Data Crunch

If you've ever tried to find a doctor in the United States, you likely know how hard it is to find one who's the right fit—it takes quite a bit of research to find good information to make an informed choice. Wouldn't it be nice to easily find a doctor who is the right fit for you? Using data, Covera Health aims to do just that in the radiology specialty.Ron Vianu: I think the tools are really improving year over year to a significant degree, but like anything else, the tools themselves are only as useful as how you apply them. You can have the most amazing tools that could understand very large datasets, but you know how you approach looking for solutions, I think can dramatically impact. Do you yield anything usefulGinette Methot: I’m Ginette,Curtis Seare: 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: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company.If you're a business leader listening to our podcast and would like to move 10 times faster and be 10 times smarter than your competitors, we're running a webinar on February 13th where you can learn how to do this and more. Just go to datacrunchcorp.com/go to sign up today for free. If you're a subject matter expert in your field, like our guest today, and you're looking to understand data science and machine learning, brilliant.org is a great place to dig deeper. Their classes, help you understand algorithms, machine learning concepts, computer science basics, and many other important concepts in data science and machine learning. The nice thing about brilliant.org is that you can learn in bite-sized pieces at your own pace. Their courses have storytelling, code writing and interactive challenges, which makes them entertaining, challenging, and educational. Sign up for free and start learning by going to brilliant.org/data crunch. And also the first 200 people that go to that link will get 20% off the annual premium subscription. Today we chat with Ron Vianu, the CEO of Covera Health. Let's get right to it. Curtis: What inspired you to get into what you're doing, uh, to start Covera health? Where did the idea come from and what drives you? So if we could start there and learn a little bit about you and the beginnings of Covera health, that would be great. Ron: Sure. Uh, and I, I guess it's important to state that, you know, I'm a problem solver by nature, and my entire professional career, I've been a serial entrepreneur building companies to solve very specific problems. And as it relates to Covera, the, the Genesis of it was understanding that there were two problems in the market with respect to, uh, the healthcare space, which is where we're focused that were historically unsolved and there were no efforts really to solve them in, from my perspective, a data-driven way. And that was around understanding quality of physicians that is predictive to whether or not they'll be successful with individual patients as they walk through their practice. And so if you, and we're focused on the world of radiology, which today is highly commoditized and what that means is that there was a presumption that wherever you get an MRI or a CT study for some injury or illness, it doesn't matter where you go. It's more about convenience and price perhaps. Whereas what we understand given our research and the, the various things that we've published since our beginning is that one, it's like every other medical specialty. It's highly variable. Two, since radiology supports all other medical specialties in a, as a tool for diagnosis, diagnostic purposes, any sort of variability within that specialty has a cascading effect on patients downstream. And so for us, the beginning was, is this something that is solvable through data?

We talk with Ben Jones, CEO of Data Literacy, who's on a mission to help everyone understand the language of data. He goes over some common data pitfalls, learning strategies, and unique stories about both epic failures and great successes using data in the real world.Ginette Methot: I’m Ginette,Curtis Seare: 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: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company.It’s becoming increasingly important in our world to be data literate and to understand the basics of AI and machine learning, and Brilliant.org is a great place to dig deeper into this and related topics. Their classes help you understand algorithms, machine learning concepts, computer science basics, and many other important concepts in data science and machine learning. The nice thing about Brilliant.org is that you can learn in bite-sized pieces at your own pace. Their courses have storytelling, code-writing, and interactive challenges, which makes them entertaining, challenging, and educational.Sign up for free and start learning by going to Brilliant.org/DataCrunch, and also the first 200 people that go to that link will get 20% off the annual premium subscription.Curtis: Ben Jones is here with me on the podcast today. This is a couple months coming. Excited to have him on the show. He's well known in the data visualization community, he's done a lot of great work there. Uh, used to work for Tableau. Now he's off doing his own thing, has a company called Data Literacy, which is interesting. We're going to dig into that and also has a new book out called Avoiding Data Pitfalls. So all of this is really great stuff and we're happy to have you here, Ben. Before we get going, just give yourself a brief introduction for anyone who may not know you and we can go from there. Ben: Yeah, great. Thanks Curtis. You mentioned some of the highlights there. I uh, worked for Tableau for about seven years running the Tableau public platform, uh, in which time I wrote a book called Communicating Data with Tableau. And the fun thing was for me that launched kind of a teaching, um, mini side gig for me at the University of Washington, which really made me fall in love with this idea of just helping people get excited about working with data. Having that light bulb moment where they feel like they've got what it takes. And so that's what caused me to really want to lead Tableau and launch my own company Data Literacy at dataliteracy.com which is where I help people, you know, as I say, learn the language of data, right? Whether that's reading charts and graphs, whether that's exploring data and communicating it to other people through training programs to the public as well as working one on one with clients and such. So it's been a been an exciting year doing that. Also, other things about me, I live here in Seattle, I love it up here and go hiking and backpacking when I can and have three teenage boys all in high school. So that keeps me busy too. And it's been a fun week for me getting this book out and seeing it's a start to ship and seeing people get it. Curtis: Let's talk a little bit about that because the book, it sounds super interesting, right? Avoiding Data Pitfalls, and there are a lot of pitfalls that people fall into. So I'm curious what you're seeing, why you decided to write the book, how difficult of a process it was and then some of the insights that you have in there as well. Ben: Yeah, so I feel like the tools that are out there now are so powerful and way more so than when I was going to school in the 90s, and it's amazing what you can do with those tools. And I think also it's amazing that it's amazing how easy it is to mislead yourself. And so I started realizing that that's sometim..

