful information. Why pay for a course that can be Googled?