My first DATA VIZ!
I finally did it! I just published my first data visualization on Tableau Public - it's a simple treemap of the main social themes that have been popping up in art (books, music, movies) based on the Feb 2, 2019 NYT Arts section. You can see the data vis by going to my Tableau profile: https://public.tableau.com/profile/ioana.s2410#!/
This project was inspired by Nicholas Felton's Skillshare class "Introduction to Data Visualization: From Data to Design" which is an amazing class for thinking about data visualization projects as I'm starting out. He's got a lot of great information that's presented really well and it's taught at a great pace.
While this visualization is hopefully far from the best thing I ever do, I'm so happy it's out!!! Now that the first one is out of the way, my next step is to re-create some visualizations from the MakeoverMonday site. This is a great data vis resource because each week, an article and a data set get published on the site and people contribute their take on the visualization. The purpose of this exercise is that as I'm re-creating these projects, I can pick up on best practices and start incorporating them into my own designs. This learning technique is from Mike Locke, a UX design professional and instructor and I'm excited to try this out! Here's the video of him going through this below:
This project was inspired by Nicholas Felton's Skillshare class "Introduction to Data Visualization: From Data to Design" which is an amazing class for thinking about data visualization projects as I'm starting out. He's got a lot of great information that's presented really well and it's taught at a great pace.
While this visualization is hopefully far from the best thing I ever do, I'm so happy it's out!!! Now that the first one is out of the way, my next step is to re-create some visualizations from the MakeoverMonday site. This is a great data vis resource because each week, an article and a data set get published on the site and people contribute their take on the visualization. The purpose of this exercise is that as I'm re-creating these projects, I can pick up on best practices and start incorporating them into my own designs. This learning technique is from Mike Locke, a UX design professional and instructor and I'm excited to try this out! Here's the video of him going through this below:
The plan is to go through this process once a week for 10 weeks (April 1 - June 9th) by picking one week with an interesting article and data set, find one cool visualization and re-create. During this process I'll try to analyze why the creator made certain decisions (e.g. style, graph type, etc.) and hopefully pick up some great skills along the way. I won't publish them since they're not original work, but hopefully this will allow me the confidence to submit some of my own to MakeoverMonday!
nice
ReplyDeleteThe article is an encouraging reflection on completing a first data visualization project and publishing it on Tableau Public. Creating a treemap from social themes appearing in the New York Times Arts section gave the author an opportunity to move from learning about visualization to actually producing and sharing a finished project. I appreciate the honesty about being a beginner and the excitement that comes with completing that first milestone.
ReplyDeleteThe author also highlights the value of learning from structured courses and experienced practitioners. The Skillshare class by Nicholas Felton helped provide ideas for approaching visualization projects from both data and design perspectives. This practical, project-based approach makes Data Visualization Course a relevant direction for learners who want to build visualization skills through hands-on work.
The plan to recreate visualizations from MakeoverMonday is another interesting learning strategy because it provides opportunities to study existing work and identify visualization best practices. Rebuilding examples can help beginners understand design choices while gradually developing their own approach to presenting data. For learners who want to practice creating visualizations programmatically, Matplotlib Course offers a complementary technical direction.
ReplyDeleteThe author's approach of publishing an initial visualization, studying established examples, and recreating them as practice is a strong way to build confidence through hands-on learning. Each project can provide an opportunity to experiment with different ways of presenting information, evaluate design choices, and gradually improve visualization techniques. This project-based approach also makes Data Science Projects for Final Year a relevant direction for learners who want to apply data analysis and visualization concepts to practical projects.
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