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PHPUK Data Web Data Science Data Mining BIG BIG BIG BIG
Agenda ,[object Object]
BIG DATA SCIENCE
BIG DATA MINING
SUMMARY
com
Web2.0 Moores Law Economics Bandwidth SOCIAL open/share
 
Data web Web 2.0 + mobile Cloud Computing Data Science
In practice ,[object Object]
Always working
Every webpage personalized
DataWeb Summary ,[object Object]
Economic free cloud
Personalization real time
Science applied to society
Data Science ,[object Object]
Data lifecycle
Case studies
What is data science? ,[object Object]
Engineering
Mathematics Statistics ML
Communication ,[object Object]
Data lifecycle ,[object Object],[object Object],[object Object],[object Object],[object Object]
Case Studies ,[object Object]
Cloudera
Bitly
LinkedIN
e-commerce
Cloudera ,[object Object]
http://jeffhammerbacher.com/
Video
http://www.cloudera.com/?resource=orbitz-ideas-jeff-hammerbacher-evolving-new-analytical-platform-apache-hadoop
Enterprise side – Dataspaces
Bitly ,[object Object]

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Big dataweb, science, mining

Editor's Notes

  1. Welcome thank for invite, background, assumed read profile First talk, as an entrepreneur through n in at the deepend always good, make sure you learn to swim fast.
  2. 10min set the science 10min what is data science and review of characters in the industry, what saying whats being leartn, OPEN source 20 hands on code. 10 min Q&A
  3. Started with a dot, physists tells you a big bang! Data story began. .com commercial, transaction focus, e-commerce automation, mechanical Burst – thinking continueium – web2.0
  4. Send it to friends, family, share openess story build application Infractucture – econonmics Pace of data – bandwidth FB Zuckerberg open share ration growing faster than more law. What happens as we cycle through this and speeds up – DATA web web2.0 squared web3.0 . . . .
  5. Open and share accellerate (privacy debate – wont go there) How is could difference from moore law, that plus more – hadoop, more to go in the cloud, don t want per hour, want what I need, NOSQL, data portability etc. Data science- what does it all mean?
  6. Re cap and make conclusions
  7. Live state of physics 1800 Chairman Google Community rallying around Data Science, strataconf. Structure, local meetups How does data live? Characters in the industry, I ve been reading about, useful to link to post get started.
  8. Add three hats graphics yellow hard hat, prof hat and marketing hat! Dave mccure!
  9. Data Flow Clean keep up to date include new? (big problem? If data with answer is not included, doesn 't matter how smart you DM is !) Algorithm – magic Present -communicate, API portable, feedback loop, etc
  10. Range of business, infrastructure hadoop cloudera, business linkedin, amazon e-commerce, health everything LL me Link into data mining,
  11. Infrastructure stack
  12. Cross source view of world
  13. Amazon and ebay talk tomorrow keynote
  14. Yahoo meetup James Sarwoski Wisdom of the Crowd book, prediction markets, choice bet with money better, what if replace bet with money with bet with your life? Need to measure life? Set hypthosis – test. Need curiosity to apply ideas Smart on our own – smarter networked? Only live life in real time Lots of 'path' already worn
  15. Next push of the web? Start up to existing need skill set, education market adopting to skill up work place Picture of a cat, = curiosity
  16. Picuture small med large show different level of granularity of data What hypothsisi are you trying to ask? Lets go and see what each is usfeul for?
  17. Show live site stats Need to get screen shot
  18. Got chrome or FF Code open files Story show class of data lifecycle, clean, make wise, UI API RDF Example, choices made, two words limit 50 FREQUENCY PLAYING GOT image assumption try and crowd source everything, getting start, re start once started Use Couch DB to show top50 May change two words or limit to 100? Trade off with speed We know what the answer will look like? Just getting there. Not always awere choice made, frequency of matching, weights attached 'Rule' be consistent Could be better but is quantums better than what we have Learn by doing ie learn be accident! 'play god slide'
  19. Dave winer not so much data for and against, to be use to make what we need.
  20. Speak on conf. On future of language, our job to pursudate in data science ie this direction