學生對 IBM 提供的 Data Visualization with Python 的評價和反饋

All videos have one minute content in common.

Too basic

More exercises pleases. I need more practice. Please answers discussions.

Out of all IBM courses so far, I didn't like this that much. IBM should put more emphasis on teaching database transformation.

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.)

Again I cannot access the cognitive class ai and this has truly hindered my learning experience. I cannot open any jupyter notebooks nor can I submit any assignments, This is really bad

The course itself was good.

Unfortunatly it was not clear at the beginning, that the "Data Analysis Module" is a pre-requisite. After struggling with the lab of week 3, I found out and took the Data Analysis Module. I tried the lab again - meanwhile the first part has changed - the file was not in the same structure. So, the code I wrote before was worthless. Took a while to figure this out.

Then in CC Lab the "conda install" did not run - neither in the lesson, nor in the lab - therefore I spent many hours struggling to find this out - didn't know, if it was my coding.

It would be good, to improve those "organisational problems". That's why I only gave 3 of 5 stars. It did cost me a lot of time.

The content and lessons and exercises and the lab itself is very good and interesting. Also the amount and speed, very good to handle besides a full-time job (if everything works ;-) ).

Peer grade assignment in this course is far too hard and packed with a bunch of topics not directly covered in the curse.

It could have more explanation and content. Everything else is good.

This course had several areas where it could be improved: (1) The Final Project was made much more difficult by requiring the students to use skills which were not taught in the course. This seems to defeat the purpose of testing, which is to test what was learned. (2) The course should have contained content which explains when it is more appropriate to use the specific types of visualizations.

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.

Some of the notebooks given did not function correctly, overall did not ruin the learning experience thought.

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.

More information about Plots, charts and overall things could be provided in the videos - on the slides.

Labs are very difficult and hard to follow. There is a big gap between what we learn from the videos and what we are asked to do in the Labs.

At least, this course is way more useful than the others.

The final exam is really vague and not organized. It would be a much better course if the instructor made exam in the JupyterLab like other courses. Also, the videos are not very comprehensive.

Not all of the tutorials rendered correctly. Information was good. Would have preferred more questions throughout the videos.

I like the lab work but the jupyter notebook is so low to work with creating the Choropleth map.

Can be better

Course design could have been better.Still a good course.Had to research a lot for this Course.

1) Quite a lot of contents of the final assignment are not covered in labs/videos

2) Huge portion of videos is redundancy and repitition

3) Labs cannot be exported to HTML

I would recommend that there should be more contents in the lecture videos and the lab sessions. It would be good to have more practical tutoring on the code. for example, in the lab it only mentioned how to do annotation on an ungrouped bar chart, but the assignment requires to annotate on a group bar chart, which is hard when i just followed the lab steps, and i ended up doing hours of searching, alghough it's a helpful process. So it will be good if the course can add more details on different methods of using the libraries that were covered.

I had many issues when people marked my final assingment. Maybe the indications should be clarified. For example some people didn't gave me the points for not showing the dataframe with the rows in the same order, although all the rows were respectively correct.

The way topics are explained during lectures are not enough deeply treated for the final assignment. It is anyway interesting.