Thinking Big презентация
Содержание
- 2. About Me Shawn Hermans Data Engineer/Scientist Technology consultant Physics, math, data
- 3. About this Talk Non-technical introduction to Big Data Not focused on
- 4. Should you believe the hype? Should you believe the hype?
- 5. Big Data Promises No need for scientific method Predict disease
- 7. Big Data Criticism Garbage in, Garbage out Ignores the role
- 8. Big Data is just another way to think about data Big
- 9. Mental Models “A mental model is simply a representation of an
- 10. Examples Occam's razor Mind maps Law of supply and demand Never
- 11. All models are wrong, but some are useful All models are
- 12. Relational Resistance Resistance to big data concepts, technologies, and techniques because
- 14. Data Mental Models Relational Linked Object Oriented Geospatial Temporal
- 15. What is Big Data? What is Big Data?
- 16. According to Gartner “Big data is high volume, high velocity, and/or
- 17. According to Me Big data is the Bazaar to traditional data’s
- 18. Cathedral and Bazaar Traditional Data Clean Top down Carefully collected Scales
- 19. Big Data Differences Relational Normalization ACID SQL/Query Structured/Schema
- 20. Integrating all available data is the promise of Big Data
- 21. Why should you care? Why should you care?
- 23. Information as an Asset Target specific customer's needs rather than broad
- 24. Big Data and You What information do you have, that no
- 25. Big Data Technology Big Data Technology
- 26. Big Data Platforms Cloud AWS Google Microsoft
- 27. Big Data Stack Batch Processing Data Collection SQL/Query Search Machine Learning
- 29. What about data science? What about data science?
- 30. What IS Data Science? Data science is statistics on a Mac
- 32. The need for Data Science There is a LOT of data
- 33. Big Data has its limits Big Data has its limits
- 34. Black Swans and Big Data There are fundamental limits to prediction
- 35. What’s next? What’s next?
- 36. Getting Started Business Identify some unresolved questions Figure out what data
- 37. My Info Twitter: @shawnhermans Github: github.com/shawnhermans Blog: http://shawnhermans.github.io/ (In Progress)
- 38. Backup Slides Backup Slides
- 40. The Fourth Quadrant and the Failure of Statistics The Fourth Quadrant
- 41. Soothsayer Simple HTTP/JSON API for training/classifying data Lots of built
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