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.

I am taking the data anlayst cert and this course is the first one in the series where I hit a wall with what I had already learned (I pretty much forgot most of it from my data science course and ml course on udemy), so i watched the videos a couple of times and did the labs. I have to say the udemy courses on data science and machine learning are far better and more advance (given that is all they focus on). Some of the questions on the quizes seemed like it was disconnected from the videos and relied on the labs to fill in what they didn't teach in the videos but even the labs seemed lackluster. The last question on the peer review always gets me and I have to say it usually takes me a day or two on the last few questions while the rest of the questions I can do in a sitting (maybe 1-2 hours). Over all I am not that impressed on how they are presenting and teaching the information.

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.

Pros: The labs (guided worksheets that ran in Jupyter) were good, and were how I learned the material. The final project was also pretty good, with clear instructions and a reasonable challenge level. Cons: The video lectures were not at all engaging, and I mostly skipped those, or just skimmed the transcript. Occasionally there was a lack of alignment between the videos and the labs, such as a different method being used than that presented, or the videos on Python DB API which never showed up in the labs. Also, the multiple choice assessments were terrible - most questions were trivial, a few obscure, but almost none effectively tested either programming skills or conceptual understanding.

It goes okay and can be understood and followed nicely until about half way through. Then it starts to go lightning speed and I felt things were not explained. Was like "we do this, this and this" but no explanations of why and what the obtained results mean.

There is like 0 chance I could do any normalization, standartization, polynomial transformation, pipelines, ridges and what not by myself after this. Partially because I did not fully understand what all of that does and what real life applications are and partially because much of the code in labs was pre-written. Though I bet half of us who start this would not be able to finish ever without all the pre-written code.

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.

The labs are very good. That is the most redeeming part.

The instruction videos are quite simply, very monotonous and boring - you don't see the instructor and there is no attempt to make the learning stick.

You don't get timely or quality responses in the discussion forums, so sometimes you feel like you are on your own.

The final lab assignment required you to get on to the IBM cloud and set up your account. I get why they are doing it, but it was clunky. You are required to set up on the free option, but the set up is overkill for a relatively small assignment.

Overall, I probably will not do another IBM course.

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!

Some of the instructions were not clear enough, with a couple of typos here and there. Alot of explanations can be given to the code, e.g. what is for what. Also, before the video quizzes, needs to let learners look at the screen, pause before flashing out the quiz. Overall, good experience. Aside from having some difficulties trying to understand some parts of the module, but able to pick up Data analysis thanks to the course.

Many of codes that listed in the hands on lab were not well explain in first place, it just suddenly appeared. It was not easy to understand. Instructor should gave some note on what each line of the codes will do. For my case, i need to research it by myself, therefore the time to complete the course are not always as what designated since it required more own effort to gather information that lack in the hands on lab.

+Useful course for beginners. You get to learn basic concepts although these are not enough to get to work on real projects. Another good point is the set of useful libraries and methods presented in the course.

-Downsides of the course are the amount of mistakes found in the labs which are supposed to help understand the theory seen in the videos, but in some occasions can even mislead and mess the students up.

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.

Great coverage of topic, but unfortunately comes with several imprecise (or even planely wrong) explanations in the videos. Video quality (style of presentation) is ok, but sometimes missing things are slightly missaligned or questions show up before the topic/sentence is finished - could use some polishing. The hands-on labs are great though - if the notebooks open or the servers are reachable.

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.

Material to learn data analysis was very good but had quite a few bugs. It was very annoying to review the assignment of a peer because it is not possible to zoom into the screenshot. Furthermore did I need to flag a person because he had copied screenshots and his notebook was empty or only with screenshots but I was still required to review a second person to complete the course.