The videos are nice and clear, the visualizations are beautiful, and I'm sure that all of the libraries presented are extremely useful. But this course is not well-suited to students who have no prior background in Python before taking the Applied Data Science specialization. I look forward to coming back and maybe having a shot at understanding the code in the labs after I take a Python programming course. The long chunks of code presented here are mostly opaque if all you have are the previous courses in this specialization.

The course is amazing but the labs are really complicated as many of the things used in the lab are not been taught in the videos. It would be very helpful if after the labs a video explanation is given about each problem in the lab or the complicated ones. Even the peer graded assignment seemed little complicated because some was not taught. It was difficult to type out the code. Only this found as odd. Rest were absolutely amazing and teaching by IBM professionals. Thank you so much IBM and Coursera!

The content of the course is interesting, especially the last modules. However, there are some cons.

The content of the lectures (videos) is somehow redundant. Another "negative point" is that the final assignement is not 100% doable with the contents of the course. There are details that are not covered by the lectures.

It should be easier to complete the final assignment in our computer with a (local) notebook. It seems to me that the lecturer wanted us to use Jupyter

I Learned a lot in this course and the teaching assistants have been very helpful in the forums. This is very useful information that I learned and I highly suggest this course. The final assignment was quite a jump from the videos and labs and took a lot of work to figure out. The labs could have supported the final assignment better. Also, perhaps more work and examples in the labs would help to learn the material better. Thank you for the good learning.

The reason I'm giving it 4 stars is because the although the content was good, the labs were challenging but there are something which I found missing, for example, there should have been more information on libraries related to cholorpleth map. !wget was not working on my PC's jupyter notebook and looking it up on the internet was even harder because this extension or whatever it is big on its own. I don't know what to write to get the correct google search.

It was a great learning experience with coursera.After Data Science course ,learning Data Visualization with Python was my next target to complete.I learned many basic and advance things about how to work with data using visualization.With every questionior and assignments it was interesting and challenging to learn from this course.Thank you coursera for this course it was really helpful to learn and know about data visualization more accurately.

Like every review I've written before: there was tons of good information in the videos/readings. the test/quizzes properly evaluated the information presented in the videos. and the labs reinforced the material presented from the videos through real world application. Just the final lab project is way more difficult than anything previously presented in the class and there is little to no help from the instructor, coursera, or anyone else.

The final assignment is awful. It takes more time to generate those pdfs with screenshots than the code itself. It would be better to have the option to load multiple files per question. Or split the questions so that you need only one screenshot to load per question. I saw some submissions with the same problem. So the final assignment code is just 'copy and paste', and making pdf with screenshots - that is a real waste of time.

I believe a more comprehensive review of the material discussed in the Final Assignment would be beneficial. Perhaps including a directory of other topics outside of course and under which courses to find the material. I have all the information from prior IBM courses to complete assignment, but I did spend a bit of time just looking for my old labs trying to find material that covered the Final Assignment questions.

There is a lot of repetition regarding the data within the videos, but thankfully they are quite short (especially when played on double speed). Unfortunately there are some issues completing later modules and the assignment on any browser other than Chrome because of compatibility issues with Leaflet/Folium. Other than that, the course is pretty good. iPython notebooks do make learning a lot nicer than it can be.

The course is good but sometimes the exercise texts are not very clear and some of the lessons are very straightforward, leaving many doubts. The course should have a larger series of exercises and an automatic correction system that facilitates the review of the exercises. In addition, it would be interesting to have a module on how to use IBMDB2 without the online platform, but through Jupyter on the computer.

The lectures make everything seem simple, but you really have to dive into the labs and make a point of studying on your own. You can easily get through most of this course just by running the Jupyter Notebooks that are provided then copy/pasting and editing for the final. If you really want to get something out of the course, you really have to motivate yourself to learn the material.

Honestly, out of all the courses I've taken so far, this one was the best, in terms of presentation. The instructor repeated a lot of the formatting for each code block and by the end, one could easily remember what code to use for the specific visualizations.

The only reason I did not give 5 stars was because I thought the final assignment deviated a bit, but otherwise, a good course.

A good course to learn know-how of Data Visualization using Python language so as to facilitate analysis and visualization of data to make effective decisions. I thank the professors to make this course interesting and worth it. Only thing is, videos can be made in a better way so as to facilitate people with non programming background. Maybe some basics of programming would help.

This course has one advantage over the others in the series: practice time. The labs are more thorough and provide more practice problems. However, the overall quality in production of this course is lower than the others. Additionally, there were some points awarded on the final project for things simply not covered in the lectures or labs, which was frustrating to say the least.

I tried to get some help with my code several times, for very specific lines and did not receive an answer. The program was ok but more practice exercises would be great. The questions in the final exam were not very clear in terms of redaction. At one point i suffered a loss of information in my course progress and I had to restart the whole thing including the final assignment.

The last third of the course was not much more than two Jupyter notebooks that I Shift-Entered through, with a few problems presented to work out on my own. These were usually able to be completed by copy and paste, I learned more in one question in the final quiz, which required me to to the Maplotlib documentation site and apply that to the question. I expected more.

It is a very useful course for data visualization. It guides you through all the steps to create graphs. It is a difficult course compared to the previous Python courses because generation of graphs requires a substantial amount of input and can be hard to memorize. The instruction was useful in helping students practice, but some more instructions are recommended.

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.