Data Crunch | Big Data | Data Analytics | Data Science

Data Crunch | Big Data | Data Analytics | Data Science
By Vault Analytics
About this podcast
Whether you like it or not, your world is shaped by data. We explore how it impacts people, society, and llamas perched high on Peruvian mountain peaks—through interviews, inquest, and inference. Buckle up.
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Jan. 19, 2018
Episode Summary Few things are more 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. 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. 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 anal...
Dec. 19, 2017
The ubiquity of and demand for data has increased the need for better data tools, and as the tools get better and better, they ease the entry into data work. In turn, as more people enjoy the ease of use, data literacy becomes the norm.   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.” “We have a gift for you this holiday season. We’re giving you, our listeners, a website . . . it’s a website of all the AI applications we come across or hear about in our daily research. We post bite-size snippets about the interesting applications we are finding that we can’t feature on the podcast so that you can stay informed and see how AI is changing the world right now. There are so many interesting ways that AI is being used to change the way people are doing things. For example, did you know that there is an AI application for translating chicken chatter? Or using drones to detect and prevent shark attacks on coastal waters? To experience your holiday gift, go to datacrunchpodcast.com/ai.” Curtis: “If you’ve listened to our History of Data Science series, you know about the amazing advances in technology behind the leaps we’ve seen in data science over the past several years, and how AI and machine learning are changing the way people work and live. “But there is another trend that’s also been happening that isn’t talked about as much, and it’s playing an increasingly important role in the story of how data science is changing the world. “To introduce the topic, we talked with someone who is part of this trend, Nick Goodhartz.” Nick Goodhartz: “So I went to school at Baylor University, and I studied finance and entrepreneurship and a minor in music. I ended up taking a job with a start-up as a data analyst essentially. So it was an ad technology company that was a broker between websites and advertisers, and so I analyzed all the transactions between those and tried to find out what we are missing. “We were building out these reports in Excel, but there was a breaking point when we had this report that we all worked off of, but it got too big to even email to each other. It was this massive monolith of an Excel report, and we figured there’s got to be a better way, and someone else on our team had heard of Tableau, and so we got a trial of it. In 14 days we—actually less than 14 days—we were able to get our data into Tableau, take a look at some things we were curious about, and pinpointed a possible customer who had popped their head out and then disappeared. We approached them and signed a half million dollar deal, and that paid for Tableau a hundred times over, so it was one of those moments where you really realize, ‘man, there’s something to this.’ “That’s what got me into Tableau and what ...
Nov. 17, 2017
Episode Summary The growth of the Internet of Things, or IoT, is often compared with the industrial revolution. A completely new phase of existence. But what does it take to be part of this revolution by building an IoT product? It’s complex, and Daniel Elizalde gives us a peek into what the successful process looks like. 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. Donate 15 Seconds If you liked this episode, please consider giving us a review on iTunes! It helps other people find the show and lets us know how we’re doing. Partial Transcript (for the full episode, select play above or go here) Ginette: “So, today, we’re defining an IoT product, or an Internet of Things product, as “a product that has a combination of hardware and software. It acquires signals from the real world, sends that information to the cloud through the Internet, and it provides some value to your customers. ”Okay, so before we introduce you to our guest, consider this: The IoT Market is infernally hot. In 2016, we had 6.4 billion connected ‘things’ in use worldwide, and Gartner research firm projects that number will nearly double to 11.2 billion in 2018, and then nearly doubling again to 20.4 billion IoT products in 2020. For context, this last number is about 2 and a half times the number of people on earth. “Let’s look at an example of IoT at