Podcast appearances and mentions of jared lander

Trends in open-source AI: Join Jon Krohn and a panel of data science icons as they discuss the most exciting and concerning developments in open-source AI. Hear insights from Drew Conway, Jared Lander, Emily Zabor, and JD Long on the transformative potential of AI and its future impact. Additional materials: www.superdatascience.com/794 Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

The experts reveal their top open-source R libraries with us live from the New York R Conference! This Super Data Science Podcast episode features an exclusive panel with data science trailblazers Drew Conway, Jared Lander, Emily Zabor, and JD Long. They share their favorite R libraries and valuable insights to enhance your data science practice. Additional materials: www.superdatascience.com/790 Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.

Jared Lander joins us to discuss his work as an R meetup organizer, the upcoming virtual R Conference, and his work as a consultant for a variety of companies from metal workers to professional football teams. In this episode you will learn: • Jared's R meetups and our professional history [3:27] • NYHackR [6:42] • The R Conference [13:25] • R for Everyone [18:55] • Lander Analytics [22:10] • Job openings at Lander Analytics [25:04] • R vs. Python [29:15] • The importance of pizza in Jared's life [32:19] Additional materials: www.superdatascience.com/501

This week, Hugo speaks with Debbie Berebichez about the importance of critical thinking in data science. Debbie is a physicist, TV host and data scientist and is currently the Chief Data Scientist at Metis in NY.In a world and a professional space plagued by buzz terms like AI, big data, deep learning, and neural networks, conversations around skill sets and less than productive programming language wars, what has happened to critical thinking in data science and data thinking in general? What type of critical thinking skills are even necessary as data science, AI and machine learning become even more present in all of our lives and how spread out do they need to be across organizations and society? Listen to find out!LINKS FROM THE SHOWDATAFRAMED GUEST SUGGESTIONSDataFramed Guest Suggestions (who do you want to hear on DataFramed?)FROM THE INTERVIEWDebbie on TwitterDebbie's WebsiteDebbie Berebichez- Media Reel (Video)Deborah Berebichez' Keynote at Grace Hopper Celebration 2017 (Video)Debbie Berebichez on Perseverance and Paying it Forward (Video)Things about the Future and the Future of Things (By Debbie Berebichez, Video)FROM THE SEGMENTSData Science tools for getting stuff done and giving it to the world (with Jared Lander ~21:55)Lander Analytics WebsiteDocker Websiteplumber WebsiteStatistical Distributions and their Stories (with Justin Bois ~39:30)Probability distributions and their stories (By Justin Bois)The History of Statistics (By Stephen M. Stigler)The Evolution of the Normal Distribution (By Saul Stahl)Original music and sounds by The Sticks.

Jared Lander, the organizer of NYHackR and general data science guru, joined us to talk about the landscape of AI techniques, how deep learning fits into that landscape, and why you might consider using R for ML/AI.

Jared Lander, the organizer of NYHackR and general data science guru, joined us to talk about the landscape of AI techniques, how deep learning fits into that landscape, and why you might consider using R for ML/AI.

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?