- Format
- Häftad (Paperback / softback)
- Språk
- Engelska
- Antal sidor
- 649
- Utgivningsdatum
- 2018-09-09
- Upplaga
- 1st ed. 2018
- Förlag
- Springer Verlag, Singapore
- Medarbetare
- Lu, Zeguang (red.)
- Illustratör/Fotograf
- Bibliographie
- Illustrationer
- 269 Illustrations, black and white; XXIX, 649 p. 269 illus.
- Dimensioner
- 234 x 156 x 35 mm
- Vikt
- Antal komponenter
- 1
- Komponenter
- 1 Paperback / softback
- ISBN
- 9789811322051
- 935 g
Du kanske gillar
-
Data Science
4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018, Zhengzhou, China, September 21-23, 2018, Proceedings, Part II
1399- Skickas inom 7-10 vardagar.
- Gratis frakt inom Sverige över 199 kr för privatpersoner.
Finns även somPassar bra ihop
De som köpt den här boken har ofta också köpt Java How to Program, Late Objects, Global Edition av Paul Deitel (häftad).
Köp båda 2 för 2209 krKundrecensioner
Har du läst boken? Sätt ditt betyg »Fler böcker av författarna
-
Spinal Osteotomy
Yan Wang, Oheneba Boachie-Adjei, Lawrence Lenke
-
Information Retrieval
Zhicheng Dou, Qiguang Miao, Wei Lu, Jiaxin Mao, Guang Jia
-
Basic Data Structures and Program Statements
Xingni Zhou, Qiguang Miao, Lei Feng
-
Composite Data Structures and Modularization
Xingni Zhou, Qiguang Miao, Lei Feng
Innehållsförteckning
Computational theory for data science.- Big data management and applications.- Data quality and data preparation.- Evaluation and measurement in data science.- Data visualization.- Big data mining and knowledge management.- Infrastructure for data science.- Machine learning for data science.- Data security and privacy.- Applications of data science.- Case study of data science.- Multimedia data management and analysis.- Data-driven scientific research.- Data-driven bioinformatics.- Data-driven healthcare.- Data-driven management.- Data-driven e-government.- Data-driven smart city/planet.- Data marketing and economics.- Social media and recommendation systems.- Data-driven security.- Data-driven business model innovation.- Social and/or organizational impacts of data science.