Day 66 of 180 Days of Data Viz Learning #jfdi

I’m doing some form of data visualization learning for 180 days because I need to #JFDI.  See post explaining how and why I’m doing this.

This process has taught me so far about learning, learning quickly, and deep learning.  Definitely feel like who I am as a professional and paradigm around it has shifted a bit to be a bit more design-centric and builder-centric.  I’d say before I was basically a two-headed marketing and technology monster.

One theme this weekend is that going deep and going back to basics isn’t that far some each other.  I’m a generalist – I found my talents lie in tying a lot of different moving pieces together.  I got to be a little more specialist now, especially since I’m now coding most of the time in a way I’m not familiar with.  Even though I feel deeply knowledgeable in aspects about expressing data and exploring it both in terms of practical approach informed by experience, I’m weak certain tools of execution.

I’ve learned that at least in data visualization online, I’ve noticed dominant strands -> visualizations created by people more in tech/business, visualizations by people in journalism, and by those in academia.  I think I’m at a point where I can probably look at a visual and guess who did it based on approaches and shortcomings.  Each nexus has its strengths and weaknesses, which can be a huge blog post on its own on a later date.

To do a quick and dirty:

  • Business/tech big on flash, huge variance on ability in-depth data analysis and expression, but data is most suspect out of three buckets.
  • Journalists, high on accuracy, medium on deep data analysis, and high on expression.
  • Academics, high on in-depth analysis, either very good or very bad design.

I think a lot of this just has to do with skillsets, eg good coders without research or design experience. Business analysts who are working with messy data.  Journalists who need to put together work for a mass audience.

How this applies to me, is the two weeks for me are going to be very back-to-basics.  Last week was busy for me, and I had the sniffles so I was operating on fumes.  I’m going on a trip for a week to Taiwan this Friday, but I still want to devote more time.

This week I’ll continue doing my reading of Nathan Yau’s book, but I’m also going to be stepping away from direct work in D3 in terms of tutorials and only do some review at work, and of course, work in it for a lot of the day.  Instead, I’m going to go through Web Dev and Javascript Tutorials on Treehouse because I need review, and my knowledge was never that deep anyway.

Onward!

Reading and Learning Data Visualization Theoretically/Critically:

Reading Nathan Yau’s book Data Points – Visualization that Means Something

p. 114-134 Putting it all together

Three Takeaways

  • Point to point line charts showing change over file with different color hue or just black and white fills can provide a visual cue to look left to right – help a user go through time series p. 129
  • Shaded maps (heat maps as most people know them) are actually called choropleth maps, because heat maps were developed specifically to visualization 2D data. p130
  • The challenge of more data is more visualization options, get to know the methods and play with them so you can get better p. 133
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