Towards Data Science

131 Episodes

1. 51. Adrien Treuille and Tim Conkling - Streamlit Is All You Need

   Published: 9/16/2020
2. 50. Ken Jee - Building your brand in data science

   Published: 9/9/2020
3. 49. Catherine Zhou - The data science of learning

   Published: 9/2/2020
4. 48. Emmanuel Ameisen - Beyond the jupyter notebook: how to build data science products

   Published: 8/26/2020
5. 47. Goku Mohandas - Industry research and how to show off your projects

   Published: 8/19/2020
6. 46. Ihab Ilyas - Data cleaning is finally being automated

   Published: 8/12/2020
7. 45. Kenny Ning - Is data science merging with data engineering?

   Published: 8/5/2020
8. 44. Jakob Foerster - Multi-agent reinforcement learning and the future of AI

   Published: 7/29/2020
9. 43. Ian Scott - Data science at Deloitte

   Published: 7/22/2020
10. 42. Will Grathwohl - Energy-based models and the future of generative algorithms

    Published: 7/15/2020
11. 41. Solmaz Shahalizadeh - Data science in high-growth companies

    Published: 7/8/2020
12. 40. David Meza - Data science at NASA

    Published: 7/1/2020
13. 39. Nick Pogrebnyakov - Data science at Reuters, and the remote work after the coronavirus

    Published: 6/24/2020
14. 38. Matthew Stewart - Data privacy and machine learning in environmental science

    Published: 6/17/2020
15. 37. Sean Knapp - The brave new world of data engineering

    Published: 6/10/2020
16. 36. Max Welling - The future of machine learning

    Published: 6/3/2020
17. 35. Rubén Harris - Learning and looking for jobs in quarantine

    Published: 5/27/2020
18. 34. Denise Gosnell and Matthias Broecheler - You should really learn about graph databases. Here’s why.

    Published: 5/20/2020
19. 33. Roland Memisevic - Machines that can see and hear

    Published: 5/13/2020
20. 32. Bahador Khalegi - Explainable AI and AI interpretability

    Published: 5/6/2020