1. Data Science
Sean Byrnes
http://seanbyrnes.com
@sbyrnes
in
Node.js

2. Who Am I?
f
ATTENDED
FOUNDED
CURRENTLY
from Yahoo!

3. Likely.js Recommendation Engine
Classify.js Naïve Bayes Classifier
Lyric Linear Regression
Logistical.js Logistic Regression (work in progress)
https://github.com/sbyrnes
https://www.npmjs.com/~sbyrnes

4. Data Science in Node.js
• What is Data Science?
• Why Node?
• Example 1: Regression Modeling
• Example 2: Recommender Systems
• Where to go next?

5. What is Data Science?
Software Engineering
+
Statistical Analysis

6. What is Data Science?
1. Question
2. Data Gathering
3. Exploration
4. Modeling
5. Answer
6. Production

7. What is Data Science?
1. Question
2. Data Gathering
3. Exploration
4. Modeling
5. Answer
6. Production

8. Why Node?
Pros
• Functional
• NPM
• Event driven
• D3
Cons
• Binary data
structures
• Speed
Alternatives:
• R
• Python
• Java
• C++
• Go

9. https://github.com/sbyrnes/DataSci
ence_in_Node.js

10. 1 // REGRESSION MODELING
Predicting the future is hard

11. Example 1: Regression Modeling
How many users will we have next month?

12. Example 1: Regression Modeling
0
50,000
100,000
150,000
200,000
250,000

13. Example 1: Regression Modeling
Given X(n) = [x1, x2, … xk]
with dependent value y(n)
find f(n) such that
f(x(n)) ~ y(n)

14. Example 1: Regression Modeling
Given X(n) = [x1, x2, … xk]
f(n) = c1x1 + c2x2 + c3x3 + … + cnxn ~ y(n)

15. Example 1: Regression Modeling
0
50,000
100,000
150,000
200,000
250,000

16. Example 1: Regression Modeling
Given X(n) = [x1, x2, … xk]
f(n) = c1x1 + c2x2 + c3x3 + … + cnxn
Or, maybe
f(n) = c1x1 + c2x1
2 + c3x2 + c4x2
2 + …+ cmxn
2

17. Example 1: Regression Modeling
0
50,000
100,000
150,000
200,000
250,000
2nd Degree Polynomial Mode

18. Example 1: Regression Modeling
0
50,000
100,000
150,000
200,000
250,000
4th Degree Polynomial Model

19. Example 1: Regression Modeling
0
50,000
100,000
150,000
200,000
250,000
6th Degree Polynomial Model

20. > Code

21. 2 // RECOMMENDER SYSTEMS
Predicting people is really hard

22. Example 2: Recommender Systems
What other products might this
customer buy?

23. Example 2: 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

24. Example 2: Recommender Systems
Given customer preference matrix M, find
P x Q ~ M
M

25. Example 2: Recommender Systems
Given customer preference matrix M, find
P x Q ~ M
X =
P Q M

26. Example 2: 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

27. > Code

28. Where to go next?
• Programming
• Statistics
• Machine learning
• Toolkit
– R
– Hadoop
– D3

29. Sean Byrnes
seanbyrnes.com
@sbyrnes
github.com/sbyrnes