Learner Reviews & Feedback for Data Visualization with Python by IBM

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.

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?)

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.

Final assignment was frustrating, its was difficult, It took more than a month to get my assignment reviewed. At the beginning i waited for a week, I did not get any peers to review, then soon after a week when i logged in, my assignment was gone and 4th week videos and new assignment were reset. I completed again all modules and new assignment finally and again waited for a week to get it done. I've spent more time in this course in the entire Data Science Professional certificate, I don't know whether this is a problem from the creator or coursera itself.

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.

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.

Dislikes:

-Many videos repeated just over 1 minute of the exact same content reviewing the dataset.

-Videos were very brief and then exercises would be beyond concepts taught.

-lab contained thick code to prepare graphs but not explained.

Likes:

-Enjoyed creating the maps & learning about other visualisations.

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.

The teaching pedagogy wasn't as good as the other courses I have completed in this certificate (Professional Certificate in Data Science) so far.

Questions in the assignment were related to topics that were not discussed in the course. I had to search online and complete the assignment.

This course is in need of a healthy overhaul from content to lab to final assignment. I would recommend adding a recap "week" just for data frame manipulation. As with many of the courses, the labs could use some proofreading and updating, this course skips around a lot.

The course can be made much more better. The final course assignment wasn't much based on the things that were taught during the course. Example could be using the labels above the bar.

On the bright side, the contents were really good. Thanks to the instructors! :)

As all the other courses in the specialization, the depth is rather shallow. The seaborn and folium parts are extremely short and superficial. Good luck if you actually want to apply these libraries for your own projects -- then you're back to googling.

Hay una sección que repiten en casi todos los videos. Además no funcionan los laboratorios desde hace días , no puedo hacer el trabajo final porque tampoco sirve el link y no hay manera de comunicarse con los de servicio al cliente de coursera.

The videos had low sound quality, almost all videos had same recording for initial 2 minutes which was unnecessary. The final project had use of many such functions which were neither discussed in the videos nor explained in the Ungraded Tools.

Had some problems with changes to the interface that caused quiz questions to become unreadable. Also found some of the discussion around artist and scripting layer in matplolib difficult to follow. Could have done with more explanation.

The marking rubric on the final assignment gave 5 points or no points for a table, so there was no way to give part marks. Also the previous labs did not show how to add the SF map in the same way as the final assignment.

As compared to other courses in specialization, this course has many errors in labs. Video sessions are also less informative. All the things directly come to lab only. Also error resolution by faculty is very poor.

The final project was way difficult. Which is fine, difficult is fine, as long as the knowledge on how to solve it is provided by you in the lecture notes\videos\lab sessions. I mean that is your job right?

I was so upset for the last project because knowledge of I have learned from this course was not enough as all for completing all the questions. I really wish to get more advice or tips for the project.

Doing the IBM Data Science Certificate and this is probably the worse course. The content is relevant but the directions and labs are poor compared to the others. The explanations aren't as clearn.