Data Crunch | Big Data | Data Analytics | Data Science

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 done.” Curtis Seare: “Who was this man? Arnold Bengis, a modern-day pirate.” 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: “Believe it or not, these episodes take hours and hours of hard work to produce, and the success of this show depends in large part on the listener reviews and ratings we get. If you like what we do, the best way to support us is to go to iTunes, Google Play, or your favorite medium for getting the episodes, and leave us a review. “If you’re willing to do that, a big thank you in advance, and a big thank you to those who already done it.” “At the end of our last episode, we promised you the story of one of the biggest pirate busts in history, and we will deliver, but before we go on, if you’re new to Data Crunch, you may want to start with the last episode, which will give you more background and context. “By some accounts, this is what happened: Arnold Bengis became incredibly wealthy after growing a business in South Africa. He had a house in Bridgehampton, New York, worth several million dollars, an apartment in the Upper West Side of Manhattan on the 41 floor, and a house in Four Beaches, an exclusive neighborhood in Cape Town, South Africa. “His 6,000-plus square foot Bridgehampton house, a large Spanish-tile stucco villa, overlooked the beautiful Mecox Bay to one side and the Atlantic ocean on the other. His six bedroom, seven full bathroom single-family home had what you’d expect to find at a palatial place: a well-manicured golf green; a luxurious pool; large, well-decorated rooms with chandeliers, and expensive furniture. When the house last sold, it went for 10 and a half million dollars. One of the agents of the National Oceanic and Atmospheric Administration, or NOAA, who investigated Bengis’s case even said he was in partial awe of the lifestyle Bengis was living, which was supported by illegal fishing business. “Bengis held his money, both personal and business, in a highly complex network of trusts and asset havens. The money was scattered abroad in many different places, like Switzerland, Gibraltar, Jersey Islands, and Britain. While authorities didn’t know everything about his money, what they did know was that he had vast assets. For example, in just one year, he deposited $13 million into one of his accounts. His lawyer said that one of his several trusts was worth more than $25 million, according to the book Hooked: Pirates, Poaching, and the Perfect Fish. “I know what you’re probably thinking: ‘How did this man make so much money from illegal fishing?’ We told you in our last episode that IUU fishing rakes ...

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 2017. What are we up to this year? We’ll be finishing our data science history miniseries for you, and we’ll be meeting some really cool people from KDnuggets, Galvanize Austin, and Datascope in Chicago. But before we do those episodes, we have to pivot because with major recent developments, this particular episode deserves to come out now. “The lives we can’t even imagine look like this according to the Associated Press. One Burmese man left his village when he was 18 years old. He followed a recruiter who promised him a construction job. When he arrived in Thailand, his captors held him with little food or water for a month. He was then forced onto a fishing boat. He was told that he was sold and would never be rescued. In that fishing environment, sometimes he worked 24-hours a day. He and his fellow fishers were whipped with stingray tails and shocked with electric devices. They were told during their time fishing that they would never be let go, not even when they died, and men in his similar situation were sometimes sold from ship captain to ship captain. “If they tried to escape the work, they were locked in cages on remote islands. In the 22 years he was away from home, he asked to go home twice. The first time he asked, the company official chucked a helmet at his head, which left a bloody gash that he had to hold closed. The second time he begged to go home, he was chained to the boat deck for three days in the blistering sun and when the night came, it was rainy, and he could do little to protect himself from it. During that three-day period, he had no food. He amazingly fashioned a lock pick and unlocked his shackles. He knew if he was caught, he’d be killed, so he dove into the water in the cover of night and swam ashore, hiding for his life. “You might ask why he didn’t go to local officials. The answer is he couldn’t because they might sell him back to the ship captains. So after eight years in the jungle hiding from the fishing companies, he finally got to go home because of the AP’s reporting. This is modern-day slavery. Every year, thousands of people are tricked or sold into this type of slavery in order to catch fish for lucrative markets. “If you’ve ever read Solomon Northup’s gripping autobiography, Twelve Years a Slave, the similarity is eery. They are both free men who are initially unknowingly abducted. They’re shackled, beaten into servitude, and forced to work in harsh conditions for many, many years. Both are desperate to go home to their families, and both experience miraculous escapes from tyrannical systems. But unfortunately, not everyone escapes. “This is a huge problem, and it’s frequently linked to illegal, unregulated, and unreported fishing,

