Data science in Node.js
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Data Science
Sean Byrnes
http://seanbyrnes.com
@sbyrnes
in
Node.js
Who Am I?
f
ATTENDED
FOUNDED
CURRENTLY
from Yahoo!
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
Data Science in Node.js
• What is Data Science?
• Why Node?
• Example 1: Regression Modeling
• Example 2: Recommender Systems
• Where to go next?
What is Data Science?
Software Engineering
+
Statistical Analysis
What is Data Science?
1. Question
2. Data Gathering
3. Exploration
4. Modeling
5. Answer
6. Production
What is Data Science?
1. Question
2. Data Gathering
3. Exploration
4. Modeling
5. Answer
6. Production
Why Node?
Pros
• Functional
• NPM
• Event driven
• D3
Cons
• Binary data
structures
• Speed
Alternatives:
• R
• Python
• Java
• C++
• Go
https://github.com/sbyrnes/DataSci
ence_in_Node.js
1 // REGRESSION MODELING
Predicting the future is hard
Example 1: Regression Modeling
How many users will we have next month?
Example 1: Regression Modeling
0
50,000
100,000
150,000
200,000
250,000
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)
Example 1: Regression Modeling
Given X(n) = [x1, x2, … xk]
f(n) = c1x1 + c2x2 + c3x3 + … + cnxn ~ y(n)
Example 1: Regression Modeling
0
50,000
100,000
150,000
200,000
250,000
Example 1: Regression Modeling
Given X(n) = [x1, x2, … xk]
f(n) = c1x1 + c2x2 + c3x3 + … + cnxn
Or, maybe
f(n) = c1x1 + c2x12 + c3x2 + c4x2
2 + …+ cmxn2
Example 1: Regression Modeling
0
50,000
100,000
150,000
200,000
250,000
2nd Degree Polynomial Model
Example 1: Regression Modeling
0
50,000
100,000
150,000
200,000
250,000
4th Degree Polynomial Model
Example 1: Regression Modeling
0
50,000
100,000
150,000
200,000
250,000
6th Degree Polynomial Model
> Code
2 // RECOMMENDER SYSTEMS
Predicting people is really hard
Example 2: Recommender Systems
What other products might this
customer buy?
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
N4.5 4.5
Example 2: Recommender Systems
Given customer preference matrix M, find
P x Q ~ M
M
Example 2: Recommender Systems
Given customer preference matrix M, find
P x Q ~ M
X =
P Q M
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
N4.5 3.5 4.0 4.5
> Code
Where to go next?
• Programming
• Statistics
• Machine learning
• Toolkit
– R
– Hadoop
– D3
Sean Byrnes
seanbyrnes.com
@sbyrnes
github.com/sbyrnes