work. Let’s say you’re an oyster farmer, and you need to keep your oysters under a certain temperature because harmful bacteria might grow if you don’t—which would result in people getting very sick after eating your product. If that happened, the FDA could shut your operation down. “This is where IoT products can help you. You can track water temperature with sensors. Those sensors can send that data to the cloud, where you can access it. The system will even send you an alert if the temperature ranges outside your chosen temperature criteria. You can use cameras that show when the oysters are harvested and how long the oysters are out of cold water before they’re put on ice. By using these sensors and cameras to record harvest date, time, location, and temperature at all stages of harvest, you have recorded evidence that you’ve properly handled the harvest. “So, for the purposes of today’s episode, let’s now switch to the other perspective—to the perspective of someone who wants to make and sell an IoT product. Imagine you and two of your friends recently launched an IoT startup—you’re able to secure funding to build your IoT product, and you’ve hired some team members to help you get your beta version off the ground. But you’re new to building products like this, and the rest of your team is also pretty new to it as well. So you decide to talk with someone who is an expert in the IoT space who can give you and your team pointers—and you’re lucky enough to find this man.” Daniel: “My name is Daniel Elizalde. I am the founder of Tech Product Management. My company focuses on providing training for companies building IoT products, specifically I focus on training product managers. I’ve been doing IoT really for over 18 years, before it was called IoT, and I worked in small companies and large companies,
Oct. 18, 2017
  Episode Summary We’ve seen photos of disasters depicting fearful and fleeing victims, ravaged properties, and despondent survivors. In this episode, we explore two ways data can help survivors heal and how data also tells their stories. 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.   Donate 15 Seconds If you liked this episode, please consider giving us a review on iTunes! It helps other people find the show and lets us know how we’re doing!   Partial Transcript (for the full episode, select play above or go here) Aaron Titus: “I almost disbelieved my own numbers, even though I chose the most conservative ones. It’s just outrageous. I’m like, ‘Really? A 233x ROI?’ That’s insane.” 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.” “Today’s episode is brought to you by Lightpost Analytics. Data skills are in intense demand and are key for organizations to remain competitive; in fact, Forbes listed the industry’s leading data visualization software, Tableau, as the number three skill with the most explosive growth in demand, so investing in yourself to stay relevant in today’s hyper-competitive, data-rich, but insights-hungry world is extremely important. Lightpost Analytics is a trusted training partner to help you develop the Tableau skills you need to stay relevant. Check them out at lightpostanalytics.com and let them know that Data Crunch sent you.”  “Today, we look at what it takes to understand a larger story—when many disparate voices come together to tell you something much more powerful, and specifically how it can help people deal with the large scale devastation of natural disasters. Let’s jump into how one man did something about his pet peeve, and it produced $300,000,000.00 dollars in savings. And then we’ll pop over to New Zealand to explore how a disaster situation affected Christchurch and what people did about it.” Aaron: “I was a disaster relief volunteer in New Jersey during hurricanes Irma (Ginette: Here Aaron actually means Irene) and Sandy, and my area got very hard hit by Irma, and I started off as a relief volunteer and ended up directing a lot of those relief efforts for my church, and while I was there, I remember standing in very long lines, and a thousand of us would gather together at a field command center and spend an hour and a half waiting to get checked in, which is lightning speed for 1,000 people, but it’s still an hour and a half. “And while everybody was waiting, they’d pull out their phones and would start playing Angry Birds, and the technologist in me would just scream inside, “I could have you all checked in with your work orders in 30 seconds, not an hour and a half!” “And I abhor inefficiency—to a fault—like it’s almost a little bit of a sickness. I really ought to be better, but I really abhor inefficiency, and I hate it when people waste my time, and I hate wasting people’s times, especially volunteers.
