The beginning of this talk is applicable to a wide audience. Only the last step is JavaScript specific. Presentation given to UtahJS Meetup (https://utahjs.com/) on July 7, 2016. Introduction of LEAP framework and how to use AWS to create a predictive model and consume prediction with the AWS JavaScript SDK.

1. LEAP into Data Science!
With JavaScript

2. gonzoDavid Gonzalez
@datagonzo
www.linkedin.com/in/datagonzo
● Lots of data science stuff
● Lots of kids
● Lots of pets
● Playing outdoors

3. LEAP into Data Science!
With JavaScript
Why?!

4. ML + JS
Why?
● Smarter validation?
● Dynamic/personalize routing,
content, etc.?
● Both A & B?
● Automation of key points of
friction in your UI?

5. How?
Or: “Should I go to a bootcamp?”

7. Comparative availability of skills

8. Label
A category, data point, or
phenomenon you want to teach a
machine to learn to identify reliably
Explore Statistical and visual explorations of
your dataset
Analyze Process of learning how to predict a
trend or label from a dataset
Predict Categorizing, identifying, or
forecasting a trend from new data
LEAP into Data Science

9. LEAP
Specification
Version: Alpha
goo.gl/zL9Gpb

10. Labels
Classification
Forecast

11. Explore
http://vega.github.io/

12. Vega 2.0 Vega-liteD3

13. Analyze
What should you care about?
● Performant
● Exhaustive
● Accurate
● Repeatable

14. Analyze
What shouldn’t you care
about?
● Algos
● Languages
● Features

15. datalabel
Labeled
Dataset
Analyze
Saved
Analysis
Machine Learning
Algorithms

16. Label Text
positive Happy Birthday!
negative Get bent!
positive Hell Yeah!
negative #%*@ YOU!
Sentiment
Label Sales
1/1/2016 $170.00
1/2/2016 $141.00
1/3/2016 $160.00
1/4/2016 $139.00
1/5/2016 $200.00
1/6/2016 $193.00
1/7/2016 $163.00
1/8/2016 $200.00
Forecast
Label Image
cat
dog
Identify
Label Value Time
normal 1.4 142223600
normal 1.5 142223605
normal 1.6 142223610
anomaly 1.9 142223615
anomaly 2.1 142223620
anomaly 1.9 142223625
Anomaly
user
Action
Figures Clothing
Digital
Downloads Books
Customer 1 0 1 0 1
Customer 2 0 0 1 1
Customer 3 0 1 1 1
Customer 4 1 1 1 1
Customer 5 1 0 1 0
Customer 6 1 0 0 0
Recommendation
Label Last login
Avg Time
on Page
Age of
account
Red 1/1/2016 0:00:00 100 130
Yellow 4/2/2016 0:00:00 200 360
Green 4/3/2016 0:00:00 250 489
Recommendation

17. Predict
data label
Saved
Analysis

18. Label Text
positive Happy Birthday!
negative Get bent!
positive Hell Yeah!
negative #%*@ YOU!
positive Have a great day!
Sentiment
Label Sales
1/1/2016 $170.00
1/2/2016 $141.00
1/3/2016 $160.00
1/4/2016 $139.00
1/5/2016 $200.00
1/6/2016 $193.00
1/7/2016 $163.00
1/8/2016 $200.00
1/9/2016 $189.00
Forecast
Label Image
cat
dog
cat
Identify
Label Value Time
normal 1.4 142223600
normal 1.5 142223605
normal 1.6 142223610
anomaly 1.9 142223615
anomaly 2.1 142223620
anomaly 1.9 142223625
normal 1.8 142223630
Anomaly
user
Action
Figures Clothing
Digital
Downloads Books
Customer 1 0 1 0 1
Customer 2 0 0 1 1
Customer 3 0 1 1 1
Customer 4 1 1 1 1
Customer 5 1 0 1 0
Customer 6 1 0 0 0
Customer 7 0 1 1 1
Recommendation
Label Last login
Avg Time
on Page
Age of
account
Red 1/1/2016 0:00:00 100 130
Yellow 4/2/2016 0:00:00 200 360
Green 4/3/2016 0:00:00 250 489
Red 2/3/2016 0:00:00 140 90
Recommendation
New data Prediction

19. Walkthrough
Predict account creation with dataset: s3:
//aml-sample-data/banking.csv

20. Label
Dataset: Banking Marketing Data Set
https://archive.ics.uci.
edu/ml/datasets/Bank+Marketing
Label:
Has the client subscribed a term
deposit?
age job ... contact euribor3m nr_employed y
44 blue-collar cellular 4.963 5228.1 0
53 technician cellular 4.021 5195.8 0
28 management cellular 0.729 4991.6 1
39 services cellular 1.405 5099.1 0
55 retired cellular 0.869 5076.2 1
30 management cellular 4.961 5228.1 0
37 blue-collar cellular 1.327 5099.1 0
39 blue-collar cellular 1.313 5099.1 0
36 admin. cellular 1.266 5076.2 1
27 blue-collar cellular 1.41 5099.1 0
34 housemaid telephone 4.864 5191 0
41 management cellular 4.964 5228.1 0

21. Explore
w/ Voyager

22. Analyze
w/ AWS Machine Learning

23. Analyze
w/ AWS Machine Learning

24. Analyze
w/ AWS Machine Learning

25. Analyze
w/ AWS Machine Learning

26. Analyze
w/ AWS Machine Learning

27. Analyze
w/ AWS Machine Learning

28. Analyze
w/ AWS Machine Learning

29. Analyze
w/ AWS Machine Learning

30. Analyze
w/ AWS Machine Learning

31. Analyze
w/ AWS Machine Learning

32. Analyze
w/ AWS Machine Learning

33. Analyze
w/ AWS Machine Learning

34. Analyze
w/ AWS Machine Learning

35. Analyze
w/ AWS Machine Learning

36. Predict
w/ AWS Machine Learning

37. Predict
w/ JavaScript SDK
Documentation:
http://docs.aws.amazon.
com/AWSJavaScriptSDK/latest/AWS/MachineLearning.
html#predict-property

38. Links
● LEAP into Data Science (blog post)
● How to create Citizen Data Scientists
● LEAP Specification
● Vega & Vega Online Editor
● Voyager (Explore your data)
● AWS Machine Learning JavaScript
SDK docs
● Example prediction w/ javascript
● Commentable version of these slides
● Experimental single-step app