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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