My guest today is Ali Torbin. She's one of the leading data information designers in the world. She's taught data vis to companies like Google, Moderna, and created data visualizations for premier publications like The Washingtonian and Axios. But she didn't always start that way.

You see, Ali started as just a normal data analyst for a company in Washington, DC. I was writing SQL queries, I was testing software, and there was nothing wrong with it on paper, but it just felt like there was something more fulfilling for me somewhere. And that's when I

started expanding into the world of data visualization. And Ali was smart, like many of you listening, but didn't have all that much experience. And she was also looking for a flexible job as she was a new mom of two little ones. So she knew she had to network. And the networking kind of came in a strange medium. I thought, well, maybe I can start my own podcast, and I can learn about data visualization, make a portfolio, start applying to jobs, and it totally worked. So in this episode, you're going to hear Ali's full story, as well as her advice on how to become a data vis designer, and how it can work for you to stay tuned. Welcome to the Data Career Podcast, the podcast that helps aspiring data professionals land their next data job. Here's your host, Avery Smith. Ali, thank you so much for joining us on the Data Career Podcast. We're so happy to have you.

Oh, thank you so much, Avery. Happy to be here.

Yes, and I'm so excited to be talking about something that I don't think gets enough credit because data visualization is so fascinating and so big in the data world. And a lot of people just think that, especially when you're just getting started, which is totally fine to think that like, oh yeah, data vis equals, I make a chart in Excel, or I make a dashboard in Tableau or Power BI. And I'm excited to just kind of take some of your insight on like, there's actually a lot more than that, right? And the other thing I'm excited to talk to you about, one of the things that you talk about in your book, chart spark here, is like, there's room for creativity in the data world, right? Is that true? Yes,

there is, yes. We think that to be creative, that's an artist, right? That's always what I thought. Oh, are you creative? No, I'm not an artist. Mm, that's not really the definition of creativity. The way that I define it, it's the ability to generate new ideas or remix existing ideas and make sure that it's solving a problem and you're solving an ethical problem. That's we don't need anymore unethical problem solving, but mostly that it's solving a problem and you don't have to be a genius either. We're not just thinking, oh, I'm not creative because I'm not Einstein. You can do little creativity acts that build on each other. Like making a bar chart in Tableau, you have never created anything in Tableau before and you learned it, that's a creative act. And then you build on that. Like maybe you create the bar chart and connect it to a map and now they filter it together. That's what researchers Kaufmann and Beggetto call little C creative acts. So learning something new, that's a mini C creative act, little C creative acts as like experimenting. And then pro C creative acts is when you are bringing those creative acts together and for a professional purpose, you're solving problems at work. It's useful to other people. And then you have the big C creative acts and that's like long lasting impact on the world, Einstein, but that's what we have in our heads. But that is not what we should be aiming for. We should be thinking about mini C, little C pro C and bringing our creativity through our data visualization or just regular data analysis work and creating things that make us feel more fulfilled and make an impact on the world and make us stand out. So yes, you can be creative in the

data field.