Early stage venture investing has little data to draw from to make good investing decisions. So how has Connetic Ventures successfully developed a data system to inform their investment decisions? We chat with Chris Hjelm about the process they've used to develop something that does just that. 

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.

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.

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.

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.

Luciano Pesci is bullish on blockchain and data science. Since blockchain offers a complete historical record, no one can delete or alter prior information written into the record. He sees this characteristic as a massive advantage for data scientists.

There have been some spectacular fails when it comes to looking at Internet traffic, think Google Flu Trends; however, Predata, a company that helps people understand global events and market moves by interpreting signals in Internet traffic, has honed human-in-the-loop machine learning to get to the bottom of geopolitical risk and price movement.

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.

David Millar is a man bringing analytical solutions to an industry that historically has had little data. But with the explosion of smart devices, that is all changing, and the way utilities operate is as well.

Simeon Schwarz has been walking the data management tightrope for years. In this episode, he helps us see the hidden organizational and economic impacts that come from leading a data management initiative, and how to understand and overcome the inertia, fears, and status quo that hold good data management back.

David Saben is on a mission to make taking tests less painful, and he’s using data to do it. In this episode, he’ll discuss reviving methods developed in 1979 to shorten tests and make them more effective, as well as how to use psychometrics to aid in the design and crafting of an effective test.

Todd Jones talks to us about how to activate analytics and data science in both commercial and government settings by solving some of the most common problems he sees across organizations.

Today we speak with Professor Ram Bala, an expert in supply chain management analytics, particularly last-mile delivery. He has very interesting insights into how today’s supply chain is evolving. He talks about various methods and algorithms he uses, the specific challenges inherent in doing last mile logistics and deliver, how pricing factors in, and how everyone is trying to catch up to Amazon.

Today, our guest, Alain Briancon, will talk to us about how to work with Fortune 500 companies and help them get quick value from their data, how to build a roadmap of incremental value during the data collection and analysis process, how they help predict and incentivize customer purchases, and how to dial in on an idea for successful data science software companies.

Today is a special episode. We welcome three executive guests from different organizations to share their experiences and insights about how data science can best support executive goals.

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.

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 versus decentralized team, pros and cons, how to hold effective conversations with stakeholders and how to go from individual contributor to leader.

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...

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 ask to start on the right foot, critical feedback loops to put in place along the way, and how to overcome some of the most common problems that make people give up.

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 shares practical insights into how both scientists and business analysts can make sure their statistical tests have high enough power, how they can avoid “truth...

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...

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. Intro: Instead of running one model and running the next model and then...

What does it take to become a data scientist? Nic Ryan has been in the field for over a decade and answered thousands of questions from people looking to get into the field. In this episode, he talks about his journey into data science and his experiencing mentoring aspiring data scientists, giving advice to both beginners and seasoned professionals.

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. Olexandr Isayev: Historically, chemistry was empirical science. It's been...