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 science. And some of you have had some questions, like, How did data science become a thing? “In the next three episodes, we’re doing a miniseries where we’re going to address some of these questions, and I think you’ll find it very interesting. Our story starts with an impressive woman. “It’s 1854. It’s the Crimean War, and a woman shows up at a hospital to help. She finds horrifying conditions. To paint an accurate picture for you, here’s a little bit of what she found: the sewage and ventilation systems were broken; the floor was an inch thick with waste—probably human and rodent; the water was contaminated because, come to find out, the hospital was built over a sewer; rats were hiding under beds and scurrying past, as were bugs; and the soldiers’ clothing was swarming with lice and fleas; and on top of that, there were no towels, no basins, no soap, and there were only 14 baths for 2,000 soldiers. Keep in mind this was 20 years before Pasteur and Koch spread Germ Theory. “So she and the 37 nurses that she brought with her set to work, and they did their best to clean up the hospital and help the soldiers. Eventually, because of her, the government sent a sanitary commission. They flushed the sewers; they improved the ventilation. And this helped the situation dramatically. In the end, she reduced the death rate by two thirds. “But Florence Nightingale went home feeling like she had failed, which you’ll remember we mentioned right at the beginning of the podcast. She felt a lot of soldiers had died needlessly. This drove her to write her famous 830-page report. And she ended up working with lead statistician William Farr, who actually helped invent medical statistics. He would say to her, ‘We don’t want impressions, we want facts.’ And working under that type of context, she gathered vast amounts of complex army data and analyzed it to find something rather shocking: 16,000 of 18,000 deaths in hospitals were not due to battle wounds but to preventable diseases spread by poor sanitation.” “So these statistics completely changed her understanding. She thought the deaths were due to inadequate food and lack of supplies, but after the sanitary commission came in, she noticed that the mortality rate dropped significantly. So as Florence prepared her report, she was afraid that people’s eyes would glaze over the numbers and that they wouldn’t grasp the significance of what she was trying to say. So she came up with a clever way to present her data: she ended up using graphics, in particular what she’s known form the rose chart, to convey her message.” Curtis: “Nowadays, charts are everywhere, but back in her day, the idea of creating a picture that was defined by certain data points was not very common,

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. Transcript: 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 happened in 2004. Charley, Frances, Ivan, and Jeanne were four aggressors. With the group’s combined strength, they wrecked their victims. First, Charley attacked and was the most destructive. Frances followed quickly behind with a much weaker pummel, but, being so quick on the heels of Charley, the attack was effective. Then came Ivan with an unexpected one-two punch. And finally, Jeanne forcefully hit the same spot as Frances—but with much more intensity. “To some, this wrecking ball of an attack is known as the Year of the Four Hurricanes. These four hurricanes ruthlessly shredded Florida’s east coast, west coast, panhandle, and interior in about six weeks, leaving $29 to $41 billion in damages. As a point of comparison, if Google had to cover these costs, it would take two to three years of the organization’s net income. Next to Hurricane Andrew, (the most destructive hurricane in US history at the time)—Charley claimed second-place that year. “Charley obliterated mobile homes, savaged houses, knocked over water towers, caused the collapse of carports, obstructed roads by littering them with large trees and power poles, blew over semi-trucks, crushed large trailers, and rendered areas unrecognizable. “We spoke with a couple that experienced a hurricane first hand, and their ordeal sounds harrowing.” Melody Metts: “I don’t think we expected anything that we found when we came back. You couldn’t even recognize where you were.” Ginette: “Christopher and Melody Metts lived within twenty miles of Homestead, Florida, where Hurricane Andrew hit with full fury.” Christopher Metts: “There was nothing taller than the first floor. Any tree, any light pole, any anything that might have been higher than the first floor of a house was completely gone. Anything that would indicate where you were—a street sign, a light—it was all gone as far as you could see.” Ginette: “Like most south Florida residents, they didn’t think much of the storm predictions.” Christopher: “We saw it, and the predictions for it for many days. “Because we were in south Florida and because every hurricane season that comes along has scares that could be very devastating but it’s a near miss or it turns at the last minute, you get into a pattern of they cry wolf too often and you’re lulled into a sense of ‘well not this time.’” Ginette: “While this was their initial feeling, eventually the predictions became serious enough that the authorities issued an evacuation order, so the Metts prepped their house for wind damage and drove to Orlando with seven chil...