How do you build a comprehensive view of a topic on social media? Jordan Breslauer would say you let a machine learning tool scan the social sphere and add information as conversations evolve, with help from humans in the loop.Ginette Methot: I’m Ginette,Curtis Seare: 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: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company.Ginette: Many of you want to gain a deeper understanding of data science and machine learning, and Brilliant.org is a great place to dig deeper into these topics. Their classes help you understand algorithms, machine learning concepts, computer science basics, probability, computer memory, and many other important concepts in data science and machine learning. The nice thing about Brilliant.org is that you can learn in bite-sized pieces at your own pace. Their courses have storytelling, code-writing, and interactive challenges, which makes them entertaining, challenging, and educational.Sign up for free and start learning by going to Brilliant.org slash Data Crunch, and also the first 200 people that go to that link will get 20% off the annual premium subscription.Let’s get into our conversation with Jordan Breslauer, senior director of data analytics and customer success at social standards. Jordan: My name is Jordan Breslauer. I'm the senior director of data analytics and customer success at social standards. I've always been a data geek as it pertains to sports. I think of Moneyball when I was younger, I always wanted to be kind of a the next Billy Bean and I, when I started working for sports franchises right after high school and early college days, I just realized that, that type of work culture is wasn't for me, but I was so, so into trying to answer questions with data that had no previously clear answer, you know? I loved answering subjective questions like, or what makes the best player or how do, how do I know who the best player is? And I thought what was always fun was to try and bring some sort of structured subjectivity to those sorts of questions through using data. And that's really what got me passionate about data in the first place. But then I just started to apply it to a number of different business questions that I always thought were quite interesting, which have a great deal of subjectivity. And that led me to Nielsen originally where my main question that I was answering on a day-to-day basis, what was, what makes a great ad? Uh, what I found though is that advertising at least, especially as it pertains to TV, is really where brands were moving away from and a lot of the real consumer analytics that people were looking for were trying to underpin people in their natural environment, particularly on social media. And I hadn't seen any company that had done it well. Uh, and I happened to meet social standards during my time at Nielsen and was truly just blown away with this ability to essentially take a large input of conversations that people were happening or happening, I should say, and bring some sort of structure to them to actually be able to analyze them and understand what people were talking about as it pertained to different types of topics. And so I think that's really what brought me here was the fascination with this huge amount of data behind the ways that people were talking about on social. And the fact that it had some structure to it, which actually allowed for real analytics to be put behind it. Curtis: It's a hard thing to do though. Right? You know, to answer this question of how do we extract real value or real insight from social media and you'd mentioned historically or up to this point, companies that that are trying to do that missed the mark.

