Trying out Coursera’s Data Visualization Specialization

Anya Pfeiffer
Image from Coursera

In an effort to expand my data visualization skills, this week I decided to take some courses within Coursera’s Data Visualization with Tableau specialization, which you can check out here. A specialization in Coursera is essentially a series of courses that are aimed at giving a student proficiency in a particular topic — in this case, data visualization. This particular specialization contains five different courses, and Coursera suggests working on it for about six hours a week, which would put you at about 4 months to complete the whole specialization. Of course, because of the way that Coursera is structured, it’s certainly possible to do this much faster (or take more time, if needed).

In this article, I’ll take you through my experience with the courses, and I’ll highlight my personal praises and criticisms of the courses as well as Coursera’s structure in general. This is by no means an objective way to judge this course — I would highly encourage anyone interested in taking a Coursera course to, at the very least, take advantage of the free trial and give it a try.

General Notes

Before I get into the more specific structure and material of the courses I went through, I wanted to highlight some general notes about the structure of the course, as well as the Coursera interface, that I found helpful. Just for context, I haven’t taken any other Coursera courses so I’m not sure if these features/conventions are standard, but they did exist in this case.

The information is mostly delivered in the form of videos. Underneath each video, there’s a transcript of what’s said in the video, which I found helpful since sometimes I need to read something to reinforce or clarify an idea for myself. Another nice feature of this transcript is the ability to highlight, using your mouse, pieces of the transcript and save it to your personal notes on the course. I’m someone who has trouble focusing on a video or a lecture while taking useful notes at the same time, so for me the ability to highlight while still paying attention to the lecture was huge.

Within some of the videos, there are multiple choice questions embedded. For me, these were similar to iClicker or TopHat questions you might see in a traditional lecture. I felt they were helpful for reinforcing the information, as well as holding you somewhat accountable for actually paying attention during the videos. But, don’t worry if you zone out — from what I could tell, you get unlimited attempts on each of these questions, and if you’re trying to take the course for the certificate, they don’t count towards your final grade (only the projects and end-of-module quizzes do).

Finally, a note on pacing. While these courses are “supposed” to take four weeks each, I was able to finish the first two courses in about a day and a half — granted, I worked on this almost exclusively, but still. The ability of students to control the pace at which they complete the tasks is really helpful, as each individual can move ahead or slow down as needed. If you’re feeling motivated and comfortable with the material, I would definitely suggest moving a little more quickly than Coursera’s estimates.

Course 1: Fundamentals of Visualization with Tableau

As with anything, the first course in this specialization, entitled Fundamentals of Visualization with Tableau, definitely had some strengths and weaknesses. Here are my personal highlights and lowlights for the course:

Highlights:

Week 1 video — What is Data Visualization and Why Do We Do It? — this video was a really helpful way to explain data visualization to someone who has no experience with the concept. I found it helpful, even though I do have some limited experience, to have some of the terms defined in a more explicit way than I had in the past.

Podcast interview — at the end of the first week, there was a podcast interview with Dr. Ben Schneiderman. It was an optional part of the course, which I really liked, as it gives students a way to supplement/personalize their experience if they want.

Multiple choice questions — the multiple choice questions embedded in the videos were a personal favorite feature for me. They really helped reinforce the material, draw my attention to the more important points, and held me accountable for actually paying attention to the videos.

Lowlights:

Week 3 project instructions — the expectations for this project were super vague, which was really frustrating. It shouldn’t have been difficult, but it took me longer to figure out the difference between two parts of the project than it took to actually complete the project — not usually what you want. Luckily, by reading through the discussion that students before me had, I was able to figure it out.

The entire book inside week 4 — I thought this book was a cool resource, but it was intimidating to click to the next part of the module and see an entire textbook — without the promising parentheses with a smaller set of pages next to it. Having access to the whole book is nice, but if the whole textbook was assigned for the module I wished it had been split up throughout the module, or assigned at the beginning instead of smack in the middle of the other materials.

General pacing for the videos — this is a little more personal, but the instructors in the videos spoke a little slowly for my liking. However, I could see this pacing being a pro as well, and luckily I was able to control this myself by just watching the videos at 1.25 speed.

Below is an example visualization from Tableau Public, that was used to explain some of the concepts as well as explore the Tableau interface. It maps the 1854 Cholera outbreak.

Image from Tableau Public

Course 2: Essential Design Principles for Tableau

This course was a lot more abstract than the first, and didn’t include quite as many examples where you’d need to follow along. This was the tail end of my one and a half day sprint to finish the first two courses, and I think as a result, I found some of the material a little repetitive, but good overall.

A screenshot of a Tableau walkthrough.

Highlights:

Ethics — I really liked the section of this course that talked about being honest with your visualizations (for example, not skewing your bar graph’s axes to make it look like there’s a more significant difference between two bars). I also thought this was a good way to break up some of the more technical material that might be tedious for a newbie.

Week 4 Project — I felt that this was a pretty well designed project — in my opinion, the instructions were clearer than those in the first course, and I liked the added dimension that the persona added to the project.

Lowlights:

Readings — this is more about the “formatting” of the readings, rather than actual content. While the previous course had readings listed as their own “task” in the sidebar, this course had readings located within a dropdown menu underneath the videos. I think this format is fine, as long as it’s obvious that readings are located in that menu. That said, the readings were pretty useful.

Course 3: Visual Analytics in Tableau

I found this course to be the most useful information wise, but the least engaging in terms of delivery. Unlike the first two courses, it didn’t have the same level of discussion questions or multiple choice check ins. Despite this, there was tons of useful information in this course about picking the right graph for different scenarios, as well as mini tutorials for how to create basic types of each chart using Tableau’s “show me” feature. It also included a breakdown of how Tableau handles dates as well as an introduction to using maps, which I found helpful.

Screen capture of a Course 3 video

Highlights:

Week 1 Tableau Charts video — while this video was long, it was really helpful to step through the “show me” feature of Tableau. Having each type of chart explained, along with a quick explanation on how to do it and when you might use it was really awesome.

Lowlights:

Only videos — This course was pretty much only videos — they were useful, sure, but pretty tedious to get through without any breaks for readings/multiple choice questions/activities. The only break from videos were the projects at the end of each week.

Conclusion

The way Coursera delivers information is effective in a lot of ways, but I don’t think this specialization is necessarily useful for everyone. Here are my (again, completely subjective) recommendations about the courses that I took:

Take these courses if you have absolutely no technical experience and are looking to learn the basics of data visualization. You’ll get a pretty step by step walkthrough of a software that’s pretty easy to use once you have the basics down.

Don’t take these courses if you have technical experience or have used Tableau before. It might be worth looking in to particular courses within the specialization or even particular modules, readings, or activities that suit the particular skill you’re attempting to polish, but some of the material might feel redundant.

In addition, I’d suggest that whether you plan on choosing one course within this specialization to explore or you want to do the whole thing, I would advise not trying to do it in quite as much of a marathon as I did (imagine that). If you can handle that, kudos to you, but in terms of truly getting the most out of each module, it might be more effective to slow it down just a tad. That said, I think it’s totally reasonable to move faster than Coursera estimates you should.

Regardless of whether or not the data visualization specialization is appealing to you, there’s no doubt in my mind that Coursera, if used properly, can be a super effective way to learn something new.

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