Data Crunch

We now know black swans exist, but Europeans once believed that spying one of their kind would be like stumbling across a unicorn in the woods—impossible. Then, Willem de Vlamingh spotted black swans in Australia, and this black bird, which once represented the impossible to Europeans, shifted to represent the unpredictable. One company now dons the name "Black Swan." Find out how it aims to predict what we currently consider to be unpredictable. Transcript Ginette: “Submerse yourself in early 1600s London culture for a minute. Shakespeare’s alive and in his late career. The first permanent English settlement in the Americas just happened. Oxygen hasn’t been discovered yet. But a lesser known cultural idiosyncrasy has to do with a large white bird, the swan. In Europe, the only swans anyone had seen or heard about were white, so of course, in their minds, a swan couldn’t be any other color. From this concept, a popular saying develops, originally stemming from a poem. You use it when you want to make a poin

How did one boy's stuffed yellow elephant permanently intertwine itself in history? What is a data scientist? Why is right now the golden age for data science? We take a crack at all three of these questions—the second two, with the help of Gregory Piatetsky-Shapiro and Ryan Henning. Transcript Ginette: “Over the past few years, we’ve seen these news flashes: “An article in Harvard Business Review in 2014, titled: Data Scientist: the Sexiest Job of the 21st Century “Mashable’s article in 2015: So You Wanna Be a Data Scientist? A Guide to 2015’s Hottest Profession “Business Insider, 2016: Data Science was the #1 Profession as Rated by Glassdoor “A data science industry observer, KDnuggets, 2017: Data Scientist: Best Job in America, Again, which cites the most recent Glassdoor report outlining the very top jobs in America: “It turns out, four of the five top US jobs deal with data. In descending order, we find data scientist, devops engineer, data engineer, and analytics manager.” Curtis: “With four out

From a small building in Pennsylvania to widespread usage across the world, we track the compelling story of one of the greatest technological innovations in history, setting the stage for the age of data science. Ginette: “I’m Ginette.” Curtis: “And I’m Curtis.” Ginette: “And you are listening to Data Crunch.” Curtis: “A podcast about how data and prediction shape our world.” Ginette: “A Vault Analytics production.” Ginette: “Today our story starts at a business building.” Curtis: “The building is in Philadelphia, Pennsylvania, on Broad and Spring Garden Streets to be precise. Envision the late 1940s.” Ginette: “You see a man absorbed in thought entering the building, and you decide to follow him in.” Curtis: “When you walk through his office, you find some bright engineering minds working on a fairly new startup in town: the Eckert-Mauchly Computer Corporation, or EMCC. It turns out, this is the very first large-scale computer business in the United States.” Ginette: “While this business enviro

She isn’t your typical English girl from the early 1800s. She’s a girl who, because of her fortunate and unfortunate family circumstances, ends up perfectly situated to become part of something that will revolutionize the world. Ginette: “For many reasons, she isn’t your typical English girl from the early 1800s. She’s a girl who at one point examines birds to discover their body-to-wing ratio so she can invent a flying machine and write a book about it. These are goals that show mathematical skill, creativity, and initiative. She’s also a girl who, because of her fortunate and unfortunate family circumstances, ends up perfectly situated to become part of something that will revolutionize the world.” Ginette: “I’m Ginette.” Curtis: “And I’m Curtis.” Ginette: “And you are listening to Data Crunch.” Curtis: “A podcast about how data and prediction shape our world.” Ginette: “A Vault Analytics production.” Curtis: “In our last episode on the history of data science, we talked about the origins of charts a

Pirates in folk stories and popular movies conjure up strong imagery: eye patches, Jolly Rogers, parrots, swashbuckling, scruffy voices that say “Aye, Matey.” But what do the lives of successful pirates look like today? And what's being done to stop them from plundering and smuggling our ocean's precious resources? World Wildlife Fund's project Detect IT: Fish takes aim at these pirates and other illegal actors with this cutting-edge project that reduces a time-consuming tracking process from days to minutes. Ginette Methot-Seare: “After nearly 15 years of lucrative, illegal activity, he was caught and convicted. The judge in this key case stated that his business activities were an ‘astonishing display of the arrogance of wealth and power.’ He destroyed evidence, and while under investigation, even hired a private I to follow an agent around. After serving prison time, the main perpetrator and his accomplices were ordered to pay 22.5 million dollars in restitution to South Africa for the damage they had don

