Talk by Srinivasan Seshadri, founder of Zettata at The Hive Big Data Think Tank Meetup - Healthcare 2.0 hosted at the EMC India

1. EXPERIENCES WITH BIG DATA
SRINIVASAN SESHADRI, FOUNDER ZETTATA

2. WORLD BEFORE BIG DATA
It is a Capital Mistake to Theorize before one has Data
Sherlock Holmes

3. HOWEVER, DO NOT WANT TO BE HERE

4. AXIOMS
• Measure, Measure, Measure
• Garbage in, Garbage out
• Correlation is not Causation
• More Data Beats Cleverer Algorithms
• Algorithms that do better with more data are more interesting
• Independent Sources Of data add new signals
• Feature Engineering is the key to being a good data scientist
• How do machines and Human interplay in Big Data?
• Learn many models ‐ ensembles
• Outliers are always interesting..

5. MEASURE, MEASURE, MEASURE
• Have a Hypothesis
• Create a metric to determine if hypothesis is correct
• Build a solution that can be measured
• Iterate
If you can not measure it you can not improve it – Lord Kelvin

6. GARBAGE IN GARBAGE OUT

7. WHAT DO YOU WANT THE ANSWER TO BE?

8. CORRELATIONS

9. CORRELATION IS NOT CAUSATION
• Correlation in Data Need Not Imply Correlation in Real Life
• Can find random correlations in large amounts of data
• Correlation Does Not Imply Causation

10. CORRELATION IS NOT CAUSATION

11. CORRELATION STRIKES AGAIN!!

12. MORE DATA BEATS CLEVERER ALGORITHMS
• Adding IMDB data For Netflix prize
• Adding Protein Expression Data or Patient Data to Gene Expression Data
• Bag of Words Approach for Word Sense Disambiguation

13. WORD SENSE DISAMBIGUATION
• Bank
• Sloping Land Alongside a river or a lake. It typically has thick vegetation growing..
• A financial institution that takes deposits from some customers and gives loans to others who require the
money.
To disambiguate in typical sentences look for co‐occurrences of words with words in definition.
Unsupervised Learning. Bootstrap a model.
The pilot landed the plane on the Hudson River amongst several boats and an appreciative audience
cheered from the banks of the river.
He issued a check and took it to the bank so he could transfer money.
Can look for frequent co‐occurrences with each sense of the word (boats and check respectively) and build
a larger bag of words in which to disambiguate.

14. WORD SENSE DISAMBIGUATION

15. FEATURE ENGINEERING
Can not expect arbitrarily complex models to be learned by the computer

16. FEATURE ENGINEERING
CITYY 1 LAT. CITY 1 LNG. CITY 2 LAT. CITY 2 LNG. DRIVABLE?
123.24 46.71 121.33 47.34 Yes
123.24 56.91 121.33 55.23 Yes
123.24 46.71 121.33 55.34 No
123.24 46.71 130.99 47.34 No

17. FEATURE ENGINEERING
DISTANCE (MI.) DRIVABLE?
14 Yes
28 Yes
705 No
2432 No

18. OF HUMANS AND MACHINES
• Partnership is important
• Aha moment and the strategy comes from humans..
• Machines do the hard work of calculating fast and do not tire
• Maybe some day Machines will be able to do more than they are asked to do explicitly.. Today Explicit
Instructions are the norm..

19. ENSEMBLES ‐ OUTLIERS ARE NOT INTERESTING – FOR
CLASSIFIERS
• Learn many models from random subsets of training data
• Effect of outliers is reduced on a majority of the models
• Random Forests

20. OUTLIERS ARE ALWAYS INTERESTING FOR RANKING
PROBLEMS
• You have to be so good that they can not ignore you
• My personal thesis: Average in everything is boring. Be
outstanding in something.
• Outliers along some dimension always have interesting
information – whenever you are combining multiple
variables to come up with one global rank
• Search
• Job Interviews!

21. UNKNOWN UNKNOWNS – VERY INTERESTING TO A
BUSINESS – OUTLIERS

22. BIG DATA AND HEALTHCARE

23. ARE YOU IN THE JOB MARKET?

24. www.zettata.com
sesh@zetatta.com
Thanks!