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That’s like, so random!
Monte Carlo for Data Science
Corey Chivers, PhD
@cjbayesian
Drawing samples
is cool and all,
but what can I do with them?
1. Understand where obscure
statistics come from
2. Make your own statistic!
Chivers, C., Leung, B., Yan, N. D. (2014), Validation and calibration of probabilistic predictions
in ecology. Methods in ...
3.Avoid having to do hard
(and sometimes impossible)
math
4. Understand what
inferences you can make with
your data
4. Propagate uncertainty in
complex predictive models
See next talk!
4. Run ‘what if’ scenarios
What if we were to see a surge in patients in a given unit, how would
this propagate to the rest of the hospital?
sim·u·di·dactic adj.*
/ˈsimyəˌdīdakt/
To understand by creating a representation
or model of real-world phenomena.
Particu...
sim·u·di·dactic adj.*
/ˈsimyəˌdīdakt/
To understand by creating a representation
or model of real-world phenomena.
Particu...
Data Science
Seeking Software Engineers (Sr. & mid-level) to help us build out
our real-time predictive application platfo...
That's like, so random! Monte Carlo for Data Science
That's like, so random! Monte Carlo for Data Science
That's like, so random! Monte Carlo for Data Science
That's like, so random! Monte Carlo for Data Science
That's like, so random! Monte Carlo for Data Science
That's like, so random! Monte Carlo for Data Science
That's like, so random! Monte Carlo for Data Science
That's like, so random! Monte Carlo for Data Science
That's like, so random! Monte Carlo for Data Science
That's like, so random! Monte Carlo for Data Science
That's like, so random! Monte Carlo for Data Science
That's like, so random! Monte Carlo for Data Science
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That's like, so random! Monte Carlo for Data Science

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An introduction to doing data science with random numbers.

Publié dans : Données & analyses
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That's like, so random! Monte Carlo for Data Science

  1. 1. That’s like, so random! Monte Carlo for Data Science Corey Chivers, PhD @cjbayesian
  2. 2. Drawing samples is cool and all, but what can I do with them?
  3. 3. 1. Understand where obscure statistics come from
  4. 4. 2. Make your own statistic!
  5. 5. 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
  6. 6. 3.Avoid having to do hard (and sometimes impossible) math
  7. 7. 4. Understand what inferences you can make with your data
  8. 8. 4. Propagate uncertainty in complex predictive models
  9. 9. See next talk!
  10. 10. 4. Run ‘what if’ scenarios
  11. 11. What if we were to see a surge in patients in a given unit, how would this propagate to the rest of the hospital?
  12. 12. 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.
  13. 13. 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;
  14. 14. 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

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