1) Classes were OK but could be improved by sinchronizing/linking what is being said to what is written. Timing for viewing slides are poor. I had to watch nearly all videos twice to get all information I wanted.

2) The Labs were by a country mile the best part of the course. Keep this çgood job Folks.

The course describes the basic process used in data science work. It is very high level and as such does not really give you any specific skills, but I suppose that is not the purpose of it anyway and it does a nice job of explaining the basic data science methodology that can be generally applied.

The information and hands on projects in this course are great however the peer review process is lacking as it depends on another student to carefully review your submitted work. I've lost points on an assignment simply because my peer didn't carefully read both answers to a two step prompt.

The access to the data source from the Jupyter Notebook kept giving me a 403 Forbidden error so I was unable to see the results of the Python codes in the .ipynb files because the I don't think I had permissions or the link is/was outdated. Otherwise, very informative and exciting to learn.

Description of the overall methodology is good but individual stages could be more detailed. The distinction between some of the stages is not very clear, e.g., steps 3, 4, and 5. Individual videos for each step are very short. A lot is said very fast, but the slides are not very detailed.

It is refreshing to see a data science course that clearly talks about the methodology (which is fundamental to thinking about the process) rather than the technology (which, while useful, but the lure of technology is often used sloppily without real underlying thinking and reflection.).

the course was very particular about the hospital example i think it could have been more generalized or templated so that it could fit with other industries' understanding.

Rest was okay enough, although not very interactive since there is only presentation on screen and no instructor.

it s the est experience i ever hav that i have not learnt this much productive thngs on any onlne platform and its assigment and quiz its outstanding this helped me alot to understand it in deep sense i am very tjhankful to courser team that they hekping me to make my career bright.

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!