Introduction to Data Science
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What is Data Science? Why are Data Scientists so sought after at modern technology companies? In this talk, I answer those questions by reviewing the basics of data science and 3 examples of typical data science projects.
![Example 2: Regression Modeling
Assume X(ni) = [x1, x2, xk]
f(n) = c1x1 + c2x2 + c3x3 + + cnxn](https://image.slidesharecdn.com/datascience101-141023225318-conversion-gate02/85/Introduction-to-Data-Science-15-320.jpg)
![Example 2: Regression Modeling
Assume X(ni) = [x1, x2, xk]
f(n) = c1x1 + c2x2 + c3x3 + + cnxn
Or, maybe
f(n) = c1x1 + c2x1
2 + c3x2 + c4x2
2 + + cmxn
2](https://image.slidesharecdn.com/datascience101-141023225318-conversion-gate02/85/Introduction-to-Data-Science-17-320.jpg)
![Example 3: Recommender Systems
Given customer preferences c[p1,p2,pn]
and overall rating average roverall
cbias = mean(c[p1], c[p2], c[pn]) roverall](https://image.slidesharecdn.com/datascience101-141023225318-conversion-gate02/85/Introduction-to-Data-Science-25-320.jpg)
Introduction to Data Science
- 1. Introduction Sean Byrnes http://seanbyrnes.com @sbyrnes to Data Science
- 2. Who Am I? f ATTENDED FOUNDED CURRENTLY from Yahoo!
- 3. Introduction to Data Science What is Data Science? Example 1: Basic Math Example 2: Regression Modeling Example 3: Recommender Systems Getting started in data science
- 4. What is Data Science? Software Engineering + Statistical Analysis
- 5. What is Data Science? 1. Question 2. Data Gathering 3. Exploration 4. Modeling 5. Answer 6. Production
- 6. Example 1: Basic Math What is my customer churn rate? def. Churn rate: The percentage of subscribers to a service that discontinue their subscription to that service in a given time period. (aka attrition rate)
- 7. Example 1: Basic Math # customers at start Churn(month) = # customers lost
- 8. Example 1: Basic Math Month Churn Dec '13 3.75% Nov '13 1.87% Oct '13 3.82% Sep '13 2.76% Aug '13 2.43% Jul '13 2.04% Jun '13 1.60%
- 9. Example 1: Basic Math For all customers acquired in a given month Retention(Cmonth) = Active(Cmonth) Total(Cmonth)
- 10. Example 1: Basic Math 0 1 2 3 4 5 6 Dec '13 100% 12.82% 8.04% 6.34% 4.91% 3.95% 3.14% Nov '13 100% 15.66% 9.97% 6.96% 5.46% 3.88% 2.77% Oct '13 100% 16% 10.86% 8.62% 6.22% 5.06% 3.98% Sep '13 100% 13.28% 9.52% 7.28% 5.28% 4.48% 4% Aug '13 100% 12.96% 9.18% 6.55% 4.73% 3.86% 3.13% Jul '13 100% 15.84% 10.85% 8.27% 6.67% 5.60% 4.63% Jun '13 100% 16.08% 11.36% 8.36% 7.07% 6% 5.25%
- 11. Example 1: Basic Math 0 1 2 3 4 5 6 Dec '13 100% 12.82% 8.04% 6.34% 4.91% 3.95% 3.14% Nov '13 100% 15.66% 9.97% 6.96% 5.46% 3.88% 2.77% Oct '13 100% 16% 10.86% 8.62% 6.22% 5.06% 3.98% Sep '13 100% 13.28% 9.52% 7.28% 5.28% 4.48% 4% Aug '13 100% 12.96% 9.18% 6.55% 4.73% 3.86% 3.13% Jul '13 100% 15.84% 10.85% 8.27% 6.67% 5.60% 4.63% Jun '13 100% 16.08% 11.36% 8.36% 7.07% 6% 5.25%
- 12. Example 2: Regression Modeling How many users will we have next month?
- 13. Example 2: Regression Modeling 160,000 140,000 120,000 100,000 80,000 60,000 40,000 20,000 - 1/1/13 2/1/13 3/1/13 4/1/13 5/1/13 6/1/13 7/1/13 8/1/13 9/1/13 10/1/13 11/1/13 12/1/13
- 14. Example 2: Regression Modeling For data set X(n), find f(n) such that f(ni) ~ X(ni)
- 15. Example 2: Regression Modeling Assume X(ni) = [x1, x2, xk] f(n) = c1x1 + c2x2 + c3x3 + + cnxn
- 16. Example 2: Regression Modeling 160,000 140,000 120,000 100,000 80,000 60,000 40,000 20,000 - 1/1/13 2/1/13 3/1/13 4/1/13 5/1/13 6/1/13 7/1/13 8/1/13 9/1/13 10/1/13 11/1/13 12/1/13 Linear Model
- 17. Example 2: Regression Modeling Assume X(ni) = [x1, x2, xk] f(n) = c1x1 + c2x2 + c3x3 + + cnxn Or, maybe f(n) = c1x1 + c2x1 2 + c3x2 + c4x2 2 + + cmxn 2
- 18. Example 2: Regression Modeling 160,000 140,000 120,000 100,000 80,000 60,000 40,000 20,000 - 1/1/13 2/1/13 3/1/13 4/1/13 5/1/13 6/1/13 7/1/13 8/1/13 9/1/13 10/1/13 11/1/13 12/1/13 2nd Degree Polynomial Model
- 19. Example 2: Regression Modeling 160,000 140,000 120,000 100,000 80,000 60,000 40,000 20,000 - 1/1/13 2/1/13 3/1/13 4/1/13 5/1/13 6/1/13 7/1/13 8/1/13 9/1/13 10/1/13 11/1/13 12/1/13 4th Degree Polynomial Model
- 20. Example 2: Regression Modeling https://github.com/sbyrnes/Lyric
- 21. Example 3: Recommender Systems What other products might this customer buy?
- 22. Example 3: Recommender Systems Product 1 Product 2 Product 3 Product N Customer 1 3.5 4.0 3.0 Customer 2 2.0 3.5 Customer 3 3.0 2.5 Customer N 4.5 4.5
- 23. Example 3: Recommender Systems Given customer preference matrix M, find P x Q ~ M
- 24. Example 3: Recommender Systems Product 1 Product 2 Product 3 Product N Customer 1 3.5 4.0 2.5 3.0 Customer 2 2.0 1.5 3.5 3.0 Customer 3 1.5 3.0 2.5 4.0 Customer N 4.5 3.5 4.0 4.5
- 25. Example 3: Recommender Systems Given customer preferences c[p1,p2,pn] and overall rating average roverall cbias = mean(c[p1], c[p2], c[pn]) roverall
- 26. Example 3: Recommender Systems https://github.com/sbyrnes/likely.js
- 27. Getting Started in Data Science Programming Statistics Machine learning Toolkit R Hadoop D3
- 29. Sean Byrnes seanbyrnes.com @sbyrnes github.com/sbyrnes