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Data Flexibility in Healthcare
Jason Kolaczkowski has worked in both a large-company data shop and in a company trying to help large companies fix their problems. He shares his perspective as senior director of healthcare analytics at NextHealth and former Kaiser employee on the importance of streamlining data definitions—and many other helpful insights. Jason Kolaczkowski: But rather than sort of be the good steward andListen
Education and AI
For David Guralnick, education, AI, and cognitive psychology have always held possibility. With many years of experience in this niche, David runs a company that designs education programs, which employ AI and machine learning, for large companies, universities, and everything in between. David Guralnick: Somehow what’s happened in a lot of the uses of technology and education to this pointListen
Upskilling from Home
Many of us are stuck at home right now, due to the Covid-19 pandemic. There are pros and cons to this. We have less of a commute, more quality time with people in our households, and time to do little tasks we’ve been putting off. On the flip side, it can feel isolating, basic necessities are much more of aListen
How to Reduce Uncertainty in Early Stage Venture Funding
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. Chris Hjelm: We’ve back tested the data on a large number of companies andListen
Data in Healthcare with Ron Vianu
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, CoveraListen
Data Literacy with Ben Jones
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,Listen
Social Media and Machine Learning
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 dataListen
Deep Learning, Microwaves, and Bugs
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. DiegoListen
Potential Advantages of Blockchain for Data Scientists
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. Luciano Pesci: And the key for data scientists and leaders who are gonna oversee data sciences, you’ve got to get aListen
How to Predict World Events with Predata
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. Predata uncovers predictive behavior by applying machineListen