Today we’re going to see how a clever idea and the skillful use of data is starting to disrupt how people get credentials. The use case here has the potential to remove gender and racial bias in the hiring process, help companies understand specific talent gaps in their workforce, and help learners find lucrative educational pathways they can take.
Joe Kleinhenz talks about his journey from starting out in data all the way to becoming a leader in one of the largest insurance organizations in the United States. We’ll learn about the importance of staying on top of technology, how to win hearts and minds of nontechnical folks, centralized
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 of how to build contextually relevant data products that make a difference in people’s lives.
How do you whittle the murky business of creating a data-driven culture down to a proven process? Today we talk to a guest who has done this time and time again, helping companies transform their operations. He points out the small nuances and details about the process, like questions to
Beginning: Statistics are misused and abused, sometimes even unintentionally, in both scientific and business settings. Alex Reinhart, author of the book “Statistics Done Wrong: The Woefully Complete Guide” talks about the most common errors people make when trying to figure things out using statistics, and what happens as a result. He
Would you rather take a year to develop a proprietary algorithm for your company that has an accuracy of 95% or use an open source platform that takes a day to develop an algorithm that has nearly the same accuracy? In most business cases, you’d choose the latter. In this episode, we talk to Till Bergmann who works on a team that developed TransmogriAI, an open source project that helps you build models quickly.
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.
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: