This unstructured data is becoming a really important part of things that we do. Text is being generated all the time, at ever faster rates. Julia: “Text is increasingly a really important part of our work as people who are involved in data. Today, we’re looking at R, and Julia is a big name in this space, as is her collaborator Dave Robinson.” Both languages have devoted followers, and both do excellent work. It’s a great resource when you need to solve a coding problem or develop new skills.”Ĭurtis: “Now there are basically two main camps in data science: people who program with R, a statistical programming language, and people who program with Python, a high-level, general purpose language. Ginette: “Stack Overflow, where Julia works, is the largest online community for programmers to learn, share knowledge, and build their careers. My academic background is in physics and astronomy, but I’ve worked in academia, teaching and doing research, I worked at an ed tech start up, and I’ve made a transition now into data science.” Julia: “My name is Julia Silge, and I’m a data scientist at Stack Overflow. We just did, and now Data Crunch is back! To hear the latest from us, add us on Twitter, Today we hear from an exciting guest-someone who is on the cutting edge of data science tool creation, someone exploring and developing new ways to slice and dice difficult data.” Ginette: “We hope you’re enjoying some vacation time this summer. Check out for up-to-date API documentation, tutorials on SQL, and other query techniques, and much more!” Whether you’re already a frequent dataset contributor or totally new to data.world, there are several resources you can use to stay in the loop on the latest features, learn new skills, and get support. Discover and share cool data, connect with interesting people, and work together to solve problems faster at data.world. Ginette: “And you are listening to Data Crunch.”Ĭurtis: “A podcast about how data and prediction shape our world.”Ĭurtis: “Brought to you by data.world, the social network for data people. The last 50 years or so of pop songs, we have all these lyrics, so all this text data, and I wanted to ask the question, what places are mentioned more or less often in these pop songs.” Julia Silge: “ One that I worked on that was really fun was about song lyrics. For the full interview, listen to the podcast episode by selecting the Play button above or by selecting this link, or you can also listen to the podcast through Apple Podcasts, Google Play, Stitcher, and Overcast. Check out the book they’ve written called Tidy Text Mining with R.īelow is a partial transcript. Even more impressive than these finds, though, is that she and her collaborator, Dave Robinson, have developed some new, efficient ways to mine text data. When Julia Silge’s personal interests meet her professional proficiencies, she discovers new meaning in Jane Austen’s literature, and she gauges the cultural influence of locations in pop songs.
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