Discussion Panel: Statistics in the Big Data Era

on YouTube ↗01h 00m

" summary

Panelists trace concrete shifts: algorithms meet inference, with stochastic gradient descent and online methods replacing old stochastic approximation; routine tools named are cross-validation, Gibbs samplers and bootstrap; hot problems are post‑selection inference, multiple comparisons and high‑dimensional sparsity; education moves the field via Data 8 curriculum and large courses teaching coding plus inference; ethics and deployment issues focus on causality, privacy, fairness and explainability rather than abstract debates — useful if wanting practical roadmaps.

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" chapters (21)

1. 0:00Introduction
2. 1:35Peter
3. 18:57Dr Peter
4. 22:55Data Science Program
5. 26:40Machine Learning
6. 27:10Most important skills for PhD students
7. 30:20Writing
8. 31:45Data Skills
9. 34:30Impact of Big Data
10. 35:15Role of Statisticians
11. 37:30Numbers of Risk
12. 38:40Communication and Engagement
13. 40:55Graduate Education
14. 42:30Interim Research
15. 42:55Audience Comments
16. 44:20Interdisciplinary Interaction
17. 45:25Blog
18. 46:20Tools
19. 49:00Data Science vs Statistics
20. 50:25Computer Vision Machine Learning
21. 58:35Experimentation AI