Extremely powerful framework, good case study, mediocre analogical explanation. I am really impressed that IBM put heavy emphasis on the methodology before starting off with anything else. Having the mindset and the framework for execution is the most critical thing of any endeavor.

While the course was structured clearly explaining the purpose of each stage in the Data Science Methodology; however, the content in each stage could have been more detailed "in the presentations" and could have included more examples to explain 'what happens at each stage'.

a Great Understanding of How to see a real world problem and getting a proper path for that specific solution a instructor voice much feels like a radio jockey so recommended to add more easy example rather than hospital problem as it is not a field of interest for everyone

Good for those just getting into data science/analysis that don't know the full circle process beyond the number-crunching. For those that have produced full-scale deliverables, not entirely necessary, although you could get through it in a relatively short amount of time.

Really nice to have applied labs in a JupyterNotebook environment. The student can even replicate the codes (if wanted) in its own notebook.

Some improvement opportunity: add a lab in the 5th module. Also, the definition of training set vs. test set is a bit unclear, still

Great intro course for methodology used by data scientists. The optional Jupyter lab exercises are also great. However, I think the lectures can be improved further. I have watched better videos on youtube with less production value, and read better blog posts out there.

Good course. However, I think the concepts are a little tough to understand at this stage. Maybe this course can be provided at a later stage after other concepts such as Python development are covered. Also, the content of the videos should be made more easy-to-digest.

A very good course outlining a Data Science framework on approaching data science problems. If this methodology is applied to a data science problem, one can effectively determine how to proceed forward with a clearly structured plan thereby saving time and resources.

This course was intense for a beginner in Data science and I love the challenges which pushed me to explore further and read wider. However I do feel more examples on application of the data science methodology should be given. In any case, it is a wonderful course.

The course was great to have a panoramic about the methodology apply for a Data Science Project. The only reason I have not given the highest mark is because, from my point of view, some explanation was too much short to understand the meaning of an analytic stage

I really liked the course. However it was pretty challenging for me as i don't have any background in this field. I would prefer a little bit more detailed explanations on the topics so that it would be easier to understand all the models and other topics covered.

Overall content of the course is good, but I think a clearer, more common example could have been selected over congestive heart failure. Also sometimes there is a confusion between the elements of methodology, so a reading to complement videos could help a lot.

I thought that the material was certainly important, but felt that the quizzes were more memory of the videos rather than an intuitive understanding of the material. Maybe more case studies, or a less complex one might make the material more easily digestible.

Information was helpful, and laid out the broad strokes of data science projects well. But, I feel like it could have been condensed and combined with some of the other courses to make one Intro to Data Science course with the tools and methods all together.

Important overview to the systematic methodology of data science that can easily be overlooked. Interesting case study provided, along with another example in the assignments, showing that this methodology can be applied to all types of problems and domains.

Good walkthrough of one methodology for data science research. Lightly covered each of the steps involved with both a case study (a real-life hospital readmissions scenario) and a hands-on practice one (analyzing recipes to determine what cuisine they are).

I really like this course! I would like to know more about techniques a model statistics to understand more the processes in Analytic approach, data preparation and modeling and apply correctly in a specific situation in a data science project. Thanks!

Even though I used to work with data, I found the data science methodology a must course for a future data scientist. I like how each stage of data science workflow is summarized to a wheel where each stage communicates with others in an efficient way.

Great content and framework. I enjoyed the explanation of how to follow the framework and how to carry out each step, along with learning what actions to take in each one. The only improvement I would suggest is better graphics and a better narrator.

The first of the three courses that felt coherent and professionally produced. The material was nicely presented and thorough, although occasionally repetitive. Having a single narrator and a clear narrative made for a good learning experience.

I loved doing the peer graded assignment but i thought it would have been better to put the course a little later as for newbies they tend to loose interest for the simple fact that the cant understand the codes at times put up in the notebooks

The course is good and the content but I, as non native english speaker, would have preferred a clearer Case Study and avoiding questions in the Qualification Tests around it, as many term were not clear to me and some issues were quite subtle.

Less than a minute after summiting my Final assignment it came back with peer review that was disappointing. I describe every data science methodology stage and the feedback for that section was that I describe some of the stages but no all.

Need to repeat some of the videos. Also I have to find different case study since at first you can't really digest some of the case study. But after tried different sources of case study, I managed to grasp what's the author trying to tell.

Gostei muito, achei muito bom pra introduzir conceitos básicos e ir se ambientando ao pensamento de data scientist, mas um problema foi não ser mais explorado alguns conceitos e tecnologias. Acredito que o curso poderia ser mais longo.