Data Crunch

What does it take to become a data scientist? We speak with three people who have become data scientists in the last three years and find out what it takes, in their opinions, to land a data science job and to be prepared for a career in the field.Curtis: We’ve talked a lot in our recent episodes about all the interesting things you can do with data science, and we’ve only talked a little bit recently about what it actually takes to get into the field, which is a topic that a lot of you have reached out to us and asked us to cover in a more thorough way. So today, we’re taking a broader approach on this topic by talking to three data scientists who have become data scientists in the last three years. You’re going to be able to hear all the details of each of their three journeys, how they got started, how they landed their jobs, and what their best advice is for getting into the field, and this will give you a broad view about how to get into data science from three people who have actually done it.Ginette: I’m Ginette.Curtis: And I’m Curtis.Ginette: And you are listening to Data Crunch.Curtis: A podcast about how applied data science, machine learning, and artificial intelligence are changing the world.Ginette: A Vault Analytics production.Ginette: Here at Data Crunch we’ve been hard at work developing a technology that allows executives and business leaders to gain insight from their data instantly—simply by talking to the air. We hook up your data to an Alexa device with custom skills built in to understand the questions you have about your business - and give you answers. Figure out sales forecasts, marketing performance, operational compliance, progress on KPIs, and more by just talking to Alexa. We are officially launching the product this week and have room for three initial customers—if you're interested, head over to datacrunchcorp.com/alexa or datacrunchpodcast.com/alexa (both work), and book some time to chat with us. We’ll assess if your company is a good fit, and if so, we look forward to working with you!Tyler Folkman: My name’s Tyler Folkman. I've gotten into data science in kind of a strange route to be honest. I did my undergrad in economics, actually originally thinking to get into computer science, but for some reason, I had this thought that computer science was going to get outsourced; I don't know if that was a thing, but I think people back in the early 2000s were talking about computer science getting outsourced, so I thought about business, which ended up begin economics, which I really liked, and then ended up doing economic consulting, which is, basically in usually large litigation cases, lawyers hire economists to value damages, so for example, when Samsung and Apple were suing each other, I worked on the Samsung side to help value how much they might sue Apple for, for patent infringement, and a lot of that involves statistical analyses, data analytics, econometrics as economists would call it. And I got really interested in just this idea of data being a really powerful tool for making decisions and coming to conclusions, and so I started hearing about machine learning on the Internet, kind of dabbling with Python, which at the time, I was a Windows user, and it was a huge pain to get Python installed, but I kind of got it up and running, played around with things like SciKit learn, read some blogs, and really got into machine learning and found that it was really housed more in the computer science department at that time, and just kind of decided to apply to some computer science departments and was lucky to get in at University of Texas at Austin and do some studies there, join a machine learning lab and got to do some work at Amazon. Really got a really good set of experiences to kind of help me learn how to be both a programmer and a machine learning person, a little bit of statistics, and jumped straight from there over here to Ancestry and was luc...

Few things are as controversial in these perilous times as Donald Trump's Twitter account, often laced with derogatory language, hateful invective, and fifth-grade name-calling. But not all of Trump's tweets sound like they came straight out of a dystopian dictator's mouth. Some of them are actually nice.Probably because he didn't write them.Join us on a discerning journey as two data scientists tackle Donald Trump's Twitter account and, through quantitative methods, reveal to us which hands are behind the tweets.Episode TranscriptFor the full episode, listen 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.Dave Robinson: So the original Trump analysis is certainly the most popular blog post I’ve ever written. It got more than half a million hits in the first week and it still gets visits . . . and the post still gets a number of visits each week. I was able to write it up for the Washington Post and was interviewed by NPR.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: Here at Data Crunch, as we research how data and machine learning are changing things, we’re noticing an explosion of real-world applications of artificial intelligence that are changing how people work and live today. We see new applications every single day as we research, and we realize we can’t possibly keep you well enough informed with just our podcast. At the same time, we think it’s really important that people understand the impact machine learning is having on our world, because it’s changing and is going to change nearly every industry. So to help keep our listeners informed, we’ve started collecting and categorizing all of the artificial intelligence applications we see in our daily research. These are all available on a website we just launched, which Data Elixir recently recognized as a recommended website for their readers to check out. The website includes, for example, a drone taxi that will one day autonomously fly you to work, a prosthetic arm that uses AI to aid a disabled pianist to play again, and a pocket-sized ultrasound that uses AI to detect cancer.Go explore the future at datacrunchpodcast.com/ai, and if you want to keep up with the artificial intelligence beat, we send out a weekly newsletter highlighting the top 3-4 applications we find each week that you can sign up for on the website. It’s an easy read, we really enjoy writing it, and we hope you’ll enjoy reading. And now let’s get back to today’s podcast.Ginette: Today, we’re chatting with someone who made waves over a year ago with a study he conducted and he recently did a follow up study that we’ll hear about. Here’s Dave Robinson.Dave: I'm a data scientist at Stack Overflow, we’re a programming question-and-answer website, and I help analyze data and build machine learning features to help get developers answers to their questions and help them move their career forward, and I came from originally an academic background where I was doing research in computational biology, and after my PhD I was really interested in what other kinds of data I could apply a combination of statistics and data analysis and computer programming too.Curtis: Dave studied stats at Harvard and then went on to get his PhD in Quantitative and Computational Biology from Princeton. He did a study on Donald Trump’s tweets in 2016 you may have heard about and posted it to his blog, Variance Explained.For the full episode, listen 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.SourcesPicture SourcePhoto by Kayla Velasquez on UnsplashMusic

