The content was good, however the submissions on the final project were nightmarish. Include more uploads for the screen shots. It makes it nearly impossible to see with the way they are currently uploaded.

The content is excellent! But.. the final assignment didn't work almost at all. Problems with the kernel in the IDE of the program and it was quite confusing.

Part two of the final dashboard assignment is full of syntax errors and was extremely difficult to fix/find.

This course was nice but there were extra stressors that weren't included in the course.

Unfortunately, this has to be to worst Coursera course that I have taken. I only give it 2 stars for the first few weeks, otherwise this is more of a 0/5 star

While the beginning was a descent course, the final project was very much a left field task. While it did have some of the material from the course, there were several aspects that were not explained in depth or not at all. It also didnt help that there were some errors in the base code that were not explained and it took the students to resolve them.

Many people pinged the instructors for assistance, including myself, but aside from a few comments here and there, all their responses were basically "Review this thread that says to run the code a gain and give it time to load". When I asked for more detailed instruction beyond that thread, I never heard back from the instructors. With so many students opening threads and asking the same question, the instructors should take that as a hint that something is wrong and that they should take a more active roll to resolve the issue (a new lecture or assignment to help explains the errors rather than a thread provided).

The best part was that when I mentioned I had issues with this at work, a coworker of mine who had 20 years experience, at least 10 of which is with Python, offered to look over the code with me. He was confused with what the instructor was attempting to have us learn with the final assignment.

In short, I rate this course low because of the final assignment not being properly explained before or during it and that fact that there is little to no instructor support beyond repeating themselves and telling students to "toggle the dropdowns and wait"/ "rerun the code"

Beware: Only do this course if you're ready to be frustrated and confused beyond your wildest expectations. Week 1, 2 and 3 starts out well enough, but in week 4 you get hit by brand new work that never gets explained to you. Firstly, the labs just 'give' you the code layout. No explanation, just: this is how it is, live with it. Secondly, they changed from Jupyter to an IDE based on THEIA. It is horrible, impossible to view your code as a whole, full of bugs and hard to navigate. Just scrolling in it was like a course on its own.

In week 5 you get the biggest shock of all: the final assignment is solely based on the work that wasn’t explained to you, in an IDE that makes you want to pull your hair out. I struggled for a day and a half with the assignment. Most of the time was spent battling with the IDE. I had to reopen it at least a dozen times (frozen, files won’t open), and that is when you learn the hard way that all your work gets wiped out when you do. So, copy and paste your work in Word or GitHub, you will need it. There is also an error in the code provided by them. I’m not sure if that was part of the assignment, but it seems very cruel.

One positive: between being given an impossible task and Coursera taking forever to answer cries for help, you become a master at troubleshooting. You have no other choice. Maybe that was the point of the course. If it was, I passed with flying colors. Unfortunately, I cannot say the same about Data Visualization with Python. Just doing work based on instruction, and never really understanding why you are doing it, is no way to build a solid foundation for future learning.

While the lab contents are useful and I will keep the code to come back to it in the future, I find the video lectures rather superficial and the assignments completely useless. The final assignment was basically copy and paste from the instructions to the code skeleton provided by the course. There were typos in the code, and it wasn't using the latest versions of the libraries being demoed. The assignment submission form seriously needs to be reworked with clearer instructions and the correct number of upload fields to match the number of files requested. Also the assignment asked me to prove that parts of the code I got from the instructors worked. I don't see the point in doing that, apart from wasting my time. I feel that the course was designed by multiple people under time pressure and there hasn't been sufficient reviewing across the content to check that things match up. In short, I recommend reading the syllabus before enrolling into the course and picking up with the documentation of the tools included in the course. Let's be honest, no one uses matplotlib. So skip that part and move on to the other libs in the course. Each on has a very detailed documentation with lots of tutorials and huge communities you can ask questions to. Save yourself the 15 or so hours it took me to trudge through the content and get the same amount of learning done in half the time by going directly to the source.

