An introduction to doing data science with random numbers.

1. That’s like, so random!
Monte Carlo for Data Science
Corey Chivers, PhD
@cjbayesian

10. Drawing samples
is cool and all,
but what can I do with them?

11. 1. Understand where obscure
statistics come from

13. 2. Make your own statistic!

14. Chivers, C., Leung, B., Yan, N. D. (2014), Validation and calibration of probabilistic predictions
in ecology. Methods in Ecology and Evolution, 5: 1023–1032. doi: 10.1111/2041-210X.12238

15. 3.Avoid having to do hard
(and sometimes impossible)
math

18. 4. Understand what
inferences you can make with
your data

20. 4. Propagate uncertainty in
complex predictive models

21. See next talk!

22. 4. Run ‘what if’ scenarios

23. What if we were to see a surge in patients in a given unit, how would
this propagate to the rest of the hospital?

24. sim·u·di·dactic adj.*
/ˈsimyəˌdīdakt/
To understand by creating a representation
or model of real-world phenomena.
Particularly, using randomization and
computation to understand complex systems
and processes.
C2013: From Latin simulre, simult-, from similis, like and Greek
didaktos, taught;
* I totally just made this up,
but it could be a thing.

25. sim·u·di·dactic adj.*
/ˈsimyəˌdīdakt/
To understand by creating a representation
or model of real-world phenomena.
Particularly, using in randomization and
computation to understand complex systems
and processes.
C2013: From Latin simulre, simult-, from similis, like and Greek
didaktos, taught;

26. Data Science
Seeking Software Engineers (Sr. & mid-level) to help us build out
our real-time predictive application platform
http://www.med.upenn.edu/predictivehealthcare/
• Develop data products and predictive applications
• Collaborate with top medical professionals
• Revolutionize Health care delivery
Contact:
corey.chivers@uphs.upenn.edu @cjbayesian