Day 1 of 180 Days of Data Viz Learning

Day 1 of 180 Days of Data Viz Learning

I’m learning data visualization for 180 days straight because I need to JFDI.  See previous post.

Visualization Worked On or Created:

Decomposition of Visualization:

I do more than one of these a day on a notebook, but I want to make sure I share one as well that forces me to make more thought out assessment than my scribbled notes.  I do write my decompositions initially because writing helps me learn more actively.

The Food Capitals of Instagram:

  • What are the:
    • Variables: Food type in x-position, City on map, % of hashtagged photos on Instagram on map
    • Data Types: Quantitative # of Instagram photos of a hashtag
    • Encodings: Retinal variables (size), Opacity, Area
  • What works well here? One use to color on the maps and consistent locations.
  • What does not work well and what would I improve? Size overwhelms some of the smaller data points.
  • What is the data source?  Do I see any problems with how it’s cited/used?
  • Any other comments about what I learned? Extra commentary on each food makes for a much better narrative and user experience.

Code Learning:

Done Already before 180 Days:

  • Javascript Code Academy
  • Started Javascript Treehouse
  • Started Treehouse D3.js

D3.js Treehouse Intro to JSON

Three Takeaways:

  • JSON one of main formats to work with through APIs and Javascript
  • Primitives, Objects, and Arrays stored in JSON
  • Operations on Array

Data Visualization and D3.js on Udacity

  • Finished 1a Visualization Fundamentals up to 1b D3 Building Blocks
  • Three Important Takeaways:
    • Visual Encoding: Order of effectiveness: Position, Length, Angle, Slope, Area, Volume, Color, Density
    • Where D3.js lies in spectrum of tools
    • DOM as abstract representation of HTML source that gets created during page load and access with the Javascript API. DOM is a specification of an interface and a hierarchal object and has a tree structure due to nested nature of HTML. Common programming interface for HTML and XML documents.

Reading and Learning Data Visualization Theoretically/Critically:

Practical Rules for Using Color in Chart:                                    

Three Takeways:

  •  “Whenever you’re tempted to add color to a data display, ask yourself these questions: “What purpose will this color serve?” and “Will it serve this purpose effectively?” If the answer is “It serves no useful purpose” or “It serves a purpose, but something other than color or this particular color would do the job better,” avoid using it.”
  • Use soft, natural colors to display most information and bright and/or dark colors to highlight information that requires greater attention.
  •  Diverging palettes, which I prefer to call dual-ordered palettes, encode a range of quantitative values that are above or below some logical breakpoint in the middle. For example, a company’s profi ts can be displayed using a heatmap with a dual-ordered palette of colors: one set for profi ts and another for losses, with a zero midpoint.

Done previously to starting 180 days:

  • Reading Data Viz Theory (eg. Nathan Yau’s Flowing Data) and doing three Data Decomposition Exercises daily

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