Once again, quality hands-on labs were the highlight of this course (as has been the case throughout the IBM Data Science Certificate courses). The end-of-week quizzes were also a bit more difficult/involved, which was a good challenge. Still, I think there's room to increase the difficulty a bit further - after all, you can re-take the quizzes if at first you don't pass. I appreciated that the final project gave us the opportunity to apply a wide range of the skills that we learned.

That being said, I think there was quite a bit of fluff in the lectures. I would have preferred more content/exposure to other libraries rather than the redundant "data recaps" at the beginning of almost every video. I also would have appreciated more theory/recommendations for selecting the best visualization for a given application.

The best parts of the course were the labs and the final assignment. I spend a lot of time at the labs, paying extra attention to the details and often following the external links suggested by the instructor. I found the final assignment very interesting with good explanations step by step and I especially liked how the instructor were present at the discussion forums.

The weakest part of the course were the videos, I think I could have skipped them altogether. The information mentioned in them were elaborated much better at the labs. Also, for some reason, 1/3 of each video was exactly the same clip recalling the dataset. That felt a bit useless and loss of time! On the other hand, each video was a couple of minutes long so no big deal in the end.

I have two minor issues. First, the final assignment called for a few things that were not directly discussed in the class. The class did recommend reading the documentation, and the needed information was available there. This is probably more realistic for a real world environment, where you won't know all the ins-and-outs of an API before using it, but a little warning at the top of the assignment would have been nice.

The second issue is that folium.choropleth() is now deprecated and produces a warning if you use it; the recommended alternative is to use the folium.Choropleth() object. Using the old method produces a warning, but works (for now). The labs and lecture should be updated to use the new API.

there is a lot of practice which is a plus. explanations are rather clear. the labs a mixed bag. it's good that all of the phases of writing a code for visualizations and dashboards are written out. sometimes it's not clear where you should insert your code(e.g. output and input components of dash). it also doesn't seem like an optimal way of learning by only completing missing code lines. for example, the sql course had exercises that felt more instructive. I can't speak of the relevance to the industry, since I don't work in it in a role of a data analyst, which is actually the most important point in evaluating this course.

This course in its general scope is well understood, but needs to be improved, realizing that it is somewhat outdated in some points, and emphasizing complex and already obsolete techniques and tools, and also the diadactics in the interruptions of some subjects are very ruinous and confusing , especially the step-by-step exercises in Notebooks

This course is better with step-by-step explanations and updating the content will be perfect !!!

The course is very good, it just needs to get some aspects right, but even so I liked it and it helped me a lot to know and understand

This course was a change of pace compared to the previous, absolutely stellar courses in the Applied Data Science specialization. The instructor is different and the methodology as well, the content was equally as digestible as the courses that preceded it, but at times the course had inconsistency on its quality. There was a lot repetition and it felt like padding at times. The difficulty and learning curve takes an abrupt spike on some of the assignments, but nothing you can't manage if you put in the work and leverage on the content of the previous courses.

In the Final Project Assignments: - In Part 1, there are some contradictory instructions (the title of the task, the hints and the "skeleton" of the solution sometimes don't lead you to the same result if followed independently) - In Part 2, the Dash application building supposed a great challenge for me. However, I felt that the real challenge was trying to guess how to fill the skeleton of the already provided code, instead of building the application from scratch and gathering useful knowledge. I also felt this throughout the Dash module.

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.