Linear Digressions

289 Episodes

1. Network effects re-release: when the power of a public health measure lies in widespread adoption

   Published: 3/15/2020
2. Causal inference when you can't experiment: difference-in-differences and synthetic controls

   Published: 3/9/2020
3. Better know a distribution: the Poisson distribution

   Published: 3/2/2020
4. The Lottery Ticket Hypothesis

   Published: 2/23/2020
5. Interesting technical issues prompted by GDPR and data privacy concerns

   Published: 2/17/2020
6. Thinking of data science initiatives as innovation initiatives

   Published: 2/10/2020
7. Building a curriculum for educating data scientists: Interview with Prof. Xiao-Li Meng

   Published: 2/2/2020
8. Running experiments when there are network effects

   Published: 1/27/2020
9. Zeroing in on what makes adversarial examples possible

   Published: 1/20/2020
10. Unsupervised Dimensionality Reduction: UMAP vs t-SNE

    Published: 1/13/2020
11. Data scientists: beware of simple metrics

    Published: 1/5/2020
12. Communicating data science, from academia to industry

    Published: 12/30/2019
13. Optimizing for the short-term vs. the long-term

    Published: 12/23/2019
14. Interview with Prof. Andrew Lo, on using data science to inform complex business decisions

    Published: 12/16/2019
15. Using machine learning to predict drug approvals

    Published: 12/8/2019
16. Facial recognition, society, and the law

    Published: 12/2/2019
17. Lessons learned from doing data science, at scale, in industry

    Published: 11/25/2019
18. Varsity A/B Testing

    Published: 11/18/2019
19. The Care and Feeding of Data Scientists: Growing Careers

    Published: 11/11/2019
20. The Care and Feeding of Data Scientists: Recruiting and Hiring Data Scientists

    Published: 11/4/2019