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This course contains many useful information. The combination of data visualization in producing figures, dashboards, etc., and how to present slides and so on is very useful and makes a lot of sense. It started very well, and I was hopeful as the previous course (analysis) was very disappointing. However, there were many issues with this course that soon became apparent. First, the coach, at many times, is very monotonous. Which is ironic given what the coach professes to be important when presenting. Second, there is a lot of repetition. In relation to this, much of the terminology seems to be very similar and essentially just about splitting hairs, which is not good. This leads to quite a bit of confusion. Really, they should try to do away with a lot of the definitions, streamlining the 'plot' as the course deems to call it so that the big reveal and the aha moments are much clearer. Third, some of the stuff taught is outdated, particularly with the join option in Tableau. It is still possible to do joins, but there are many more hoops to jump through, and it seems like this has been changed for two years now, so it is very frustrating that this course has not been updated yet. Lastly, the ordering needs to be improved. There are also tasks that really should have accompanying videos for there to be more visual insights into how certain steps work. Ironic, given that this course is meant to be all about visualization. Not a bad course per se, but there are a lot of rooms for improvement.

I found this to be the least useful course in the sequence. Much of the information was fairly good, but it was explained in ways that were not as helpful. For example, throughout the course videos would talk about "making a presentation" and it wouldn't be clear whether they were referring to the presentation or to a slideshow that might be used in a presentation. Obviously, while these are related, they aren't the same thing.

Some of the principles of slide design taught in the course differ from those supported by current research on visual communication, and appear to reflect the common practice and community expectations at Google and in the tech industry overall, rather than research-supported best practices. Likewise, some of the questions used in tests and quizzes in the course included some really elementary misunderstandings of color theory. I'll refrain from providing specific examples here so that I'm not disclosing course content, but it is a major credibility problem. Fortunately, I didn't take this course to learn about color theory.

I recently completed the Share Data Through the Art of Visualization Course, and overall, I found it to be a valuable learning experience. The course content was comprehensive, covering a wide range of topics. The instructors demonstrated a deep understanding of the material. However, there were a few areas where the course could benefit from improvement. 1. For instance, some examples used to illustrate key points were not clear. A specific example that stood out was the "dogs" example. It seemed to be context-specific and not easily understood by learners from different cultural backgrounds. The course creators should consider writing more cross-cultural, wide-ranging, or general examples that can be understood universally. 2. The course suggest a lot of externals resources which causes the student to get distracted, lose focus and get off the track, Including the content of these resources directly within the course would be more beneficial, allowing students to stay on track without falling behind.

The worst out of all so far. Love the series of courses, thank you Google for creating it, but data visualization course is poorly designed in my opinion. Too much effort is made on linguistics instead of substance and by that I mean not effective communication techniques, but learning the preferred terms to call certain things. Certain quizes will literally give me synonyms for words meaning the same thing, but only one is correct. How does that make me a better visual designer in data analysis?

Also, maybe I am bias because I really do not like Tableau and I do not understand why after learning SQL we go straight into the software that is designed to use visual queries instead of SQL so that people who don't know SQL will have easier time using it... Redash and Metabase might be tricky to deploy, but at least with them you use actual SQL to build visualizations, not GUI.

There was a great deal of material on presentation skills and how to apply the basics for a good data visualization and I am very greatful for that. It did allow me to learn more and I am sure that my future presentations will improve significantly.

However, I have to say that there are two specific aspects about this course that could definitely be improved.

First, it seems like the course material could be condensed to get rid of all the repetition! Practice what you preach kind of recommendation!

Second, the sections about Tableau were very confusing. We were supposed to follow along using a different version of Tableau than the one used in the presentation. I had a lot of issues with that and I even decided that I needed another course on Tableau from another resource. That is quite unfortunate. That is the reason why I gave 3 stars.

I have a love-hate relationship with the course. The content (videos, readings, and activities) are absolutely outstanding. The quiz questions, however, are truly abysmal. It feels like the quizzes were created by someone who asked, "How can I create a question that fits the multiple choice format?" rather than asking, "How can I assess the student's learning?" Nearly all the questions are low on Bloom's Taxonomy of learning. I found myself really enjoying the videos and activities, only to dread the poor quality of the quizzes. I can provide specific examples (screenshots) of poorly formed quiz questions if that would be helpful. The quiz questions' poor quality and execution stand in stark contrast to the otherwise outstanding quality of the course. I strongly recommend that Google revises these questions.

I appreciate getting a first introduction to Tableau. I now know some basics. However, the part of Tableau should have been much more extensive. The amount of practice that we received with SQL was not equalized by the amount of practice with Tableau. I feel that I will need to take another Tableau course to give me all the basics.

Why not add a few weeks, or combine some other weeks' contents to make more space for Tableau? Additionally, a few sub-assignments were not possible in Tableau, as issues arose. I think the course would have benefited from a basic exploration of the software as well. The part with the map that was used as an introduction did not suffice. Keep up the good work, though, as this is a nice first version of a data visualization course!

I was sort of disappointed with this course. I liked the presenter, and the general outline of it, but I feel a bit shortchanged when it came to Tableau. For one thing, Tableau Public seems to have gotten a bit more limited since this course was created. For another thing, it was a smaller portion of the class than I think it should have been. I wish the course had used a more robust tool and spent more time with it.

The emphasis on soft skills and storytelling is probably necessary for a lot of people who want to get into data analysis, but as a former novelist, the whole concept of "data storytelling" always feels a bit forced to me. I appreciate that they're trying to liven things up, but it's an awkward metaphor that doesn't fit all that well.

THE GOOD This course has a comprehensive view of the visualization phase of the analysis, and forms a process oriented mentality. There is a lot of inspiration and encouragement going around. I can say I learned quite a bit. THE BAD I can't say I'm confident working with Tableau. As usual in these courses, there is no theoretical explanation of Tableau's structure and functions. It was hard to follow along with the exercises, and sometimes I had to skip to the next. Which isn't like me. There was always some techincal issue preventing me from achieving the same result as the instructor. Maybe there is an update problem. I expected more exercises with Tableau anyway.

I think this course needs a bit more hands-on activities and visualization building examples. While there are a few basic examples with Tableau and Google Sheets, I would have appreciated a Google Data Studio (or Earth Engine) example or maybe an example demonstrating linking data dynamic data (living in a SQL database, Kaggle, or even GitHub) to visualization software. I would have really enjoyed additional best practice methods or savvy visualization techniques. Overall, I appreciated all the material in this course. However, I felt that the information on presentations and communicating to stakeholders was slightly too surface level, dry, and too lengthy.