Community building is an essential aspect of data science. But how do you do it? Find out in Hugo's conversation with Jared Lander, organizer of the New York Open Statistical Programming Meetup and the New York R Conference. Jared is also the Chief Data Scientist of Lander Analytics, a data science consultancy based in New York City and an Adjunct Professor of Statistics at Columbia University. How does Jared think about creating safe and welcoming spaces for budding and practicing data scientists of all ilk? How does he put this into practice? How does he make people feel comfortable and at home in a field in which so many intelligent and curious people feel like imposters? What practical & specific considerations are there in creating this home for underrepresented groups? How does he stay ahead of the curve in terms of modern, up-to-date content and speakers for his meetup and conference?

"Cloud computing is a huge revolution in the computing space, and it's also probably going to be one of the most transformative technologies that any of us experience in our lifetime. " Paige Bailey, Senior Cloud Developer Advocate at Microsoft, in this episode of DataFramed. In this conversation with Hugo, Paige reports from the frontier of cloud-based data science technologies, having just been at the Microsoft Build and Google I/O conferences. What is the future of data science in the cloud? How can you get started? Stick around to find out and much, much more.

What do online experiments, data science and product development look like at Booking.com, the world’s largest accommodations provider? Join Hugo's conversation with Lukas Vermeer to find out. Lukas is responsible for experimentation at Booking in the broadest sense of the word: from Infrastructure and Tools used to run experiments, Methodology and Metrics that help people make decisions to Training and Culture that help people understand what to do. They'll be talking about how Booking leverages Data Science to help empower people to experience the world through the three pillars of exploratory analysis, qualitative research and quantitative studies. They'll also take a deep dive into the fact that data science isn't actually anywhere near as objective as you may think.

Building models of the world is dangerous and there are pitfalls everywhere, even down to the assumptions that you make. To find out about many statistical pitfalls, and how to build more robust data scientific models using statistical modeling, whether it be in tech, epidemiology, finance or anything else, join Hugo's chat with Michael Betancourt, a physicist, statistician and one of the core developers of the open source statistical modeling platform Stan.

How can data science help in the fight against cancer? What are its limitations? Find out in this conversation from the frontier of research. Hugo speaks with Sandy Griffith from Flatiron Health, a healthcare technology and services company focused on accelerating cancer research and improving patient care. Sandy is Principal methodologist on Flatiron's Quantitative Sciences team and is tasked with leveraging data science "To improve lives by learning from the experience of every cancer patient".

Anthony Goldbloom, CEO of Kaggle, speaks with Hugo about Kaggle, data science communities, reproducible data science, machine learning competitions and the future of data science in the cloud. If you thought that Kaggle was merely a platform for machine learning competitions, you have to check out this chat, because these ML comps account for less than a third of activity on Kaggle today. In the discussion: Kaggle kernels for reproducible data science and the evolution of the Kaggle public data platform; the genesis of Kaggle and how Anthony managed to solve the cold start problem of building a two-sided market place; the exciting implications of Kaggle's recent acquisition by Google for the future of cloud-based data science; why Python is dominant on Kaggle.

"We should be looking at Automated Machine Learning tools as more like data science assistants, rather than replacements for data scientists" -- Randy Olson, Lead Data Scientist at Life Epigenetics, Inc. Randy specializes in artificial intelligence, machine learning, and created TPOT, a Data Science Assistant and a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. Will the future of data science be automated? Which verticals will experience the largest disruption? What will the role of data science become? There's one way to find out: jump straight into this chat with Randy and Hugo.

Michelle Gill, a deep learning expert at NVIDIA, an Artificial Intelligence company that builds GPUs, the processors that everybody uses for deep learning, speaks with Hugo about the modern superpower of deep learning and where it has the largest impact, past, present and future, filtered through the lens of Michelle's work at NVIDIA. Where is the modern superpower of deep learning most effective? Where is it not? Where should we channel our skepticism of the hype surrounding it?

Sebastian Raschka, a machine learning aficionado, data analyst, author, python programmer, open source contributor, computational biologist, and occasional blogger, speaks with Hugo about the role of data science in modern biology and the power of deep learning in today's rapidly evolving data science landscape. How is Sebastian using deep learning to build facial recognition software that also prevents racial and gender profiling? Check out this week's episode to find out.

Drew Conway, world-renowned data scientist, entrepreneur, author, speaker and creator of the Data Science Venn Diagram speaks with Hugo about how to build data science teams, along with the unique challenges of building data science products for industrial users. How does Drew now view the Venn circles he created, those of hacking skills, mathematical and statistical knowledge and substantive expertise, when building out data science teams?