Sept. 19, 2017
Hilary Mason is a huge name in the data science space, and she has an extensive understanding of what’s happening in this space. Today, she answers these questions for us: * What are the backgrounds of your typical data scientists? * What are key differences between software engineering and data science that most companies get wrong? * How should you measure the effectiveness of your work or your team’s work as a data scientist for the best results? * What is a good approach for creating a successful data product? * How can we peak behind the curtain of black-box deep learning algorithms? Below is a partial transcript. 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. Curtis: Today we hear from one of the biggest thinkers in the data science space, someone who DJ Patil endorses on LinkedIn for data science skills. She worked at bit.ly, the url shortener, and is a data scientist in residence at venture capital firm Accel Partners, a firm that helped fund some companies you may know, like Facebook, Slack, Etsy, Venmo, Vox Media, Lynda.com, Cloudera, Trifacta—and you get the picture. Ginette: The partner of this VC firm said that Accel wouldn’t have brought on just any data scientist. This position was specifically created because this particular data scientist might be able to join their team. Curtis: But beyond her position as data in residence with Accel, she founded a company that’s doing very interesting research, and today, she shares with us some of her experiences and perspective on where AI is headed. 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. Hilary: I’m Hilary Mason, and I’m the founder and CEO of Fast Forward Labs (Please note that Hilary is now the VP of Research at Cloudera). In addition to that, I’m a data science in residence for Accel Partners. And I’ve been working in what we now call data science, or even now call AI, for about twenty years at this point. Started my career in academic machine learning and decided startups were more fun and have been doing that for about 10,   12 years depending on how you count now, and it’s a lot of fun! Ginette: Something I’d like to note here is there’s been a very recent change: Hilary’s company, Fast Forward Labs, and Cloudera recently joined forces, and Hilary’s new position is Vice President of Research at Cloudera. Now, one thing that Hilary talks to is where the data scientists she works with come from, which is a great example of the different paths people take to get into this field. Hilary I am a computer scientist, and I have studied computer science. It’s funny because now at Fast Forward, our team only has only two computer scientists on it, and one of them is our general counsel, and one is me, and I’m running the business, so most of the people doing data science here come from very different backgrounds. We have a bunch of physicists, mathematicians, a   neuroscientist, a person who does brilliant machine learning design who was an English major, and so data science is one of those fields where one of the things I really love about it is that people come to it from so many different b...
Aug. 9, 2017
Tesla isn’t the only car brand in the world producing or aiming to produce self-driving cars. Every single car brand is working on developing self-driving cars. But what does this mean for our future? We talk about this and other interesting deep learning projects and history with Ran Levi, science and technology observer and podcaster, who explains in thought-provoking ways what we have to look forward to. Below is a partial transcript. 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. Ran Levi: “I actually had the pleasure of being invited to Google’s Mountain View headquarters, and they took me for a drive in one of their autonomous vehicles, and it was, to tell you about that drive because it was boring—boring in a good way. Nothing happened! We were just driving around. The car was driving itself all around Mountain View. And it worked. “The first time I entered such a car, I didn’t know what to expect. I mean, I didn’t know how reliable are those kinds of cars. So I had the idea that maybe I should sit somewhere where I can maybe jump and grab the wheel if necessary. You know, I was a bit dumb. They don’t need me, really. And probably if I touch the steering wheel, I would probably make some mistake and ruin the car. It drives better without me.” 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: “We have a great live show planned that we hope to give at SXSW 2018. It’s a really awesome show about the power of niche artificial intelligence, and we’re going to share details from our research into what amazing things AI is doing right now on the fringe and in mainstream AI projects. We’re really excited to share it, so if you’re going to SXSW, or you just want to be good hearted and help us out, please vote on our dual panel by going to panelpicker.sxsw.com, signing in, and liking our topic, which you can find by searching for ‘The Power of Niche AI: From Cucumbers to Cancer.’ “Today we get to talk to Ran Levi, who’s been researching and reporting on science and technology for the past 10 years. He’s a hugely successful science and tech podcaster in Israel, producing a Hebrew-language show called Making History, and he’s also producing two English podcasts right now for an international audience, so since he’s steeped in the subject, he has a lot of very interesting insights for us.” Ran: “I’m actually an electronics engineer by trade. I was an engineer for 15 years. I was both a hardware and software developer for several companies in Israel. And during my day job as an engineer, I wrote some books about the history of science and technology, which was always a big hobby of mine. And actually, I started a podcast about this very subject about 10 years ago, and it became quite a hit in Israel I’m happy to say. So about four years ago, I quit my day job, and I actually started my own podcasting company, and now we are podcasting both in Israel and in the U.S. for international audience and actually launched my brand new podcast last week. It’s called Malicious Life about the history of malware and cybersecurity, which is a fun topic. Actually, the day I launched the podcast,
July 16, 2017
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. 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. Check out the book they’ve written called Tidy Text Mining with R. Below is a partial transcript. 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. Transcript Julia Silge: “One that I worked on that was really fun was about song lyrics. 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.” 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: “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. 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. Check out docs.data.world for up-to-date API documentation, tutorials on SQL, and other query techniques, and much more!” Ginette: “We hope you’re enjoying some vacation time this summer. We just did, and now Data Crunch is back! To hear the latest from us, add us on Twitter, @datacrunchpod. 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.” Julia: “My name is Julia Silge, and I’m a data scientist at Stack Overflow. 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.” Ginette: “Stack Overflow, where Julia works, is the largest online community for programmers to learn, share knowledge, and build their careers. It’s a great resource when you need to solve a coding problem or develop new skills.” Curtis: “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. Both languages have devoted followers, and both do excellent work. Today, we’re looking at R, and Julia is a big name in this space, as is her collaborator Dave Robinson.” Julia: “Text is increasingly a really important part of our work as people who are involved in data. Text is being generated all the time, at ever faster rates. This unstructured data is becoming a really important part of things that we do. I also am somebody that—my academic background is not in text or literature or natural language processing or anything like that, but I am somebody who’s always been a reader and always been interested in language, and these sort of collection of circumstances kind of all came together to converge that me and Dave decided to ...