Sometimes AI and deep learning are not only overkill, but also a subpar solution. Learn when to use them and when not. Diego from Northwestern's Deep Learning Institute discusses practical AI and deep learning in industry. He covers insights on how to train models well, the difference between textbook and real AI problems, and the problem of multiple explanations.Diego Klabjan: One aspect of the problem it has to have in order to be, to be amenable to AI is complexity, right? So if you have, if you have a nice data with, I don't know, 20, 30 features that you can quote, put in a spreadsheet, right? So then, then AI is going to be an overkill and it's actually sort of not, is going to be an overkill. It's going to be a subpar solution.Ginette Methot: I’m Ginette, Curtis Seare: 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: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company.We’d like to hear what you want to learn on our future podcast episodes, and so we’re running a give away until our next podcast episode comes out. We’re giving away our book Simple Predictive Analytics. All you have to do is go on to LinkedIn and tag The Data Crunch Corporation in a post with your suggestion, and we’ll randomly pick a winner from those who submit. If you win and you’re in the US, we’ll send you a physical copy, and if you’re in another country, we’ll send you an electronic copy. Can’t wait to hear from you.Today, we chat with Professor Diego Klabjan the director of the Master of Science in Analytics and director of the Deep Learning Lab at Northwestern University. Diego: My name is Diego Klabjan. So I'm a faculty at Northwestern University in the department of industrial engineering and management sciences. I actually spend my entire career in academia. So I graduated from Georgia tech in '99, and then I spent six years at the university of Illinois Urbana-Champaign and got my tenure there. And then I was recruited here at Northwestern as a tenured faculty member a year later. So I'm at Northwestern for approximately 14 years. Yeah, so I'm the director of the master of science in analytics, actually founding director of the master of science in analytics, so I established the master's program back in 2010, and I'm directing it since then. And recently, I also became the director of the center for deep learning, which is a relatively new initiative at Northwestern. Sort of we, we are having discussions for the last year and a half, and about half a year ago, we officially kicked it off with a few founding members. So my expertise is in machine learning and deep learning. So I have, I run sort of a very big research program. So I advise more than 15 PhD students from a variety of, of departments and the vast majority of them do deep learning research. Yeah, so I started, I started deep learning what was around six, seven years ago. So I was definitely not sort of one of the, one of the early or the earliest faculty members conducting, studying, being attached to deep learning. But I wasn't that late to the game either. Right. So I still, I still remember approximately six, seven years ago attending deep learning conferences with like 50 attendees, and now, now those conferences are like 5,000 people. Just astonishing. Curtis: That's crazy. How you've seen that grow.Diego: Yup. Um, yeah, and I'm also, so the last word is ah, I'm also a founder of OPEX analytics, which is a consulting company. I no longer have much to do with the company, uh, but sort of have experience also on the business side. Curtis: Great. So this, uh, the deep learning Institute started about a year or two ago, is that right? Did I understand that right?Diego: Yeah, that's correct. I mean, so we,

With recent events being what they are, epidemiology has come into the spotlight. What do epidemiologists do and how does data shape their everyday experience? Sitara and Mee-a from "Donuts and Data" fill us in. 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: Data crunch is produced by the Data Crunch Corporation, an analytics training and consulting company. Many people are on the lookout for online math and science resources right now, particularly data and statistics courses, and whether you're a student looking to get ahead, a professional brushing up on cutting-edge topics, or someone who just wants to use this time to understand the world better, you should check out Brilliant. Brilliant’s thought-provoking math, science, and computer science content helps guide you to mastery by taking complex concepts and breaking them up into bite-sized understandable chunks. You'll start by having fun with their interactive explorations, over time you'll be amazed at what you can accomplish. Sign up for free and start learning by going to Brilliant.org slash Data Crunch, and also the first 200 people that go to that link will get 20% off the annual premium subscription. Now onto the show. Curtis: I'd like to welcome Sitara and Mee-a from the Instagram account Donuts and Data to talk to us today. I guess let's just have you guys introduce yourselves, as opposed to me trying to introduce you cause you know what you do better than I do. So maybe we just have some introductions. Sitara: So I'm Sitara one half of Donuts and Data. I'm a PhD student in epidemiology at the University of Texas Health Science Center. I'm also a research assistant in a lab that I work in. Mee-a: And I'm Mee-a. I am an infectious disease epidemiologist that works in the public sector. I actually met Sitara through the lab that she's currently working in. Curtis: Nice. And I'm excited to have you guys on. I just, I think epidemiology is a really interesting space, especially with what, you know, with what's going on now with COVID. I think it's more pertinent than it ever has been. Not that it ever hasn't been pertinent, but maybe it's more top of mind for people. So I'd love maybe just to have you guys level set with everybody, like what is epidemiology. There's probably some confusion about what that is and maybe how you guys got into it. And then we can get into what your day to day is and, and what it's all about. Sitara: So, epidemiology, I think everyone's kind of understanding is setting patterns of disease in the, in the human population. And so in that sense, what Mee-a and I do are the same, but instead of studying infectious diseases or the natural science part of epidemiology, what I focus on is how human behavior contributes to those patterns of disease. So I look for patterns in data associated like demographics or just behaviors, diet, nutrition, and how that contributes to getting diseases. Mee-a: For me in the public sector, it's going to be a lot of looking at incidents, rates of infectious diseases. It . . . primarily with COVID-19 right now, and just different ways that we can try to possibly implement infection prevention measures. So we are dealing a little bit more with, I don't want to say the medical side of it because we aren't clinicians, but we are dealing more with the medical side of, of the infectious disease than we are with, with the data compared to when I was in academia, at least. Curtis: So take us through maybe the end goal, right? So what you guys are working on. You're hoping to come out with, I think, some recommendations for people to, to take maybe a better understanding of how the disease spreads, so we get in front of it. What does that look like? Mee-a: I always thought that epidemiology's gold standard of what we try to achieve is probably..