I found this course to be very poor compared to the other IBM Data Science courses that I have been doing (this is my 8th of 10 courses). The lecture content was interesting and well put together, and I would have been liked a lot more of it. Many of the assignments and timed quizzes, particularly in the final week, were of very poor quality. The lab instructions in particular contained many errors, with instructions that lacked clarity and setting out a clear objective. The data used in the final lab are currently flawed and also do not facilitate investigation of interesting or intuitive relationships between variables through visualisation. The authors note that the data for the final week was artificially constructed, in contrast to all other courses where real data were used, and they don't seem to have put any real thought into it. I found the quiz questions to be tedious, with many subjective answers that require simple rote memorisation and do not test any level of deeper understanding (I achieved full marks on these only because I know what they wanted us to answer, but I disagreed with many of the model answers). I have seven years experience teaching undergraduate engineering, and I would have been embarrassed to deliver something like this to my students.

The first few weeks of content was actually really useful for someone interested in data science, but the last 2 weeks were a bit of a trainwreck. It was clear that the assignments related to dash weren't well conceived--there were many issues, instructors were slow to address these issues in the forums in a concrete way, and honestly it felt more like a programming lesson than something practical in data visualization.

The final assignment grading also wasn't well conceived. The way the guidelines/questions were asked were very unclear, so sometimes you didn't see what you actually needed to upload until you had already taken the screenshot or submitted the assignment. This carried over into the grading--there were times the directions said to remove points if certain things weren't present on a student's answer, but then as a grader you actually didn't have the ability to remove points for that reason.

These types of mistakes are, I feel, pretty sloppy and shouldn't exist in a course that's part of an IBM-backed certification that people pay money for.

This course is the most disorganized I ever followed to date on Coursera. Up to the step where we learn about the Dashboards, the course is pretty well presented and the labs are working with good guidance but then, when reaching the dashboards, the guidances disappear, none of the dashboard lab work in JupyterLab. The final exam is a torture with a peer review submission so messy that you will have to spend more time assembling screenshots together than producing the dashboards themselves, and after so much struggle, you will have to answer sneaky questions with some answer that do not even make any sense in English. Be prepared for a lot of frustration and even though, they say that knowledge of HTML language is not required... well... most of the dashboard labs are actually based on HTML language. I wish the course was better prepared and that all labs were working, I have learned less than I could due to those broken labs.

Course could benefit from a refresh - for instance, support for Mapbox Bright tiles has been dropped from Folium for 1+ year, but the course still tries to demonstrate their use. There are several posts from confused students wondering why this doesn't work. Surely it'd be better to just remove/update this section of the course rather than have to deal with so many bug reports in the forum?

Also the videos for this course are extremely repetitive and barely of any relevance, e.g. 1-2 mins of several of the videos is just the same footage of the data being imported to Pandas and cleaned. Once you've seen this once, you've more or less got the point. Add to this that the Final Assignment required knowledge of matplotlib which was *not* covered in the course, and had to be researched elsewhere, and it seems obvious that the quality and relevance of the video content could be improved significantly.

Although the course was informative, the course components related to Dash applications need to be reworked or removed. For a course that should only require the use of a course-provided cloud-based Jupyter notebook (JupyterLab via IBM Skills Network Labs), the labs and final assignment should work via that service. The final assignment also should be reworked into a new assignment because of its incompatibility with the course-provided Jupyter notebook. A final assignment should not be debugged extensively and require the use of outside applications (one of them being an application that requires computer installation) to be completed. The course should explicitly state what resources are needed for the final assignment if it requires outside resources, and the debugging instructions should be listed in the final assignment instructions, not in a thread in a discussion board.

I'm disappointed. I believe data visualization is a very important skill, but this course didn't teach the most valuable skills.

The videos feel like someone is merely reading the documentation. There is a difference between showing something and teaching something, and very little was thought in this course.

In the labs, the visualizations (1) Do not tell a message (2) Are not compelling (3) Do not teach you how to generalize the idea behind the chart.

The worst part: the course creators apparently know this. Some of the labs don't even have exercises, because clearly these "classes" are not enough to teach you how to do it on your own. And the final assignment has multiple posts explaining how to fix the many oversights in it.

