Statistics for Data Science: what you should know and why

Talk at IBM Community Day: Data Science, Online 2017-07-24

- https://ibmdatascienceday.bemyapp.com/talks

1. Statistics for Data Science: what you should know and why 
 —
 Gabriela de Queiroz
 Senior Developer Advocate @ IBM Founder of R-Ladies http://codait.org Ladies http://rladies.org
2. Agenda • Know your data • Correlation (and Causation) • A/B test • Statistical Models • Probability Bonus: R-Ladies
3. Where are you coming from?
4. Lonely Statistician Lonely Data Scientist
5. TOP 5 STATISTICAL CONCEPTS
6. 1. Know your data
7. Some ways to know your data
8. Summary Statistics
9. Anscombe's quartet
10. The Datasaurus Dozen AutoDesk Research: https://www.autodeskresearch.com/publications/samestats R-package: https://github.com/stephlocke/datasauRus
11. Be sure to plot your data!
12. Think twice before using it source: http://extremepresentation.typepad.com/blog/2006/09/choosing_a_good.html
13. 2. Correlation* ρ = -1 ρ = +1 * Pearson correlation
14. Correlation describes the strength of the linear relationship between two variables.
15. What can we say about this chart? Credits: http://www2.stat.duke.edu/~mc301/ARTSCI101_Su16/post/slides/w2_d2_smoking_research.pdf
16. ICE CREAM SALES SHARK ATTACKS? CAUSE
17. ICE CREAM SALES SHARK ATTACKS? CAUSE X SUMMER?
18. observer
19. umbrella => rain
20. Where is the rain???
21. Correlation doesn’t imply causation
22. Causation vs Correlation • Causality indicates that one event is the result of the occurrence of the other event. • Correlation between two things can be caused by a third factor (confounder) that affects both of them.
23. Is there any time where correlation implies causation? The gold standard for establishing cause and effect is a controlled trial (aka A/B test).
24. 3. A/B Testing
25. A/B Testing Online experiments are used to test a new design, a machine learning model, or any new feature.
26. A/B Testing - Hypothesis Tests A hypothesis test is a way to decide whether the data strongly support one point of view or another.
27. How do you set up an experiment?
28. DEFINE THE GOAL AND FORM THE HYPOTHESIS
29. DEFINE THE GOAL AND FORM THE HYPOTHESIS 'SPN
    TUBUT
    IZQPUIFTJT
    UFTUT
    TJHOJpDBODF
    MFWFM
30. IDENTIFY THE CONTROL AND THE TREATMENT GROUP
31. IDENTIFY KEY METRICS AND DESIRED IMPROVEMENT 'SPN
    TUBUT
    F⒎FDU
    TJ[F
    
32. DETERMINE THE FRACTION IN BOTH GROUPS
33. RUN THE TEST FOR A CERTAIN AMOUNT OF TIME 'SPN
    TUBUT
    TBNQMF
    TJ[F
    
34. ANALYZE THE RESULTS
35. 4. Statistical Models
36. The response is the one whose content we are trying to model with other variables (explanatory variables) In any given model: • response variable (Y) • explanatory variables (X1, . . . .Xn)
37. Examples of models Time Series Linear Regression Non-Linear Regression
38. Use Case: Improve Sales of a product • Let’s say we were hired to provide advice on how to improve sales of a particular product. • Our goal is to develop a model that can be used to predict sales based on these 3 media budgets. Example extracted from the book "An Introduction to Statistical Learning with Applications in R"
39. The data consists of the sales of the product in 200 different markets, along with advertising budgets for the product in each of those markets for three different media: TV, radio, and newspaper.
40. output variable: sales (in thousands of units) input variables: advertising budgets (in thousands of dollars) The sales for a particular product is a function of advertising budgets.
41. Suppose we are asked to suggest a marketing plan for next year that will result in high product sales. WHAT INFORMATION WOULD BE USEFUL TO PROVIDE?
42. 1. Is there a relationship between advertising budget and sales? Our first goal should be to determine whether the data provide evidence of an association between advertising spend and sales.
43. 2. How strong is the relationship between advertising budget and sales?
44. 3. Which media contribute to sales? Do all three media contribute to sales, or do just one or two?
45. 4. How accurately can we estimate the effect of each media on sales? For every dollar spent on advertising in a particular media, by what amount will sales increase?
46. 5. How accurately can we predict future sales? For any given advertising, what is our prediction for sales, and what is the accuracy of this prediction?
47. 6. Is the relationship linear? If the relationship between advertising spend in the various media and sales is approximately a straight-line then linear regression is an appropriate tool. If not, then it may still be possible to transform the predictor or the response so that linear regression can be used.
48. We could answer all those questions by setting up a multiple linear regression: sales = 0 + 1TV + 2radio + 3newspaper + ✏
49. Why can’t we throw all these in a black box algorithm?
50. INTERPRETABILITY
51. 5. Probability
52. • Naive Bayes • Logistic Regression • k-NN • Latent Dirichlet Allocation (LDA) • Decision Trees • Association Rules (ex: Basket Analysis) • …
53. It doesn’t matter what technique you choose, the most important skill is critical thinking.
54. Resources
56. Worldwide organization that promotes gender diversity in the R community via meetups and mentorship in a friendly and safe environment
57. Our mission More women/non-binary • coders • developers • speakers • leaders More gender minorities developing R packages and being part of the R community.
58. http://bit.ly/rladiesgroups
59. How can I start my own chapter? #rcatladies
60. Send an email to info@rladies.org
61. We’ll send everything you'll need! #rdogladies
62. What do you get? 1) Starter-Kit ▪ Tech Infrastructure ▪ Tips on how to organize events ▪ Code of Conduct (En/Spanish) 2) @rladies.org email 3) Organizer slack channel 4) Shared training material 5) Financial support to cover meetup registration/renewal fees!
63. And there is more! YOU WILL: - be part of an incredible family - learn a lot (not onlyR)! - have unlimited support - meet other R-Ladies + MUCH MORE!
64. COME JOIN US!
65. Make sure to schedule your 1:1 Session https://ibmcommunityday.bemyapp.com/#/mentors You can also reach me via: twitter: @gdequeiroz linkedin: http://bit.ly/linkedin-gdq
66. THANK YOU! @RLadiesGlobal www.rladies.org @gdequeiroz www.k-roz.com
67. Aditional Resources • R-Ladies: www.rladies.org • Call for code: https://developer.ibm.com/callforcode/ • Intro to Statistics with R - DataCamp • R4DS: http://r4ds.had.co.nz/ • Think Stats - Probability and Statistics for Programmers: http://greenteapress.com/thinkstats/ • Statistical Learning online class: https://online.stanford.edu/ courses/sohs-ystatslearning-statistical-learning-self-paced
68. Continue the conversation and join: https://community.ibm.com/datascience