The history books teach that slavery ended, but it still exists; it’s just morphed its form—different commodity, different location, but same abuses. The commodity is seafood. The location, Southeast Asia. The abuses, forced servitude with all its ugly associations. Some people make a substantial living off illegal, unregulated, and unreported (IUU) fishing, which fuels a dark underground. How is big data angling to stop it? Find out in our next two episodes. Transcript: Michele Kuruc: “People who were seeking better lives and, and coming to look for work were kidnapped by unscrupulous dealers, who forced them into lives we can’t even imagine.” Ginette Methot: “I’m Ginette.” Curtis Seare: “And I’m Curtis.” Ginette: “And you are listening to Data Crunch.” Curtis: “A podcast about how data and prediction shape our world.” Ginette: “A Vault Analytics production.” Ginette: “Welcome back to Data Crunch! We took a bit of a break over the holidays, and we hope you were able to too. “So upward and onward to

Who were the people pushing the limits of their time and circumstances to bring us what we know today as data science? We examine what motivated them to do their important work and how they laid the foundations for our modern world where algorithms and analytics affect everything from communications to transportation to health care—to basically every aspect of our lives. This is their story. Transcript: Ginette: “She was obsessed with her failure—she thought she hadn’t done enough. And it didn’t matter that the public saw her as a heroine. So she ended up writing an 830-page report where she employed some power graphics, and this paired with her other efforts ended up changing the entire system.” Ginette and Curtis: “I’m Ginette, and I’m Curtis, and you are listening to Data Crunch, a podcast about how data and prediction shape our world. A Vault Analytics production.” Ginette: “In our last three episodes, we have just thrown you into the middle of data and prediction and the explosion of data scienc

When breakfast food takes on hurricanes, who wins? For another interesting take on the Waffle House Index, see this article the Fivethirtyeight blog, which they posted December 6, 2016. Curtis: “I love waffles. I fill up each of the little squares with the precise amount of syrup so that each bite is a perfect distribution of syrupy goodness.” Nathan: “I love owl-shaped waffles.” Tiffany: “The kind you get at a hotel when they serve you those free breakfasts—they’re just perfect.” Lily: “I love waffles with strawberries.” Vince: “Liège waffles—Belgian waffles were pale in comparison. They’re sugar clumps in the shape of pearls, and they put this in the batter, and it doesn’t dissolve out, and they taste really good. I didn’t even need to add syrup.” Ginette: "I'm Ginette, and I’m Curtis, and you are listening to Data Crunch, a podcast about how data and prediction shape our world. A Vault Analytics production." Curtis: “Today we’re talking about hurricanes, waffles, and predictions.” Ginette: “It hap

Imagine you have to leave your home immediately, and you have little time to grab anything to take with you. You don't know where you are going—you just know you have to flee for your life. Many people face a similar situation—one in every 113 people on the earth, in fact. There are 65 million people living in a state of limbo, and they don't know what's going to happen to them, but they do know they can't go home. After losing their homes, often their loved ones, and sometimes their identity, they desperately hope for safety and a new home. This episode is where data science meets refugees. Transcript: Hadidja Nyiransekuye: “It wasn’t until I started having as a teacher and a principal of a school when people come in the middle of the night to come attack my house. That’s when I decided I think I need to run again.” Ginette Methot-Seare: “I'm Ginette Methot-Seare, and you are listening to Data Crunch, a Vault Analytics production.” Hadidja: “Just think about something threatening you. Your first reaction

What if one day, out of the blue, you find yourself sick—really sick—and no one knows what's wrong. This is a podcast about a sleeper illness and what one team of data scientists led by Elaine Nsoesie is doing to reduce its reach. Sam Williamson: "It felt as if I were on some kind of hallucinogenic drug. I felt really, really hot. Really cold again. The room started spinning. I got tunnel vision. I was about to black out." Ginette Methot: "I'm Ginette Methot-Seare, and you are listening to Data Crunch, a Vault Analytics production. Today we're going to talk about something that could affect you or someone you love if it hasn't already." Shawn Milne: "It still is a pretty vivid memory for me just because it was such a, such a terrible thing." Ginette: "This is Shawn Milne." Shawn: "Both of us just booked for the bathroom because we were both throwing up." Ginette: "He's describing a sickness that both he and a friend suffered from." Shawn: "On the way home, we had to keep pulling the car over, and we