This has honestly impacted my opinion on IBM (is this what you offer to your clients?) and Coursera (is this the average quality of a course?)

Within the IBM Data Science Curriculum, "Data Visualisation" was one of the courses I looked forward to the most. Unfortunately, it did not deliver on its promises. The way some of the topics were covered was really superficial. To be completely honest, I am not sure what I really learned, as often all I did was copying code from left to right, without really understanding why the code looked the way it did. For example, the use of decorators and callbacks is something that is probably very important but has not been explained really well. Also, I missed a thorough coverage of how to program graphics conceptually, and of the interplay of HTML elements and dash.

It took me much longer to finalize this course than any other course, but at the same time I don't think I have learned much. Overall, a rather disappointing experience.

The lessons are so easy to finish. Annoying is to have 40% of the time of each video repeating data cleaning. MOST FRUSTRATING is the FINAL PEER GRADED PROJECT, so frustrating to the level that even if your code is correct it will fail to run to show the required charts. Google colab was the final savior for me to avoid the unending errors thrown at me in Jupyter NB, and through skills lab. In terms of recommending, I am not sure of what i can say based on the last part of it. I was grading and came across 2 students who seemed frustrated and submitted wrong files for each question. I am sure the their tactic was first to submit the project and get access to other student's work. Which showed a high order that students were frustrated and tired enough

Out of all the courses in the IBM Data Science Professional Certificate, this was the one I had the highest expectation for and unfortunately I was a bit disappointed. The course materials are lacking in information and the final assignment asks for customisations that weren't covered in the course materials, which leads to question: are these important things to know and the materials are lacking in information ? Or are these irrelevant and should be a part of the final assignment? Because if they're just there to make sure no one gets a 100% grade, then that's just sad.

This class could have been one of the best based on my interest, but it wasnt explained very well and I had to use outside sources to figure out what was going on in the labs and sections... Also some of the final project material wasnt covered in the class itself... It was more difficult than it needed to be... Once I used Google to find answers, the stuff I actually learned were useful...

Much of what was tested, was not taught in the course. Therefore, the course requires individuals to do their own research online to answer the final assignment questions.

The purpose of paying for the course is to quickly learn fundamentals from the course, NOT to spend hours looking online for the right answers and waste time with trial and error experimentation.

Nur Powerpoint, kein Handout.

Bei Google und bei der Uni Michigan sieht man den Dozenten und wie er das Laptop oder Pad bedient. So kann man viel besser nachvollziehen wie etwas gemacht wird.

Die Schulungs-Umgebung bei IBM ist eine Katastrophe. Man muss sich mühsehlig einarbeiten wie die Umgebung funktioniert. Das macht google einfach viel besser

Very poor. As the course carries IBM's name, I expected a premium product, but I was disappointed. The training videos were brief and didn't go into the material in any great detail, and certainly didn't prepare the student for the lab sessions or the final assignment. I learnt more from Stack Overflow than I did from from the training videos.

Links did not work for classes hands-on exercises, repetitive video explanations, and final project required content that was not explained in classes. Overall, it was a bad experience on a subject that should have received more thought and caring from instructors on lesson plans and class materials given its importance.

Labs and lessons did not adequately prepare me for the peer review lab. A lot of information went through quickly or was hard to reread on account of the sheer volume of charts created. Minor gripe but when learning pie charts the videos mention pie charts being awful without properly explaining why they are awful.

Almost all videos included the same bit about getting and reframing the data. This was a significant portion of the videos as well.

There seemed to be more confusion around the final assignment judging by the amount of questions in the forum. The assignment needs to be rewritten or made more clear.

Content was solid, but videos mostly said "go do the labs" vs offer meaningful tips for the final an beyond. Worst of all, the tech is outdated. I spent an entire weekend working on the final assignment due to technology issues. An embarrassment for Coursera and IBM that they let this happen.

Better to read matplotlib and folium documentation and look for answers from stackoverflow.

This course didn't offer enough components of matplotlib and folium.

This offers lab sessions but they are not main contents.

Final assignments are impossible for someone who just takes only this course.