If someone came up to you and randomly asked you, "How many slaves work for you?" maybe you'd think, "Slavery ended a long time ago, Bro." Or maybe you would take the question seriously. With 20 million to 46 million people enslaved in the world, it is a serious question, and while we don't see it daily, some of these enslaved people make things for us. Even if we're judicious about what we buy, we would be surprised just how much global slavery goes into producing the goods we do buy. But how can we quantify it? How can we solve this? Justin Dillon, who has worked with the U.S. State Department and hundreds of businesses, thinks he has the answer.Transcript:Ginette: “Our world today is an extremely vast, complicated, and interconnected web of 7.5 billion people. We’re directly connected to some, and it’s really easy to see those connections on Facebook, Instagram, Twitter, LinkedIn. But there’s a whole other group of people we are much more subtly connected to—people who are basically (who are essentially working for us) invisible to us, 20 to 46 million of them.“Our guest today deals with this invisible web every day.”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’s episode is brought to you by data.world, the social network for data people. Discover and share cool data, connect with interesting people, and work together to solve problems faster at data.world. Quickly locating data, understanding it, and combining it with other sources can be difficult. The data.world Python library allows you to bring data.world datasets straight into your workflow. Easily work with data and metadata in your Python scripts and Jupyter notebooks. Ready to dive in? Learn how to use data.world’s Python library at meta.data.world. Curtis: “Before we get going, one other note about data.world—starting today until May 5th, we are hosting a data competition on their site, and we’d love your participation. Donald Trump’s tweets have been the source of a lot of media attention recently—many high profile news outlets have asserted his tweets show signs of authoritarianism, some say he’s using his twitter account to shape the new cycle, and some have even built algorithms to make stock market decisions based on his tweets. Whatever your stance is on the subject, we’ve uploaded a dataset of every single one of his Tweets up to data.world, and we want to see what you can make of the data. This is a create competition by nature—submissions can be of any format, but the point is we want to see what you can learn, assert, or create with this data set. It’s easy to participate—just go to data.world/datacrunch, and you’ll find the dataset and all of the details. Submit by May 5, and we’re going to take all the submissions that tell the most compelling stories, we want to feature them on a future podcast episode.”Ginette: “Now back to the story. A few months ago, I ran across a website. It sucked me in. It asked me a provocative question, which we’ll get to in just a second, but first, we’ll introduce you to the man who’ll situate the story for you—the main person behind the website.” Justin: “My name’s Justin Dillon. I’m the founder and CEO of Made in a Free World. We started off years ago. I would say probably the genesis for us was me getting a call from the State Department in about 2010. I’d already been doing some projects, a few websites and, films that I was producing, around human trafficking and modern-day slavery.”Curtis: “Justin directed a documentary he released in 2008 called ‘Call + Response,’ which ranked as one of the top documentaries in 2011.”Justin: “And the State Department called and said, we would like to do a project with you, we like the way that you use data and tell stories,

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, well known as IUU fishing. Unfortunately, IUU fishing is linked to some of the ugliest transnational crimes: modern-day slavery, human trafficking, drug trafficking,

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 in her country.”Hadidja: “People tend to think that the war in Rwanda started in ’94. Actually the war started on October 1, 1990.”Ginette: “Hadidja is referencing an invasion of a group of mostly Tutsis, a minority group,