I appreciate the objective of this course - to provide a high level overview of a very complex process and to emphasize the role of the business requirements, feedback, and iteration in the data science methodology. As a relatively concrete thinker, I would have benefitted from a more practical application to a very small problem (perhaps one that could be performed with just excel). This could emphasize the context and feedback portion without requiring the programming background.

A very good overview of the problem solving methodology for data science projects. The capstone exercise was practical and helpful to put all of the pieces together in a logical order. Perhaps analytic approach and model development and deployment could have used additional modules or case studies. The single module for each is a good start, but a second case study could better illustrate the difference among predictive, descriptive, or prescriptive approaches and outcomes.

A subject that can take a whole year was packed into a few short videos, if the goal was for me to have a vague concept about data science methodology then maybe the course is ok. If they wanted me do really undertand the methdology then they did a poor job |I might as well study this by myself searching google, they just gave headlines of the subject , this is confusing I hope the next course is better because this and the previous one are under par for me at least.

This was the first course in this series that seemed to provide some knowledge. That said, the cognitive class external tool is painfully slow to use. I'd recommend skipping the ungraded assignments, as the payoff isn't worth the time you'll waste waiting for the notebook to open. This must be an obsolete tool, which IBM stopped supporting at some point.

I'm hoping the next course will allow me to run python on my cpu, rather than using a broken cloud tool.

The course is good and interesting, but I feel that it lacks the hands-on part, and that it could be more engaging. I feel that this course should be after the students have a tool that they can manage the data with, and that they can start dirty their hands with data.

The course as a stand alone course doesn't contribute a lot - it's interesting only as part of the whole certification, and should be linked to other tools in order to bring more value.

Lab exercise/further reading doesn't make sense to me since I'm new to data science. Got a headache following what happening with the codes. The methodology introduced here is an IBM methodology and its pretty easy follow. Some of the terminologies are not enunciated clearly and it's pretty hard to track and understand. Overall, this course is a basic understanding of Data Science approaches and the use of important use methodology.

Overall l it is a very good course. but on the lab section, the instructor's english is not very good. He can not deliver his thinking very well. You have to translate it to your self everything you read on the lab. In the business understanding section, he can not deliver the problem. Readers can not understand what he wants and what the goal is. IBM should rewrite this section so make it easy for readers to understand it better.

A little wordy with the labs focused on shift-entering prewritten code as opposed to giving significant input. Also felt that one peer-grade being factored into final deliverable is a little sketchy. I had one peer completely fail my deliverable selecting lowest marks on each section of the schema yet when submitted a second time with no change (i was honestly happy that my deliverable met the requirements) i was awarded 100%.

I think the content presented was okay, and was generally presented quite clearly. The labs were well structured and easy to follow, but I didn't feel that I was learning skills to understand when to use different methodologies, or what kinds of challenges I might face along the way. The example given was clear and easy to follow, but I don't feel that I learned a lot that prepared me to analyze other data science questions.

Very general and abstract presentation of what the Data Science recipe is. Still nothing practical three courses into the data science specialization... Had I followed the schedule, I would be 9 weeks in with nothing to show off. At least, this course gives a nice overview of what a data scientist will be doing, but I think this should have been presented in the first week of the first course, without necessarily testing it.

The exam for week 2 was terrible. The questions were way too tricky it was not necessary. Also I only was reviewed by one peer for my final assessment. This was bad because I deserved 100% and they gave me a only "Good" mark on one section probably because they figured out I gave them a "Good" mark on a section which they only did good on. More peer reviews should have been done than just one. I deserved a higher grade.

An informative introduction to data science methodology, but the presentation of the material could use more work. The videos could use better production values, with perhaps a bit of music and more visual aides. There is also an annoying six seconds of silence at the beginning of each video which made me think there was something wrong with my audio. It would also be nice if some of the labs were a bit more interactive.

On overall IBM data science professional certificate track: Pros: Content is just good enough, instructors are good. Cons: IBM watson and the platform given to practise on is awful and has terrible performance and reliability issues, most often doesnt work and had an impact on my test deliverables. I personally overcame those issues to some extent with kaggle's and google colab jupyter notebook environments.

While it is important to demonstrate that there is more to data science than simply applying a tool, this course did little more than name some steps in the methodological process and give a one or two sentence description. The main case study was fine for me as I have a health background, but were full of undefined clinical terminology. The description of what belonged in each step is somewhat inconsistent.

The videos were really good and the content clear, and the final activity was good. The final exam had some confusing questions that could have more than one answer or that I didn't feel were really covered in the course (and I saved all the transcripts from the videos). I know most all of this information already from prior degree programs and years of experience but still got these questions incorrect.

The lecture videos are extremely verbose and monotonic. The features on the lecture slides have low resolution, and consequently, it's hard-to-impossible to read some of the contents on the charts and graphics. The lecturer talks non-stop without properly distinguishing between the steps. Lastly, the lecture slides are often redundant and have contents that don't really represent the step being lectured.

the course videos are sometimes not exactly to the point when describing what has to happen in the different stages of the provided methodology.

this makes doing the final peer-graded review somewhat difficult.

also the description of the final assessments objectives is super vague (especially compared to the very good descriptions of the final deliverables and assessments in the other courses!)

Warning for those whose native language is NOT English: These IBM Data Science courses are in DESPERATE need of review by a native English speaker. If English wasn't my first language, I can only imagine how much I would have struggled. It is pretty unbelievable that they expect people to pay money for courses that have so many many grammar, syntax, and audio transcription errors.

Videos are at a high pace and the hospital use case introduces lots of information without proper slides,

when there is different text or points in the slides compared to the audio, it is hard to focus.

My sincere recommendation is to first talk the point in the slides, then explain the details. Also animations can be used to hide content and keep the focus on one item at a time.

A fairly odd way to teach the process of data science. I think this should be combined with the introduction to data science course and perhaps simplified/clarified. The amount of jargon between this course and the other courses is significantly greater, and while the course did a decent job, I still leave the course thinking, "hmm, what *exactly* did I learn from that class?"

Very nice course, but I have a few points -

1. As this module is a part of a single course. It would be much more better if the python codes remain same in all the modules. for eg. in one module for splitting datasets we use sklearn train_test_split, but in another we use numpy.

2. As good we explain the methodology in this course, python codes are not explained properly.

Ce cours présente quelques méthodes d'analyse, mais elles ne sont pas assez structurées. Une présentation plus exhaustive des méthodes avec des exemples, voire une nomenclature pourraient être plus utiles. Le cours gagnerait en qualité s'il donnait un schéma par type de données et méthodologie de traitement conseillée avec ensuite les outils techniques recommandés.

This course need to be more informative and give more details about each step of the DS Methodology. Maybe more info on models - what models are there, how they can be classified. More info about types of the analysis. The corresponding literature recommendation would also be awesome. Overall - good course to give you an overiew on Data Science Methodology.

Its an amazing course to give you an introduction to Data Science Methodology. But the case chosen was a hard case to understand specially if someone is a beginner in statistics and not into the medical field. I wasn't the only one who got confused while using the methodology on the case shown. Hopefully, a simpler case gets introduced in future.

The course is good but the way the example is explained is a bit confusing, especially the when jumping from study content/material to the example.

The peer to peer review for the final assignment is veeeerrryyy subjective. I had to submit 3 times (with little to no change on my answer) in order to pass. Good luck on getting a nice reviewer! :)