June 11, 2017
According to the CDC, people have been writing descriptions of malaria—or a disease strikingly similar to it—for over 4,000 years. How is data helping Zambian officials eradicate these parasites? Tableau Foundation’s Neal Myrick opens the story to us. Below is a partial transcript. 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. Neal: “When somebody walks from their village to their clinic because they’re sick, health officials can see that person now as the canary in a coal mine.” 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: “This 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. Looking for a lightweight way to deliver a collection of tables in a machine-readable format? Now you can easily convert any tabular dataset into a Tabular Data Package on data.world. Just upload the file to your dataset, select ‘Tabular Data Package’ from the ‘Download’ drop-down, and now your data can be effortlessly loaded into analytics environments. Get full details at meta.data.world.” Ginette: “Today we’re talking about something that can hijack different cells in your body for what we’ve deemed nefarious purposes. It enters your bloodstream when a mosquito transfers it from someone else who has it, to you. Once it’s in your body, it makes a B-line for your liver, and when safely inside your liver, it starts creating more of itself. “Sometimes, this parasite stays dormant for a long time, but usually it only takes a few days for it to get to work. It starts replicating, and there are suddenly thousands of new babies that burst into your bloodstream from your liver. When this happens, you might get a fever because of this parasite surge. As these new baby parasites invade your bloodstream, they hunt down and hijack red blood cells. They use these blood cells to make more of themselves, and once they’ve used the red blood cells, they leave them for dead and spread out to find more. Every time a wave of new parasites leaves the cells, it spikes the number of parasites in your blood, which may cause you to have waves of fever since it happens every few days. “This parasite can causes very dangerous side effects, even death. It can cause liver, spleen, or kidney failure, and it can also cause brain damage and a coma. To avoid detection, the parasites cause a sticky surface to develop on the red blood cell so the cell gets stuck in one spot so that it doesn’t head to the spleen where it’d probably get cleaned out. When the cells stick like this, they can clog small blood vessels, which are important passageways in your body. You may have guessed it, we’re describing malaria. “It plagues little children, pregnant women, and other vulnerable people. Children in particular are incredibly vulnerable, something that’s reflected in the statistics: one child dies every two minutes from malaria. “But often outbreaks are treatable, trackable, and preventable when the data is properly captured and analyzed. The United States eradicated malaria in the 1950s.
