000 04036nam a22005775i 4500
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
024 7 _a10.1007/978-3-030-36375-8
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aKordon, Arthur K.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
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.
300 _aXXXII, 494 p. 262 illus., 195 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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