000 | 04036nam a22005775i 4500 | ||
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001 | 978-3-030-36375-8 | ||
003 | DE-He213 | ||
005 | 20240423125225.0 | ||
007 | cr nn 008mamaa | ||
008 | 200912s2020 sz | s |||| 0|eng d | ||
020 |
_a9783030363758 _9978-3-030-36375-8 |
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024 | 7 |
_a10.1007/978-3-030-36375-8 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
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_aCOM004000 _2bisacsh |
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_aUYQ _2thema |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aKordon, Arthur K. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aApplying Data Science _h[electronic resource] : _bHow to Create Value with Artificial Intelligence / _cby Arthur K. Kordon. |
250 | _a1st ed. 2020. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2020. |
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300 |
_aXXXII, 494 p. 262 illus., 195 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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505 | 0 | _aPart I, From Business Problems to Data Science -- Data Science Based on Artificial Intelligence -- Business Problems Dependent on Data -- Artificial Intelligence-Based Data Science Solutions -- Integrate and Conquer -- The Lost-in-Translation Trap -- Part II, The AI-Based Data Science Toolbox -- The AI-Based Data Science Workflow -- Problem Knowledge Acquisition -- Data Preparation -- Data Analysis -- Model Development -- The Model Deployment Life Cycle -- Part III, AI-Based Data Science in Action -- Infrastructure -- People -- Applications of AI-Based Data Science in Manufacturing -- Applications of AI-Based Data Science in Business -- How to Operate AI-Based Data Science in a Business -- How to Become an Effective Data Scientist -- Glossary. | |
520 | _aThis book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations of key technologies: machine learning, deep learning, decision trees and random forests, evolutionary computation, swarm intelligence, and intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also shows how to introduce this technology in a business setting and guides the reader on how to build the appropriate infrastructure and develop the required skillsets. The book is ideal for data scientists who will implement the proposed methodology and techniques in their projects. It is also intended to help business leaders and entrepreneurs who want to create competitive advantage by using AI-based data science, as well as academics and students looking for an industrial view of this discipline. | ||
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aQuantitative research. | |
650 | 0 | _aData mining. | |
650 | 0 | _aBig data. | |
650 | 0 | _aComputational intelligence. | |
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aData Analysis and Big Data. |
650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
650 | 2 | 4 | _aBig Data. |
650 | 2 | 4 | _aComputational Intelligence. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030363741 |
776 | 0 | 8 |
_iPrinted edition: _z9783030363765 |
776 | 0 | 8 |
_iPrinted edition: _z9783030363772 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-36375-8 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c175604 _d175604 |