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! :)

CONS: I would really prefer more interactive lectures. The lectures tended to be boring and monotone. Also the case study content many times was difficult to grasp because it is very specific to hospital field.

PROS: The material covered is quite beneficial in understanding the overall data science process. It is a nice summary.

I thought the course was pretty thorough. Differences between AI automation and data science problem solving is not really explored. Also the main case study was a little out of date and not very well explained. I thought it was a course worth taking as the material around the earlier parts of the methodology were really good.

IBM Developer Skills Network was have connection issues during the lessons. I worked on this course at several different locations on two different PC environments. One PC was a corporate controlled windows system, and the other was personal windows system. These connection issues distracted from the course content.

If you could have explained the terms related to machine learning more and if you could have spend more time on understanding the Actual problem of the case study and then slowly built up the solution it would have been great course. I loved the organization of course but not the flow of the course. Thank You.

It provides a satisfactory overview of the data Science methodology, but the slides and the videos does not suffice the needs to fully understand the concepts and the Labs. Supplementary readings for this course are MANDATORY to understand and fill the knowledge gaps for several topics named in the videos.

This course would benefit from more real life examples, and more time spent on an overview of the methodology prior to looking in depth. How the stages would be applied is not explained very clearly. Having completed this course, I am not completely confident in my knowledge of the contents.

The course is very good. Using a 'case' is helpful to the process. The material presented is also very good, however, would be goog if it was avaliable for the students, even in PDF format. The transcriptions itself are not enough and I was expecting more from Coursera and IBM .

I think that they should define more the specific concepts of all the states of the methodology, and then make references to "hypothetical" cases. Personally, I lost more trying to understand the examples and I had to go to find more specific information in other sources.

I really enjoyed the information in the course. Despite having all the information necessary to pass, I do not feel like the course went into enough detail on some of the topics in order to make them understandable. I think this course should be more in-depth than it is.

Although the course does provide a high-level overview of the IBM Data Science Methodology, I would say it does so at a very basic level that does not really help you prepare for any real-world on-the-job application. I can only hope the coming modules go in more depth.

The course was good but I feel the materials need to be updated. I do not think the videos get down to the nitty-gritty of the concepts. To complete this course, I still had to use external content a lot more than I did in previous courses to get proper understanding.

The concepts discussed in the video lectures are not clear enough. Also the case/example used in the video lectures have complicated terms and requires some subject matter knowledge. However, the labs are very well designed which helped in the understanding process.

There were issues with Jypter notebook not working. I also felt for some of the steps such as Data Understanding, Data Preparation etc the descriptions of the stages were too similar which let to confusion. See Week 2 Forum messages. Overall i enjoyed it nonetheless

Grateful if more explanation or more cases can be given. Also, I found that the final assignment has a mistake, making the total available scores changed from 10 to 9, which force us to make a "perfect answer" to get almost a full mark (the passing mark is 8 marks).

I think the case study is too hard to understand. The analogy using cooking it's good, however, the case study using hospital problem it's really hard for me to understand. Maybe it could improve if it's using like general case study that everyone would understand.