I believe this course is key to have an overall understanding of the Data Science Methodology, however found that it was a bit un-organized and some stages werent fully clear, so had to look for additional information in other sites.

This is a good course - I would have given 5 stars for the course if there was a hands-on lab to build a Supervised Regression model with some data sets and introduced the learner to normalization and fundamentals of statistics.

Doing it for the IBM data science certificate, definitely liked this course more, as what is taught is linked in an excellent way with the interactive Notebooks, letting you experiment with the code to learn their inner workings.

Great walk-through of the general steps of a data science life cycle! My only complaint is that the materials often spoke of more than one step of the ten in a video, making it easy to confuse what things belong to each step.

The instructor's speaking pace was quite fast so had some difficulty understanding the lecture because of that. The case studies should be kept simple. Congestive heart failure case study was quite difficult to understand.

Well explained and concise. The test questions were at times hard to understand and the answers to some were in the following videos. The practice questions should be related to the video just watched not the next one.

Capstone project is a bit limited on 3 topics to choose from.

Why not more creative or even let the user come up with his own problem !!

Still you do learn a goo methodology to handle a problem with datascience approach

This course through the explanatory videos of each stage of Data Science Methodology and a case study provides you with the mindset a data scientist needs against real-world problems. Would definitely recommend it.

Overall great, maybe the most critical knowledge when starting out with data science. However, videos tend to be too short, could go more in-depth and provide better explanations and maybe more practical examples.

Good overview of the process overall. Lecture style, not a project based course. Some of the videos don't always completely convey the information as expected in the quizzes, but still overall good information.

The course is an expository one that has helped me understand some other vital areas of data science. The effort I have put into the course is not wasted at all. I am a changed man when it comes to data science.

I would have liked more explanation on what the code was performing, however I assume that also comes later. Otherwise, it was a good overview of the workflow leading to answer questions with machine learning.

Good course, very well explanation of the concept in the course. But the case study that was taken in the course was a little typical for a beginner and could be explained in detailed for better understanding.

It was a course with a good structured focus on each stage of the methodology process. I found very interesting the Lab in Jupyter notebook, providing a coherent application of the described methodology.

Great course by IBM. Would like instructors to have better communication skills. As the instructor in week 2 and 3 did not have that strong communication skills as compared to week 1 . Overall 8/10

Great course for understanding data science and data related methodologies. Some parts that included machine learning algorithms confused me a little bit, but a little google search made it clear.

The course is well structured but the case studies were not well elaborated, felt like I was struggling to understand. Also, Data science methodology should be taught after a course in Python.

The final assignment should be rated by the instructors, becuase of the comlexity of the methodology.

The best way to assimilate the methodology is by OJT. Try to assign students to companies.

I like the way they provide sample with food preparation on each of the stage of data science methodology. Need to give more sample for the study case to give more insight and understanding.

A bit more complex than what I would have hoped, but the material is still digestible. I think this course could be improve if the lecturer slow down a bit and spend more time on each topic

It is a good course, teaching about the general process and life cycle of a data science project. Excellent tips are provided. Overall, I feel it was lacking a bit in content for 3 weeks.

It was good to be able to learn the cycle of data science. The learning materials were helpful. This lecture is recommended for those who want to learn the whole flow of data science.

More real examples will be very useful to get more understanding of the methodolgy, but the course was so good that now i know how data scientist think and handle the problems they face

It is self paced giving a good grounding on the subject provided you are an inquisitive & enthusiastic learner. It does not provide a deep dive but gives a road map for self learning.

The course introduces us to the data science methodology. However, if the examples used for explaining the concepts have to be changed for better understanding of the concepts.