Ofer Ron, senior data scientist at LivePerson. Recently, I've had the pleasure of presenting an introduction to Data Science and data driven products at DevconTLV I focused this talk around the basic ideas of data science, not the technology used, since I thought that far too many times companies and developers rush to play around with "big data" related technologies, instead of figuring out what questions they want to answer, and whether these answers form a successful product.

1. Data Science 101
Ofer Ron | DevCon – October 2013

2. Just what is data science anyway?
Data science has many different
manifestations:
• BI
–find new markets and niches to target.
• Information
• Predictive
retrieval – search engines.
modeling –personalization of the
user experience.

3. Building data driven products
Increase each user’s
exposure to the social
network by easing him
in.

4. Building data driven products
Increase revenues through
recommendations.

5. Building data driven products

6. Building data driven products
Targeting visitors
needing assistance.

7. Toys, beautiful toys
What is the right question to ask?
Data science is not about technology.
It is about using data to solve business
needs.

8. That BIG DATA thing
What is Big Data?
“Big data” is when the size of the
data itself becomes part of the
problem.
Small social graph
Large distributed
social graphs

9. Hypothesize – test - iterate
The basics of data science
• Examine the data
• Perform basic analysis
• Formulate a hypothesis
• Validate the hypothesis
• Productize

10. Hypothesize – test - iterate - cnt’d
The principles of data science
• Iterate quickly – know your tools
• Don’t be afraid of statistics
• Don’t fall in love with your ideas

11. Practical Examples
When should I tweet?
The problem: Give the tweeter user an indication
when he is most likely to get a retweet.
The input: The users tweet stream, with all
features.

12. Practical Example – the workflow
• Choose features
• Split data into training and evaluation
• Model the training data
• Validate the hypothesis
• What now?

13. Practical Example – features – the simple choice
October
th,
8
2013 16:43
hour of day = 16
day of week = Tuesday

14. Practical Example – the modeling (on training)
A decision tree using the features
28.2% (259)
Hour
37.2% (145)
Day
16.7% (114)
34.1% (132)
30.8% (104)
69.2% (13)
Hour
Day
33.3% (18)
46.4% (28)
70.0% (10)

15. Practical Example – evaluation
On Training
AUC=.667
On Evaluation
AUC=.576

16. Practical Example – what now?
• More features – #,NLP on tweets?
• More data
• Validate across users, cluster users?
Etc. etc.

17. LivePerson’s Ecosystem
developer.liveperson.com
apps.liveperson.com

18. LivePerson’s Ecosystem
YouTube.com/LivePersonDev
Twitter.com/LivePersonDev
Facebook.com/LivePersonDev

19. Thank You
And we’re hiring!