Predictive Insights AI · Book
Contents
  1. Introduction
  2. Ch 0 · DataFest
  3. 1.1 Data exploration
  4. 1.2 Intro to modeling
  5. 1.3 GLMs
  6. 1.4 GLMs for classification
  7. 1.5 Classification metrics
  8. 1.6 More GLM topics
  9. 1.7 Variable selection
  10. 1.8 Tree-based models
  11. 1.9 Unsupervised learning
  12. Ch 2 · Cybersecurity
  13. 3.1 Health costs case study
  14. 3.2 Apartment applicants
  15. 3.3 Titanic case study
  16. Ch 4 · Tools
  17. Ch 5 · Seven mistakes
  18. Ch 6 · Entrepreneurship
  19. Wrap-up
  20. Appendix
  21. References
  22. PA R Study Manual

Predictive Analytics & Risk Modeling: For Insurance and Actuarial Science

Chapter 0: Highlights from Five College DataFest

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Volunteering at the Five College DataFest and what the event teaches about real-world data analysis.

Volunteering at this event was deeply rewarding, offering an opportunity to support advancements in AI that positively impact society and drive economic progress.  The professionals and academic researchers pictured here represent elite institutions—including Carnegie Mellon, UMass Amherst (highly ranked for computer science), Amherst College, and Brown University—as well as leading tech companies such as those within FAAMG.

The mission of DataFest is to expose undergraduate students to challenging questions with immediate real-world significance that can be addressed through data analysis. By working in teams, students with varying skill sets will combine their efforts and expand their collective data analysis horizons. Interaction among students, as well as with outside consultants, will promote the sense that data analysis is a dynamic, engaging, and vibrant part of our society, as well as a realistic, practical, and fun career path.

In 2017 I was a DataFest participant, and the data at the time was from Expedia - travel and vacation information.  Then five years later I landed a job working for Expedia on the actual datasets. In addition to advising the teams this weekend, I actually put in work to develop Python and R code, and a Tableau dashboard because I never like to advise other people on work that I can't do myself.   This year, the prize went to Mount Holyoke.

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