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I found a lot of the information given in the course very helpful. It covers basic data visualization techniques and tools, so, as a newbie, I thoroughly enjoyed most of it. One thing that I wasn't particularly happy about is that toward the end of the course it is starting to get quite difficult and therefore takes up a lot more of your time than declared because some things that I (as a beginner) am unfamiliar with are treated as absolutely obvious, like HTML components. You are explained how to use them in Dash, but first you have to find out what they are in the first place by yourself if you are not familiar with HTML at all.

Another thing that bothers me a lot is the final assignment. I believe, the whole point of data analyst's job in data visualization is to provide other people with understandable, ready-to-use information. Yet you find yourself gluing screenshots together and converting them to pdf for a solid half hour in order to present your peers with the info you need them to assess, while you actually already have that necessary dashboard coded and ready. And I am left wondering where I should learn the required skills and what software I should actually use in order to present people with the dashboard I've just learnt to code.

Perhaps it is another level that would allow us to present the dashboard in its actual form and not its screenshot snippets, but it would be nice if the course taught you how to do that and gave you the tools to practice it.

Before visualizing any data, one should gather and import those data to their computer directory, and this could not happen without the Pandas library. Importing the data could be done simply using the Pandas library, whose functions somewhat overlaps with the Matplotlib library.

Although in the last week, the author introduces the Folium library, which is a library to visualize Maps and other related things that could be shown on the Maps, like the population density of different cities in a country, the main focus of the course is on the Pandas library, which is, of course, need that lots of attention and time.

In summary, this course is especially helpful for those who want to become familiar with the Pandas library.

The author also gives a very short amount of time to show how seaborn could be used to plot the regression plots using seaborn.regplot function, which is also showing wise time management by the author since it does not need more amount of time to spend on.

The course and materials were very useful. However, there are a couple of things that I would like to flag up for possible improvement

There's are over reliance on the Jupyter Notebook and a lot of useful information that should have been in the videos was pushed into them

I know Dash is a large subject to cover but more information about the call back mechanism in Dash would have been useful - Fortunately I've used Dash, Matplotlib and Flask for a few years so it wasn't much of an issue for me.

Every video spent the first minute going over the data layout rather than focusing on information about a particular function (plot)

The biggest issue was the fact that I had to ask to be moved from an inactive session group, to an active one, to get access to the external tools and tests. This has impacted a large number of students and I have left a 'how to raise a support case' note in the discussion board for the group I was originally with

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.