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Big Data Analytics and Applications
Содержание
- 1. Big Data Analytics and Applications
- 2. VelocityValueVolumeVariety
- 3. Big Data are not data!Technology for gathering,
- 4. Paradigm shiftSubject of labour is not a program, but hypothesis and data
- 5. Paradigm shiftMore sources – higher veracityMore data
- 6. Слайд 6
- 7. 2014
- 8. 2015
- 9. Problems in Russian Big DataNo depersonalization culture
- 10. Big Data education in Russia
- 11. Master programsHSE:Big Data SystemsData SciencesMSU:«Intellectual analysis of
- 12. Online1 week to 1 yearCoursera, edX (http://rusbase.com/list/bigdatye-kursy/) Intuit (Introduction to Big Data Analytics) http://bit.ly/IntuitBDA
- 13. Additional education1 week - 3 month -
- 14. NSU Big Data Strategy
- 15. Syllabus of Master program
- 16. Challenges1st place, 2015, AVITO1st place, 2015, eKapusta4th place among 619 teams, 2009, Data Mining Cup
- 17. Skull surface restoreNo formulaeNo negative examplesNeural networks, autoencoders
- 18. Deep learningUnsupervised
- 19. Semantic segmentationhttp://arxiv.org/pdf/1511.00561v2.pdf
- 20. Van Gogh Ivan GogovAlex J. Champandard. Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks. 2016
- 21. Paintingshttp://tinyclouds.org/colorize/
- 22. Articles for revisionhttp://karpathy.github.io/2015/05/21/rnn-effectiveness/
- 23. Pushkin A.I.Зафонствуя попруг, Ивисшивый чела, На воспопе
- 24. Thank you!http://bigdata.nsu.ru Evgeniy Pavlovskiy, head of the SDAML N*SU e@expasoft.ru, +79139117907
- 25. Скачать презентанцию
VelocityValueVolumeVariety
Слайды и текст этой презентации
Слайд 1Big Data Analytics and Applications
Pavlovskiy E.N., Ph.D.
head of the Stream
Data Analytics and Machine Learning lab NSU
Слайд 3Big Data are not data!
Technology for gathering, storage, processing, and
utilize
Method of data processing and representation
Problem of resource lack
Social phenomenon
Data
of big volume, variety, velocity, distributedBig potential value
Слайд 5Paradigm shift
More sources – higher veracity
More data – higher accuracy
More
data – lower quality requirements
High-speed algorithms: O(N) or O(NlogN)
Unmovable data
=> parallelism and map reduceStructure decline => information extraction
Слайд 9Problems in Russian Big Data
No depersonalization culture (FL-152)
No understanding of
potential value
Insufficient competence in statistics
Absence of data brokers
Highly risked data
analytics projectsLack of data
Слайд 11Master programs
HSE:
Big Data Systems
Data Sciences
MSU:
«Intellectual analysis of big data»
«Big Data:
infrastructure and solution technique»
NSU
Big Data Analytics
Computer modeling
Слайд 12Online
1 week to 1 year
Coursera, edX (http://rusbase.com/list/bigdatye-kursy/)
Intuit (Introduction to
Big Data Analytics) http://bit.ly/IntuitBDA
Слайд 13Additional education
1 week - 3 month - 2 years
Yandex Data
Analysis School – https://yandexdataschool.ru/
Digital October – http://newprolab.ru
Beeline - http://bigdata.beeline.digital/datamba
Expasoft – http://expasoft.com/edu/
Слайд 16Challenges
1st place, 2015, AVITO
1st place, 2015, eKapusta
4th place among 619
teams, 2009, Data Mining Cup
Слайд 20Van Gogh Ivan Gogov
Alex J. Champandard. Semantic Style Transfer and Turning
Two-Bit Doodles into Fine Artworks. 2016
Слайд 23Pushkin A.I.
Зафонствуя попруг,
Ивисшивый чела,
На воспопе днего,
Я могина бесслужел,
Катирей свети довой,
Из
увядебиле меня,
И на гразой шле, далодной
Вольностью примстают;
Я, водешил перцов миренья?
N.I.
Putincev, stream data analytics and machine learning lab NSU
Слайд 24Thank you!
http://bigdata.nsu.ru
Evgeniy Pavlovskiy,
head of the SDAML N*SU
e@expasoft.ru,
+79139117907
