Comentários e feedback de alunos de Data Visualization with Python da instituição IBM

Overall a good course, especially the final assignment is well done. However, there is too much focus on the class labs and practically no effort put into the videos. Within the class labs there are only comments as reference to how and why something is done which often lacks proper explanation (e.g. what the called methods in a chart mean, how loops are used to fit data etc.)

I have nothing to criticise except that all these tools could also be taught without being dependent on the IBM platform. It's not a bad platform, it's just that you heavily rely on the internet to complete assignments. That's a bit frustrating if you want to complete an assignment while you're on the road or just have a bad connection in general. Except for that: flawless!

I felt this class was not bad.... I do think that the quizzes are a bit too easy with the assignment being a serious step up. The assignment also required you to Google some how-to's, use some patches and reference prior courses which I feel asked a lot of learners. The info is good, the skill learned is pretty cool. Not the best class, with definite room for improvement.

The material we learned will be useful. But, the Week 5 assignment had to much busy work concerning making screenshots. I got some tips on the forum though that allowed me to make the submissions while minimizing the number of screenshots I took. Hint: create the app with mode equal to 'external' instead of 'inline' and print save the resulting browser tab as a PDF.

It was a good course. I really enjoyed it when the labs worked. The labs did not work most of the time. It was frustrating and degraded the learning experience. I spent hours combing the forum and the Internet to find solutions. I was desperately afraid that I would not be able to complete the final assignment. I am glad I was able to finish.

I thought the final assignment shall be focus on Matplotlib and seaborn, but found it was only about "dash". And it is too complicated and exhausting with many bugs. I don't think python has advantage to build a dashboard since it is much easier to use Tableau and Power BI to build it. The final assignment should be restructured and improved.

This is a crucial course however does very little in the way of teaching. The final assessment is also rather buggy. I could not get the dashboard to display in the provided online notebook, so I had to complete the tasks locally. You could argue that this serves better as a teaching aid, however the videos and lessons do not cover enough.

Although the idea of this course is good, it didn't have the same flow as the other IBM courses in the IBM Data Science Professional Certificate. There were no quizes during the videos, and the final project had concepts and code that weren't in any labs or videos. Even the hints from the professors in the discussion were misleading.

The first part of the course was good as I learned about creating visualizations for EDA. Unfortunately, the section on dashboards was not done well, in my opinion, and the final assignment was quite frustrating. I kept getting errors with my code but did not have enough knowledge from the course to understand how to fix them.

I learned from this course, however, of the nine courses in the professional certificate, I would say this is the worst. There are errors in the instructions of the labs, including being incomplete. Jupyterlab doesn't work (though they do say that it is optional.) I believe they need to reevaluate the content of this course,

I felt that the course was a bit disorganized. The actual code bits that were used in labs were hard to follow and material covered in final assignment required a LOT of independent googling of pandas libraries. Overall, it was a good overview but this course fell short compared to the other courses in this specialization.

This course was good but for too many of the final assignment questions I really had a to look up how to do it on the web.

A better explanation of the key_on parameter in choropleths would help - even though the entry in the json file is features - the key_on value is feature.properties.<key> not features.properties.<key>.

Needs further improvement, examples: (1) discuss important features/syntax ... go over it, may need not be too detailed but simple instructions on what the parameters do, (2) dont repeat throughout the case because it is assumed that the students knew it from the start so that other topics can be discussed or included.

Course was very hit and miss, fine through to the final section on dash boarding which was all over the place. Complicating matters was the fact that the lab sessions wouldn't run on the suggested site, meaning that they needed to be downloaded and executed separately on something like VSCode or Google Colaboratory.

The course is nice, but there are several issues that could be easily solved:

Some of the notebooks on JupyterLab were not working (e.g. "exploratory...").

On the final assignment page there were some bugs regarding the upload (i.e. question 3)

The videos in week 5 were too condensed and resulted hard to follow.

This course is a little disappointing for me. It is a 3 Week course and content you learn in this course are not even cover introductory sections. The Final Project is So hard, that it didn't cover the important sections. I don't suggest this course if you are really serious about Data Visualization.

None of the labs data imports worked. The majority of the video content said to take the time to really learn the topics via the labs. The final assignment data sources worked, so at least that could be completed. Paid courses really need to have external dependencies reliably available or updated.

The content was really interesting and I learned a lot. I just wish the code was explained better because there were a lot of times where there were functions I had not seen before and were not explained. The final project was also a humongous pain to complete do to graphs not being displayed right.

Good content to know. Fair but not great in terms of presentation. Many videos repeated how to prep the data frame so you end up watching the same 2 minutes several times. Also a lot of the things you had to know you had to figure out on your own versus finding it in the material presented.

Still good overall but not as well designed as previous courses in the IBM data science certificate track. The final assignment is MUCH more difficult than any content in the labs and harder than previous final assignments, which isn't necessarily bad but it's inconsistent and unexpected.

I didn't completely understood the labs and where some lines of code came from. Also, I felt that we don't get enough directions to complete the final assignment, not even which notebook to use. This has been by far the most difficult course of this training in my opinion.

Great course except of the final assignment being based on a deprecated or soon to be deprecated version of Folium Choropleth implementation. Please review that part as it could be very confusing to students that do not use cognitive class as their development environment.

Since students weren't able to submit code, this made it extremely difficult to answer the final project (which I couldn't figure out how to finish). Getting feed back on how to correctly code the answers is more important than showing a screenshot of the final product.

Need more clarity and practice for this course. This course seems the toughest as it asks for memorizing artistic layer syntax which seems so difficult coupled with the humongous choropleth map!

A huge amount of practice is needed for this certificate even after labs!

Good approach to basic concepts of Matplotlib and other tools to visualize data with Python, but the assignment and final evaluation require much more knowledge than those taught during the course and, eventually you spend more time googling concepts on websites.