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Data science in Node.js

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Sean Byrnes
Sean Byrnes

This document provides an overview of data science and how it can be done in Node.js. It defines data science as combining software engineering and statistical analysis. It discusses regression modeling and recommender systems as examples. Regression modeling predicts future values like number of users based on past data. Recommender systems predict what other products a customer may buy based on their preferences. Node.js is recommended for data science due to its event-driven asynchronous nature and packages like NPM and D3.js. Code examples are provided for both techniques.

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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 + c2x1 2 + c3x2 + c4x2 2 + …+ cmxn 2
Example 1: Regression Modeling 0 50,000 100,000 150,000 200,000 250,000 2nd Degree Polynomial Mode
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 N 4.5 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 N 4.5 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

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Data science in Node.js

Editor's Notes

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  2. Explain what is going to happen!
  3. Explain what is going to happen!