Data visualization is a muscle. The more you work it, the stronger you become. Explore some interesting examples of people performing daily, or regular, practice doing data visualization and how that helped them improve their data visualization skills.
(bright music) - Practice makes perfect. Put your 10,000 hours in, do the work. How many different ways are there to say the same thing? If you actually do something again and again, put in the time, practice the skills, you will improve. This is not a revolutionary idea. So, what's the lesson here other than practice? I'd like to talk a little bit about how to practice. First of all, focus in on something that interests you. This can take many forms. For instance, maybe you're passionate about solving a social problem like homelessness. Perfect. Go find data about homelessness, dive in and discover and tell stories about the insights in the data. Your passion for the subject matter, will lead you down some really interesting paths. Alternatively, if you don't have a topic that drives your passion, but you absolutely love to draw, then do all of your practicing, making hand drawn visualizations. Or maybe you love to read Russian literature. Take that passion, and explore Russian lit, using the text as data, and figure out ways to create data visualizations of those insights. You get the gist. Whatever you're passionate about, will make it easier to learn new things and improve your skills. Second, practice a variety of skills. Maybe on one project, you focus on storytelling, organizing a narrative and writing a compelling explanation of the story. On another, you focus on, creating innovative visualizations of the data. On another, maybe you focus on doing text analytics, rather than a numbers driven data exploration. And on another, you tackle a new technology to execute on your ideas. By practicing a variety of things, you obviously develop new skills, and you'll also keep from getting bored. Third, work on a deadline. A deadline will keep you honest, constantly moving toward a goal. There are a bunch of ways to impose a deadline or a deadline-like experience, you can just declare that you're going to practice x minutes per day or week, or you can enter data visualization challenges like makeover Monday, or you can choose projects like visualizing an event, or calendar driven datasets, with the idea of publishing them in a time appropriate way. For instance, if you choose to visualize New Year's Eve related data, you kind of want to finish by the end of December, right? The appetite for that content will be pretty low in April. Practice makes perfect. Choose a process and strategy for practicing this craft, that you can uphold over time. You don't get points for promising to practice, you only gain from actually doing it, for actually putting in your 10,000 hours, for doing the work. Next up, we'll talk to Amy Cesal, who has put in the hours in many ways, one being creating DayDohViz, data visualizations made out of Play-Doh, which was a regular practice for her and helped her develop some interesting skills and insights, into this work.