1. your name Tweaking Intro Stats in the Age of n = All Glenn Miller Borough of Manhattan Community College AMATYC Conference New Orleans November 20, 2015

2. your name Big Data and GAISE Emphasize statistical literacy and statistical thinking Use real data Stress conceptual understanding Foster active learning Use technology (to develop understanding and to analyse data) Use assessments to improve and evaluate student learning

3. your name My focus: My Introduction to Statistics course is focused on the Central Limit Theorem What mathematical content is relevant for N = All?

4. your name Goodness-of-fit? Experimental design Data mining Replication & Bootstrapping Various test statistics Deterministic, axiomatic methods Math modelling

5. your name What is Big Data? Targeted marketing (Netflix, Amazon, Google ads, political campaigns) Spam filter- log form of Bayes Theorem Health care- large scale factor analysis or clustering analysis on unstructured data LinkedIn – graph theory

6. your name Issues in Analysing Big Data Correlation is not causation, but it is... $$$ profitable The Four V’s: Velocity, Variability, Veracity and Volume Finding the signal in the noise of unstructured data Apophenia: seeing patterns where none actually exist: Replication?

7. your name Issues in Analysing Big Data Outliers: Dirty data or a Black Swan? Smoothing- do not want a model to overfit the data N = All of what? Use of Twitter data

8. your name Tweak #1: Math Modelling

9. your name Goodness-of-fit? Is it normal?? (Normal approximation to the binomial)

10. your name Goodness-of-fit? Is it mathematics? Normal approximation to the binomial

11. your name Goodness-of-fit? Is it normal?? Return on S&P

12. your name Goodness-of-fit? Is it normal?? Return on S&P

13. your name Goodness-of-fit? Is it Pareto?? (Zipf’s Law)

14. your name Tweak #2: De-emphasize experimental design With less emphasis on inference, sampling techniques are less important (and not mathematical) Emphasize type of data in that part of the course

15. your name Tweak #3: The Project Have students choose their own data set from online source Allow the research question to be chosen after the selection of data set (global warming, education research,…)

16. your name Tweak #4: Deeper not Broader Which hypothesis tests matter? Again, something has to go... Confidence interval vs hypothesis test

17. your name Tweak #5: Rethink the Role of Probability Probability’s role in inference? Conditional probability and Bayes’ Theorem Independence and dependence as information theory This is where N=All has always been in our course!

18. your name From ASA Webinar by Nicholas Horton:

19. your name Bibliography Gould, R. (2015) Intro Stats and GAISE in the Age of Big Data AMATYC Webinar, Available at http://www.amatyc.org/?page=Webinars, August 17, 2015 Hand, D. J. (1999). Statistics and data mining: intersecting disciplines. ACM SIGKDD Explorations Newsletter, 1(1), 16-19. Hastie, T., Tibshirani, R., Friedman, J., & Franklin, J. (2005). The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer, 27(2), 83-85. Mayer-Schönberger, Viktor, and Kenneth Cukier. Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt, 2013. Miller, G. (2013) Implementing GAISE at Community Colleges: Benefits and Challenges, MathAMATYC Educator, 5(1), 9 -12 Rudder, C. (2014). Dataclysm: Who We Are (When We Think No One's Looking). Random House Incorporated. Various presenters, Mathematics in Data Science Conference, ICERM Institute for Computational and Experimental Research in Mathematics Conference, Brown University, July 28-30, 2015 https://icerm.brown.edu/topical_workshops/tw15-6-mds/

20. your name Bibliography American Statistical Association Undergraduate Guidelines Workgroup. 2014. 2014 curriculum guidelines for undergraduate programs in statistical science. Alexandria,VA: American Statistical Association. Mandelbrot, Benoit, and Richard L. Hudson. The Misbehavior of Markets: A fractal view of financial turbulence. Basic Books, 2014. gmiller@bmcc.cuny.edu