This one is fairly painful to sit through and needlessly complex. Other sites have explained this much simpler and clearer than here

Nothing is discussed in details. For people know nothing about data science, many topics are not explained and they won't understand anything valuable; for people already have a background in data science, the topics are useless and too shallow.

1) This descriptions in this course are very dull. They need to be supported by better examples which do not include a lot of terminology specific to the topic.

2) The questions in the videos can be better designed to evaluate the students' knowledge about the topic, e.g., letting them apply their knowledge in new examples. Some questions are redundant such as the name of the person who designed the data science methodology or questions specific to the case study and does not necessarily provide insight into general concepts.

3) Simply reading what is in the slides is not a good use of videos and cannot keep the focus of the students for a long time.

4) This course might be located after the Python for Data Science course or even later so that the students could have a more meaningful final assignment, actually applying what they learned on a small data set.

5) Knowing a subject and teaching a subject are two different things. I hope you consult a university professor in the field about how to teach these courses. There is a lot of room for improvement in terms of the pedagogical perspective.

Instead of CHF the case study should be change to something which everyone can understand.

The example should change to a easier one. This example is hard to understand.

It just totally rebuilds my mind in thinking about how I should approach solving problems. I feel that I'm learning strong framework for an evidence-based logical approach. Just like a consultant.

Quizzes quiz on material not covered in the course or directed to externally. Most of the quizzes are word games and do not apply concepts covered in the material. Everything from how disconnected the quiz questions are compared to available information provided in the course to the peer-graded final assignment show little or now effort was put into composing this course.

This Course is pretty good and concepts are so nice but the example taken to describe the Data Science Methodology is not so good. Could have been taken a better example with far better presentation skills.

Really difficult content to digest without much written information. This course needs to provide more readings and the videos need to provide more text, as opposed to relying on voice instruction.

This was a good course. It was an overview of the entire data science process, which was helpful for me since I didn't really have a good understanding of what data science meant before this class. Now I have a much better understanding of what people mean when they say data science. Also, this class gives a good orientation for other courses; for example, I would see "data mining" courses on Coursera and not understand how that fit in with data science. Now I do. I would recommend this course for people very new at programming and data science, like me.

The base concept for every Data Scientist and taught excellent here in Coursera.

This is a proper course which will make you to understand each and every stage of Data science methodology. Lectures are well enough to make you think as a data scientist. Thank you fr this course :)

This is my favourite in the series, the 10 questions to be answered were mind opening. The repetition after every video makes easier for important points to stick to the brain. Very good indeed...

This was a critical course for me. Understanding the data scientists workflow which includes customer\client interaction has help me in understanding how to proceed in future endeavors.

The example should be easier than CHF in the videos like the example of cuisines was. The CHF example was good but it was not self explanatory and it might be hard for some people.

excellent training with hands-on exercises

Was a bit hard to understand for a complete beginner without a statistical background. Hospital example was a bit difficult to follow as well.

this course should be a little later in the IBM sylalbis

Fine for an introductory course

Course was dry, and not very engaging. I did not get much out of it, and it seems like most people only spend a couple of hours taking this course. The labs were more interesting, I had more fun looking at the code that was not explained or part of the course, than learning the actual course material.

Too complex of a case study to understand stuff. Also, too boring and theoretical and very less interactive.

Gives the basic understanding of the methodologies involved in data science domain. Outlines the step-by-step stages of the methodologies. Allows you to think like a data scientist for the final project (although not extensively). Didn't cover all the possible models that a data scientist uses on a daily basis. This course tries to explain the things with the help of case studies which consists the basic models and analytical techniques. All the way, this course walks you through the basic fundamentals of the stages in data science methodology.

The course paints an overall picture on the complete set of steps that are followed while working on a Data science project. The best part are the exercises, where we are required to solve a problem to identify the cuisine of any recipe by using a decision tree algorithm. One thing which I found tough was that the python coding part was not explained even a bit. A brief information on the various functions and methods that are used as a part of exercise would be even more helpful. Other than that, its a great course for beginners.

Good course. Thanks to the instructors, IBM and Coursera for making this course available online.

One small thing I would like to request for the answer-input area for the final Peer-Graded assignment. is to provide mechanism to add images and or link as well. That can be very useful for anyone who want to add images of Decision-Tree or Data Model etc. in addition to text explanation to make it more clear. The workaround to load images on Google-Drive and then copy-paste text-link in the answer-box was

okay as well, I guess !!

I would probably give 4.5 stars if there is such choice. Overall, it is good and fun to work through the material but there are places where the message was not crystal clear. For examples, the analogy between data scientist and cook is not always helpful. One of the quiz question described model 2 but was associated with the wrong cost ratio (4:1 instead of 9:1). If Receiver Operation Characteristic (ROC) curve is an important concept then perhaps a little bit more explanation is warranted.