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 would be to duck away from the noise or from whatever is threatening you. Now think about somebody coming with a gun or with a machete, threatening not only your life but the life of your loved ones. You run, you run. Everybody does.” Ginette: “And that’s exactly what Hadidja Nyiransekuye did twice.” Hadidja: “The first time I run, I run because I needed to run.” Ginette: “She was fleeing from bombs.” Hadidja: “It was a mass exodus. Everybody was running, so we run like everybody else.” Ginette: “Hadidja had to flee in her PJs with four children. One of them, a baby on her back.” Hadidja: “My little girl, Lydia, was eight at the time, and I had two of my nieces.” Ginette: “Her husband, who was imminent danger, fled first. And her boys also ran before her.” Hadidja: “It was hot. We were thirsty and hungry. And these young people were perched on . . .” Ginette: “pickup trucks” Hadidja: “And they would say, ‘Keep moving, keep moving! There’s a nice place called Mugunga; that’s where you’ll get food and you’ll get water and you’ll get shelter. And I remember saying to myself, ‘People are dying of Cholera, and I’m going to Mugunga on foot—like 50 miles?’ I just didn’t think I was going to make it.” Ginette: “As a child, Hadidja had polio. Everyone one in 200 polio cases leaves its victims permanently paralyzed. For Hadidja, while her virus didn’t paralyze her, it left her disabled. She walks with a cane and a leg brace.” Hadidja: “At the time, I actually ended up at the Center for People with Disability in the Congo because I had been treated there in my teens. And of course, you just wished people would just let you spread your mat or something you have on their door so you can spend the night there. But they were asking us to get out of the city, to go to that place where they were going to be building refugee camps, so in those conditions, you actually, you hear what other people are saying. Well you just follow because it’s not like you have a choice. Nobody knows where they are going when they are refugees. That’s why they’re called forced migrants.” Ginette: “Let me go back and fill in some holes for you. Hadidja’s story starts . . . ” Hadidja: “in the town of Gisenyi. That’s where I was born and raised.”  Ginette: “Her town is right inside the border of Rwanda.” Hadidja: “It’s at the border of former Zaire, now Democratic Republic of Congo.”  Ginette: “As she grew, she gained an education, became involved in women’s movements, and taught modern languages with an emphasis in applied linguistics. During that time, she married her husband, and they had four children. But then in the 1990s things became precarious i...

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 were just both throwing up on the side of the road. It was absolutely terrible. We were just both up all night just throwing up. Just so beat." Ginette: "While Shawn's experience lasted about 48 hours, Samuel Williamson, the person you heard speak at the beginning of our podcast, had one that lasted for about a month." Sam: "I did go to a doctor for it after a while. They convinced me to go to a doctor. He in fact told me that my stomach was just tired, which I thought was a very strange diagnosis. So he suggested that I don't eat anything for a week. I think I lost about ten to twelve pounds in the first week, and so I went a week without eating anything, and came back a week later, and he asked me if the symptoms had gone away, and I told him 'no, they were about the same,' and he said, 'okay, well you can't eat anything else for another week.' I went about three days and then pigged out." Ginette: "While everyone's body reacts differently to this type of sickness, stomach pain was one symptom that everyone we interviewed described." Amy Smart: "I remember at one point, lying on my couch in excruciating pain, and thinking, ‘this is like having a baby, only with a baby, I know it's going to end.’"  Ginette: "Amy had two little girls when she got sick, and she became so ill and weak that she couldn't take care of them. Fortunately, her mom lived nearby and could take her girls during the day, and her husband was able to stay home from work to take care of her." Amy: "I couldn't, I couldn't eat. I wanted to because my body was so depleted, but I couldn't drink. I couldn't keep anything down. We went to the ER because I was so weak, and they put me on IVs and gave me morphine for the pain." Ginette: "But for Amy Smart, the person speaking here, things got a lot worse." Amy: "All that was coming out both ends was blood. And I remember feeling like, 'this is what it feels like to die.'" Ginette: "Amy described to me that it literally felt like life was leaving her body." Amy: "I didn't know when it would end, when I would feel better again. If it would take days or weeks or ever. I remember thinking, 'I'm so glad it's me and not one of my little kids' because I don't know how they would have survived it.'" Ginette: "Now put yourself in her shoes for a second: you're sick and only getting worse. When you go to the doctor, the doctor isn't sure what's wrong." Amy: "They first thought it was stomach flu, then maybe Giardia, then maybe salmonella, and then they cultured it and found I had E. coli." Aside: "E. coli contamination. Possible E. coli contamination. E. coli contamination." Amy: "By then,