Big dataweb, science, mining
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The document discusses big data, the data web, data science, and data mining. It provides an overview of how data is expanding rapidly, can be analyzed using data science techniques in real-time to personalize experiences, and how data mining uses various techniques like statistics, text categorization, and self-organizing maps to gain insights from data at different levels of granularity. Case studies are presented on how companies like Cloudera, Bitly, LinkedIn, and e-commerce sites apply these concepts.
Big dataweb, science, mining
- 1. PHPUK Data Web Data Science Data Mining BIG BIG BIG BIG
- 2. Agenda BIG DATA WEB
- 5. SUMMARY
- 6. com
- 7. Web2.0 Moores Law Economics Bandwidth SOCIAL open/share
- 9. Data web Web 2.0 + mobile Cloud Computing Data Science
- 10. In practice Existing data
- 11. Always working
- 13. DataWeb Summary Data - expanding fast rate
- 16. Science applied to society
- 17. Data Science What is data science?
- 18. Data lifecycle
- 19. Case studies
- 20. What is data science? Combines three areas
- 21. Engineering
- 23. Communication PP to infographic, product, API
- 24. Data lifecycle Comes from? Data conditioning Scale Tell a story Intelligence
- 25. Case Studies Range of perspectives
- 26. Cloudera
- 27. Bitly
- 28. LinkedIN
- 29. e-commerce
- 32. Video
- 34. Enterprise side – Dataspaces
- 37. Video
- 39. Links across lots of services
- 42. Video
- 44. Core part of product team
- 45. e-commerce Ebay.com keynote Saturday morning
- 46. Amazon.com - John Rauser
- 48. Heart of discovery- probability to purchase
- 49. Me OSDS Vision
- 50. Wisdom of Crowds
- 51. Big made from small
- 52. Data Science Summary Go(ing) mainstream
- 55. Data Mining Types - techniques
- 56. Examples: Statistics - Text categorisation - SOM Summary
- 57. Types - Techniques Granularity WWW Blog Post Specific Sentiment
- 58. Statistics Simple is beautiful
- 59. Real time maybe best indicator
- 60. Text Categorisation Show me the code
- 61. Data lifecycle
- 62. Assumptions
- 63. Scaling
- 67. BIG BIG made from small BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG BIG
- 68. SOM-Automous learning SOM – SELF ORGANISING MAPS
- 69. Dr. Andrew Starkey – Blue Flow Ltd
- 70. Aberdeen University Spin Out
- 72. Liverpool play in red Liverpool have a red strip Liverpool used to play in blue Liverpool in a red strip Liverpool known for their red strip Everton play in blue Everton have a blue strip Everton known as the bitter blue Everton have a horrible blue strip Everton – don’t like their blue colour
- 76. DM summary No one on its own but combination
- 77. Future – more human
- 78. Emergence Platform – Cloudspaces PtoP
- 80. PHP - dataweb 40% web CMS leading OS
- 81. 40% value from data
- 82. Evolution language - LINC .net example
- 84. Summary Data Web here
- 85. Personalised start to everything
- 86. Society science
- 88. Thank you Q & A
- 91. Contact
- 92. James Littlejohn
- 94. +44 7521580938
Editor's Notes
- #2: 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.
- #3: 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
- #4: 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
- #5: 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 . . . .
- #7: 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?
- #9: Re cap and make conclusions
- #10: 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.
- #11: Add three hats graphics yellow hard hat, prof hat and marketing hat! Dave mccure!
- #12: 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
- #13: Range of business, infrastructure hadoop cloudera, business linkedin, amazon e-commerce, health everything LL me Link into data mining,
- #14: Infrastructure stack
- #15: Cross source view of world
- #17: Amazon and ebay talk tomorrow keynote
- #18: 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
- #19: 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
- #21: 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?
- #22: Show live site stats Need to get screen shot
- #23: 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'
- #33: Dave winer not so much data for and against, to be use to make what we need.
- #34: Speak on conf. On future of language, our job to pursudate in data science ie this direction