May 28, 2017
What does the creation of new artificial intelligence products look like today, and what do experts in this field foresee realistically happening in the near future? One thing’s for sure, the way we work and function in life will change as a result of growth in this field. Listen and find out more. Below is a partial transcript. 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. Transcript Irmak Sirer: “It’s kind of like a Where’s Waldo of finding an expert in this entire giant ocean of people.”   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: “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. A complex dataset with a ton of files can quickly become scary and unwieldy, but you need not fear! Now you can use file labels and descriptions to manage and organize your many files on data.world. With file labels and descriptions, you can quickly see what type of file it is, view a short description, and also filter down by file type. Wanna see an example of how data.world users are using file labels and descriptions to keep their dataset organized? Search “data4democracy/drug-spending” on data.world. Ginette: “Today we’re taking a closer look at something that is starting to seep into our daily lives. In one of its forms, it’s something Stephen Hawking, Bill Gates, and Elon Musk are concerned will eventually be a threat to mankind. In another form, though, you’re probably already using it, and it’s becoming a major game changer, kind of like the early days of the desktop computer. We’re talking about artificial intelligence. You use AI when you talk to Siri or your in-home assistant, Alexa or Echo, and some people are using it in the form of a self-driving car. “So daily applications of artificial intelligence are on the rise, becoming much more of a staple in our society, but AI’s definition shifts according to the source. Popular movies depict AI as having a consciousness, emotions, and exhibiting human-like characteristics. Usually it’s involved in some sort of world-domination plot to kill all the humans. Although most experts agree that artificial intelligence will never actually think and feel like a human, the existential threat still exists. This kind of apocalyptic AI is known as ‘general AI.’ But that’s a topic for another episode. Today, we’re focusing on the kind of AI that currently exists, otherwise known as narrow AI.” Curtis: “A narrow AI is called narrow because it’s usually focused on one specific task, where as a general AI would be able to be good pretty much any task thrown its way. The Google search bar is probably the most ubiquitous example of a narrow AI that most people use on a daily basis. The process usually goes like this: you give it an input like ‘How to own a llama as a pet.’ It does its processing. It gives you an output in the form of the 10 most relevant web pages to answer your questions (along, of course, with some paid advertisers who are trying to sell you a pet llama). “The simplicity of the interaction belies the complexity of the cogni...
May 14, 2017
What would the world look like without honeybees? In theory, if there were no honeybees, it could drastically change our lives. Bjorn Lagerman, though, never wants to know the actual answer to that question. but the honeybees current worst foe, Varroa Destructor, is killing off honeybee hives at intense rates. Bjorn’s in the middle of a machine learning project to save the bees from the vampirish Varroa. Below is a partial transcript. 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 iTunes, Google Play, Stitcher, and Overcast. Bjorn Lagerman: “My name is Bjorn Lagerman. I live in the middle of Sweden. When I look back in my younger days, I remember, I sat in school, looked outside the window and decided I wanted to be outside. You know, I was raised in a stone desert in the middle of Stockholm in the old town; that’s a medieval town. And inside the blocks, there were sort of an oasis of water and fountains and green in this stone desert, but the streets were very old streets. And then the contrast was that in the summertime, I spent that in the countryside, and that was total freedom—you kow, lakes, rivers, forests, and my parents let us do what we wished during all the days, just come home for dinner. So when I was 22, I thought bees might be a reason to spend more time in nature. So I went to the nearest beekeeper, . . . and he sold me my first colony, and from there on, I was really hooked.” 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: “This 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. A complex dataset with a ton of files can quickly become scary and unwieldy, but you need not fear! Now you can use file labels and descriptions to manage and organize your many files on data.world. With file labels and descriptions, you can quickly see what type of file it is, view a short description, and also filter down by file type. Wanna see an example of how data.world users are using file labels and descriptions to keep their dataset organized? Search ‘data4democracy/drug-spending’ on data.world.” Ginette: “Imagine for a minute what the world would look like without bees. The image is potentially pretty bleak: we’d have much less guacamole, fruit smoothies, chocolate everything, various vegetables, pumpkin pie, peach cobbler, almond butter, cashews, watermelons, coconuts, lemon, limes, and many more food products. Let’s not forget the obvious—we wouldn’t have honey, which man can’t replicate well. “But fruits, vegetables, and chocolate aren’t the only food stuffs that would be affected. Bees support other animal life. They pollinate alfalfa, which helps feed dairy cows and boost their milk production, and on a more limited basis, alfalfa helps feed beef cows, sheep, and goats. Statistics vary, but bee pollination affects somewhere between one to two thirds of food on American’s plates. Beyond food, bees help grow cotton, so without bees, we’d have to rely more on synthetics for our cloth. “Honeybees in particular are incredibly hard workers. They pollinate 85 percent of all flowering plants.