Linear Digressions

289 Episoade

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

   Publicat: 15.03.2020
2. Causal inference when you can't experiment: difference-in-differences and synthetic controls

   Publicat: 09.03.2020
3. Better know a distribution: the Poisson distribution

   Publicat: 02.03.2020
4. The Lottery Ticket Hypothesis

   Publicat: 23.02.2020
5. Interesting technical issues prompted by GDPR and data privacy concerns

   Publicat: 17.02.2020
6. Thinking of data science initiatives as innovation initiatives

   Publicat: 10.02.2020
7. Building a curriculum for educating data scientists: Interview with Prof. Xiao-Li Meng

   Publicat: 02.02.2020
8. Running experiments when there are network effects

   Publicat: 27.01.2020
9. Zeroing in on what makes adversarial examples possible

   Publicat: 20.01.2020
10. Unsupervised Dimensionality Reduction: UMAP vs t-SNE

    Publicat: 13.01.2020
11. Data scientists: beware of simple metrics

    Publicat: 05.01.2020
12. Communicating data science, from academia to industry

    Publicat: 30.12.2019
13. Optimizing for the short-term vs. the long-term

    Publicat: 23.12.2019
14. Interview with Prof. Andrew Lo, on using data science to inform complex business decisions

    Publicat: 16.12.2019
15. Using machine learning to predict drug approvals

    Publicat: 08.12.2019
16. Facial recognition, society, and the law

    Publicat: 02.12.2019
17. Lessons learned from doing data science, at scale, in industry

    Publicat: 25.11.2019
18. Varsity A/B Testing

    Publicat: 18.11.2019
19. The Care and Feeding of Data Scientists: Growing Careers

    Publicat: 11.11.2019
20. The Care and Feeding of Data Scientists: Recruiting and Hiring Data Scientists

    Publicat: 04.11.2019