Welcome to another exciting episode of Data Crunch! In this episode, we dive deep into the world of data analytics and manufacturing with our special guests, Alex Reid and Jay Minutrie. Both Alex and Jay bring unique entrepreneurial and data-focused experiences to the table, making them the perfect duo for an engaging conversation about the future of manufacturing. We start with the origin story of their innovative technology, which began as an idea in Alex's father's laboratory at Tulane University. Alex, a young entrepreneur, teamed up with Jay, a seasoned industry veteran, to bring this pa ..read more

The way you organize your data science team will greatly affect your business’s outcome. This episode discusses different structures for a data science team, as well as top down versus bottom up approaches, how to get data science solutions into production organically, and how to be part of the business while remaining in contact with other data scientists on the team.Mark Lowe: Having lived through small scale, two people working, to large scale, thousands of people in your organization, the way that you organize the data science team has dramatic effect on its productivity.Ginette Methot: I ..read more

Machine learning is becoming a bigger part of chemistry as of the last two or three years. Industries need to have people trained in both fields, and it's taken time for them to make their way into this sector. Olexandr Isayev is at the forefront of that wave, and he talks to us about what he's done while melding deep learning and chemistry together and his vision of where he sees this field going with this new tech ..read more

Python versus R. It's a heated debate. We won't solve this raging controversy today, but we will peek into the history of Python, particularly in the open source community surrounding it, and see how it came to be what it is today—a well used and flexible programming language.Travis Oliphant: Wes McKinney did a great job in creating Pandas . . . not just creating it but organized a community around it, which are two independent steps and both necessary, by the way. A lot of people get confused by open source. They sometimes think you just kind of going to get people together and open source em ..read more

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 abo ..read more

Our guest Andrzej Wolosewicz has had years of experience helping companies define and build machine learning and analytical solutions that have a measurable impact on the business, and he shares with us his experience and expertise. He shares with us the biggest pitfalls he sees companies fall into over an over as they try to implement these initiatives.The problem was there was a lot of activity every month that they were doing, but in terms of progressing, their analytic capabilities were really kind of being able to to grow and be more effective. They weren't, they weren't able to do that ..read more

Working with structured and semistructured data can be hard, but it's currently much easier than working with unstructured data, like images, video, audio, and text. We talk with Davit Buniatyan, CEO of Activeloop, who chats with us about how he works to make unstructured data for machine learning easier and faster to work with.  ..read more

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 lar ..read more

Transparency International started when a rebellious World Bank employee quit to dedicated himself to exposing corruption. Now the organization claims the media's attention for about one week a year when it publishes its annual Corruption Perceptions Index, an index that ranks countries in order of perceived corruption. Find out how the organization sources the data, what an important bias is in that data, and how that data ultimately impacts the world.Alejandro Salas: I studied political science and I got very interested in all the topics related to good governance, to ethics in the public se ..read more

Our guest today holds a PhD in organizational psychology and has been working on data products in the health and wellness space for over a decade. We cover a lot of ground in this interview: how to create data products that work, how to avoid the unexpected consequences of poorly designed data interventions, and the importance of ethnographic thinking in data science.We'll also talk about reducing friction in data collection, the coaching data product model, and surprising things we can learn when people's routine's are broken. From today's episode, you'll come away with a better understanding ..read more