Course is very well taught, it would be better if they taught us Artist Layer a little bit in detail, also the Final assignment is little bit difficult from what we have learned from the course, it would be better if labs content taught us in a video because in video we see in realtime. Overall its a great course for learning Data Visualization in Python.

Overall, the course is good, but some additional explanation on some parameters for the graphs (specially ar the Artist level) would be good. Apart from the platform issues (xlrd was almost never loaded and need to be loaded and imported, and some downtime issues), I would suggest to move the final assignment to a 4th week, as they do on other courses.

It is an excellent class in terms of practice and playing with tools. The weak part is that the course does not cover much the logic behind different choices of graphics. Often, we just create a plot and tweak it to make it more appealing. Overall, I would still recommend this course to people who are new to the visualization aspect of data science.

Excellent course, some of the lab instructions were a bit lacking though -- there were a few outdated imports that I had to fiddle with and more system knowledge I had to find through github and stackoverflow in order to use the labs correctly, specifically in Theia. Once I knew the minor supplemental steps the labs were great and well organized.

It took me a lot of time to realize that I had to use Jupyter Notebook, that was not attached, to do final assignment. It would be great if we have an instruction at the beginning of the final assignment that tells students about this. Also, some parts in the last assignment aren't covered in lab sessions which may cause frustration or confusion.

The videos get repetitive as they each walk through and explain the exact same dataset as if you've never seen it before, but after the first few times, you figure out you can skip past that part. The skills learned are quite cool and this class shows how to easily make several different kinds of charts and dynamic maps from a dataframe.

Covers a large range of subjects and gives you are good overview of lots of visualization techniques.

However, in covering a lot of ground in a short time, I found I needed to do quite a lot of extra reading to ensure I understood what was being taught.

For me, probably the toughest of the 7 Data Science modules I have completed to-date.

I learnt a lot about pandas, matplotlib, seaborne, data visualization (different types of plots), folium and wordcount. Overall the course is very good. The jupyter notebook assignments are very nice. Folium is fairly bleeding edge so a lot has changed between the last version of the library and the one currently used by the assignment.

Solid overview on how to generate plots and simple dashboards. The explanation for generating dashboards could use improvement (much more obtuse than the rest of the material), and some of the example plots don't make much sense (the bubble plot example comes to mind, as it should be used for 3 variables, but the example only used 2).

This was the most challenging course thus far in the IBM Data Science concentration. The quizzes are as simple as the earlier courses but the final programming assignment is much less cookie cutter and required substantial reading of the matplotlib API. As a result I think it took longer and I learned more than in previous courses.

More time should have been spent describing and showing examples in bar charts and choropleth. Only simple bar charts were used nothing related to multiple bars for grouped items were demonstrated. Some for the Choropleth. Simple example in lesson that wasn't anything like the requirements for the final assignment was discussed.

The main content should provide further details and specially on how to work the final assignment. I think there is a disconnect between the core material and the proficiency required to complete the final assignment on your own. It took me longer to complete this course, it was very challenging to complete the final assignment.

Some functions used in syllabus need to be updated by the course provider.

For example, I had issues running "!wget" function in Jupyter as it is seemed not supported anymore, hence i need to search for a suitable function instead.

Nevertheless, the class is very comprehensive and I learned a lot from this experience.

The course is good but I faced frustrating situations of not being able to run my code in the provided labs. I had to spend significant amount of time trying out several recommendations in the Discussion Forum and even outside the course material. I guess, this is part of the nature of a MOOC course of this nature.

This course is much more difficult than the previous courses of IBM Data Science Professional Certificate series. Lack of tips and procedures makes it a challenge both to follow the video and to finish the final assignment. However this is similar to the real environment where you have to solve problems yourself.

This course is great for those who want to learn the art of visualization in python using different packages available for python.The only thing I want to point out is that it is using outdated packages of some libraries.Once the assignments are updated with the latest libraries ,Then it will be a 5 start course

I enjoyed learning about Matplotlib, Seaborn, Plotly and Dash to create effective and attractive visuals. The videos were long enough and the practice was very helpful. I just think that in several occassions the study material was overly explanatory to the point of giving unnecesary and repetitive guidance.

Good work through the information. Assignment challenged your knowledge. Would have given 5 stars but I continually had issues with the Jupyter Notebook crashing. I had to restart the server or just leave it for a couple hours. The content was great, but Jupyter notebook frustrated me incredibly!

Excellent Instructor. One of the best in the series. Very clear explanations, and resourceful.

One suggestion - edit Question 4 of the final assignment so that a who student copy/pastes the instructor's image would not get more points than if they put in the code they did to try to get it.

This data visualization course teaches very basic graphs. Two good thing I came to know through this course are Folium and two layers in Matplotlib which was interesting. I was Expecting some more interactive graphs to learns by taking this course but the course disappointed me there.

The workload for lab is quite big compared to other learning component in this course.

The most difficult and time-consuming part in lab and assignment is not about the visualization, is about data processing. Suggest having a more structured data pre-processing summary at the front.

This course was thorough. However, we could have been prepared better for the final assignment. I had to rely on the internet a lot to complete it. I don't know if that was intentional. If it was, then it should have been stated explicitly. Otherwise, well done and thank you!

Hi

The course itself was interesting and helpful, but unlike other ones, most of it was based and dependent on labs. Unfortunately, due to the skills network lab environment problem, it was so tough to follow up the labs, and the most important one was the final assignment.

Extremely challenging final project. The specificity of how the bar graph was to be labeled was, quite frankly, maddening. I don't remember having encountered a single whiff in the course of how one should proceed with the labeling in the very middle column of a bar graph.

Course was very good, but uploading final assignment was a bit missleading, beacause you have no opportunity to show the whole dropdown menue with all the years included. There is always only a selection shown in browser. So screenshots to make aren t´the best solution.