informaive

Good job

Thank's

not bad

good

good

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M

The course itself is good. But the amount of material covered is staggering large compared to the previous 5 classes. Why cram so much into this one class! The material is broad enough that it should be covered in 2 classes not 1. And as I've found in all the classes in this certificate program, there are not enough problems given to help students exercise all they are learning. There should be problem sets (with answer keys) given after each week that helps to drive home the important concepts. These could be optional, but I think it is imperative that students have an opportunity to work through more problems to help lock all of this important information in. There should also be links to places to go to learn more about each presented topic.

And the amount of errors in both the videos and labs is really bad. The class preparers (IBM) have done a horrendous job of catching and fixing the multitude of errors in the videos & labs that simply lead students astray. They have to find some method to get all the material correctly updated quickly and I suggested they should keep an ERRATA PAGE that lists all of the known errors that haven't been fixed yet. This would help them to keep an active punch-list of what has to be corrected and allow students to more easily check if a problem they are seeing is related to incorrect materials without having to scrub the forums to try to find answers. And the forums are not run very well. It generally takes a day to get any answers and the answers are not very thorough and in many cases just wrong. Students need a better way to ask questions when they get confused and the answers should be completely explained and relevant.

Although the labs were pretty solid and helpful, the assignments were equally terrible, lacking depth. it seems like the course developers didn't give much thought to the level of problems being asked in the assignments. Most of the assignments contained 2-3 problems that too with absolute basics. The last few modules felt a bit rushed without proper explanation of some concepts as in why it is being used. Moreover some topics were taught erroneously as one can see from the respective forum discussion of the particular week.

could have been thorough with the assignments with problems solving emphasis like we see in the real world scenario. something like a dataset is provided and some relevant questions are asked based on the data. would have been much more helpful for aspiring data analysts. 3 stars just for the quality of labs.

Good for some one just wanting to dip their toes in the know how of the data science. could have been much better with proper formalization of assignments.

It's difficult to rate this course, because based on other courses in the data analysis program I had low expectations. I am not sure this is good for a beginner, very poorly explained, the person who wrote it is knowledgeable, but he is not a teacher. You will struggle a lot if you don't already know a fair amount. I had to go to third party internet sources to understand a few things. But, this is pretty cheap and easy. I was looking to learn and to show a credential certificate, this supplies the latter, but not so much the former. The most disappointing issue is the time we have to spend with easily fixable issues, such as code not running, no upload buttons for some test answers. You have to search thru a lot of other discussion issues to find out what to do - after spending hours trying to figure out on your own - very disrespectful. I am ok with typos, but it does show the entire thing is very sloppy.

Course content is good, but the modules (and in some cases the code itself) definitely need proofreading.

Also, students really should come to this course with a solid grasp of python, and quite a bit of mathematical background in statistics. This course will show you how to use various python packages to perform different kinds of regression (simple linear regression, multivariate regression, polynomial regression). The course does technically introduce the mathematical concepts, but very, very quickly. If it's been a while since your stats class, I would definitely recommend brushing up on the math (at least the Ordinary Least Squares method of regression) to be prepared to take advantage of the content in this course. I think Khan Academy has some good content that might be helpful for review.

Overall I learned a lot from this course. However there were too many small disconnects in the course, especially between the video voice-over and the slide material. It was like the video presentations were not sufficiently quality checked to make sure her spoken words matched the written words. In weeks 4 & 5 there were a couple of times when she named one function and the slide showed a different one. When one is still low on the learning curve it is very important that the consistency of the material is solid. When there are needless ambiguities in the material, valuable learning time is lost trying to figure out something that is more "container" than "contents". This stalls the learning process and can create a lack of confidence in the material. Good but it could have been better.

The tools are great and the labs are clear. From talking with colleagues it is clear that what I am learning in this course guarantees fundamental abilities for data science entry level jobs. I truly am thankful for counting on IBM for getting the skills I need to participate in the industry of the digital age.

IBM's brand image has a good reputation and inspires a feeling of high-quality, high-impact solutions. It is dissapointing to see the amount of mistakes, typos, and errors present in the labs of this course. It tells me whoever prepared this material - in representation of IBM - was not considerate of the reputation and image they needed to uphold.

While the content is extremely relevant, it offers virtually no theoretical base or context. Those are actually in the Machine Learning With Python course. Reason why I emphatically suggest the staff to change the course order and place this one as the 8th course in the IBM Professional Certification, right after the Machine Learning one. As somebody who is about to finish the whole series, I can say with property that the current order doesn't make sense and, for that, has a negative impact on our (students) understanding, motivation, learning and development. If this course's theory and context were properly provided before, I would give it 5 stars.

Contents seem relevant, and it gives a decent overview of the process covering data wrangling --> prediction models. There's a lot to digest though, and some rationale is not fully explained. Several sections left me with a lot of unanswered questions where I'm not sure what actions are optional in the process, and which are more essential so to say.

However, the labs struggle with technical problems resulting in users not being able to complete, or even restart, them. In addition to this, the labs haven't been proof read which means the text often being inconsistent with the code. This causing unnecessary confusion for learners.

Solid overview of the applicability and mechanics of various analysis techniques. Video content was thorough and reasonably well rounded.

Labs could use improvement. Lots of technique shown which allows for a monkey see monkey do approach to learning but not much context or explanation of why an individual approach is used or clarification of the intent of the code. For individuals already familiar with the various packages this is probably okay but without that context the take away value of the course is somewhat limited.

I understand the course isn't officially started yet, but it could've been better. There's much to be corrected in the labs as well as the quizzes. The amount of information was a lot, and I'm thankful for the notebooks I have now with steps on doing things, but the material could've been presented in a more cohesive way, this was hard to follow. Also the labs were more intimidating than anticipated (also with many errors). I think this course should be split into two classes instead with more explanation in both.

The course material was helpful, however the labs had several mistakes which I noticed they have been talked about in the forums since long time ago. Also I had strange experience with final assignment grading. At first I failed by a reviewer , I checked my answers and I was sure they were correct, I complained about it and my complaint went nowhere. By resubmitting it again I got full score! I think it would be better to have a more efficient way for grading the assignment accurately.

While the lecture material is well presented and certainly can be followed, the slides are littered with spelling mistakes, and many in important places (code that couldn't run as displayed.) Even the final assignment had formatting issues, and without the discussion forums suggesting removing the confidence interval, it was taking an excessively long time to run. These are generally minor issues that can be ignored, but as a mass, they are embarrassing at best.

The course content was relevant and quite useful. Its the structure of the course that I didnt like. These are the things that could be improved:

QA before sections are finished does not work - one should first go through the section then the mini QA should start

If one is paying for the course, the slides should be made available for download. Its nice to have reference material for afterward because one forgets things. Even more so if you pay to do a course

Many typos, some code does not match text (e.g. text says test sample of 10% but code has test sample of 15%). Where there are questions embedded in the video they often interrupt a sentence which breaks up the flow of the material. Complicated concepts or uses of code are often mentioned very quickly and the related slide disappears from view too quickly.

My peer reviewed assignment was reviewed twice and both times scored incorrectly but in different ways!