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Delen, D: Analytics, Data Science, & Artificial Intelligence de Efraim Turban

Delen, D: Analytics, Data Science, & Artificial Intelligence

Autor Efraim Turban
en Limba Engleză Paperback – 4 mai 2020

Considerăm adesea că deciziile de business bazate pe intuiție sau pe experiența empirică sunt suficiente într-o eră a vitezei, însă Analytics, Data Science, & Artificial Intelligence demonstrează că, fără un cadru tehnologic riguros, succesul rămâne o variabilă aleatorie. Această a 11-a ediție, semnată de Efraim Turban, nu este doar o actualizare de conținut, ci o reorganizare radicală a modului în care înțelegem sistemele de suport al deciziilor. Descoperim aici o tranziție clară de la simpla prelucrare a datelor către inteligența artificială aplicată, robotica industrială și Internet of Things (IoT).

Ca și Hamed Fazlollahtabar în Decision-Making and Analytics, autorul distilează experiență reală în principii acționabile, oferind studenților și profesioniștilor o hartă precisă a peisajului analitic actual. Putem afirma că forța acestui volum rezidă în structura sa progresivă: pornește de la natura datelor și vizualizare (Partea I), trece prin procesele de data mining și machine learning (Partea II) și culminează cu strategii de optimizare și big data (Partea III). Această progresie permite cititorului să înțeleagă nu doar „ce” reprezintă o tehnologie, ci și „cum” se integrează ea într-un ecosistem decizional complex. Față de lucrările sale anterioare, precum Social Commerce sau Introduction to Electronic Commerce and Social Commerce, unde accentul era pus pe platformele de tranzacționare, aici Efraim Turban se concentrează pe motorul invizibil al oricărei afaceri moderne: capacitatea de a transforma datele brute în acțiuni prescriptive prin Deep Learning și Cognitive Computing.

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Specificații

ISBN-13: 9781292341552
ISBN-10: 1292341556
Pagini: 832
Dimensiuni: 207 x 255 x 29 mm
Greutate: 1.4 kg
Ediția:11. Auflage
Editura: Pearson Education

De ce să citești această carte

Această carte se adresează managerilor și studenților care doresc să stăpânească arhitectura decizională modernă. Veți câștiga o înțelegere profundă a modului în care AI, chatbot-urile și IoT pot fi integrate în fluxurile de lucru pentru a elimina erorile umane. Este un instrument esențial pentru oricine vrea să treacă de la analiza retrospectivă a datelor la modelarea predictivă și optimizarea proceselor în timp real.

Despre autor

Efraim Turban (M.B.A., Ph.D., University of California, Berkeley) este un cercetător de renume mondial, în prezent afiliat la Pacific Institute for Information System Management, University of Hawaii. Cu o carieră academică impresionantă ce include poziții la City University of Hong Kong și University of Southern California, Dr. Turban este un pionier în domeniul sistemelor informatice. A publicat peste 100 de articole în jurnale de prestigiu precum „Management Science” și „MIS Quarterly”. Expertiza sa vastă în comerț electronic și sisteme de suport decizional fundamentează rigoarea științifică a acestui volum de referință.

Descriere scurtă

This title is a Pearson Global Edition. The Editorial team at Pearson has worked closely with educators around the world to include content which is especially relevant to students outside the United States.
For courses in decision support systems, computerized decision-making tools, and management support systems. Market-leading guide to modern analytics, for better business decisions Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support is the most comprehensive introduction to technologies collectively called analytics (or business analytics) and the fundamental methods, techniques, and software used to design and develop these systems. Students gain inspiration from examples of organizations that have employed analytics to make decisions, while leveraging the resources of a companion website. With six new chapters, the 11th edition marks a major reorganization reflecting a new focus ? analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT. Give students anytime, anywhere access with Pearson eText Pearson eText is a simple-to-use, mobile-optimized, personalized reading experience available within MyLab. It lets students highlight, take notes, and review key vocabulary all in one place ? even when offline.
Pearson eText can also be adopted on its own as the main course material. Learn more about Pearson eText or contact your Pearson rep for purchase options.

Cuprins

  • PART I: INTRODUCTION TO ANALYTICS AND AI
  • 1. An Overview of Business Analytics, Decision Support Systems, Business Intelligence, Data Science, and Artificial Intelligence
  • 2. Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications
  • 3. Nature of Data, Statistical Modeling, and Visualization
  • PART II: PREDICTIVE ANALYTICS AND MACHINE LEARNING
  • 4. Data Mining Process, Methods, and Applications
  • 5. Machine learning Techniques for Predictive Analytics
  • 6. Deep Learning and Cognitive Computing
  • 7. Text Mining, Sentiment Analysis, and Social Analytics
  • PART III: PRESCRIPTIVE ANALYTICS AND BIG DATA
  • 8. Prescriptive Analytics with Optimization and Simulation
  • 9. Big Data, Location Analytics, and Cloud Computing
  • PART IV: ROBOTICS, SOCIAL NETWORKS, AI, AND IoT
  • 10. Robotics: Industrial and Consumer Applications
  • 11. Group Decision Making, Collaborative Systems, and AI Support
  • 12. Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants, and Robo Advisors
  • 13. The Internet of Things As a Platform for Intelligent Applications
  • PART V: CAVEATS OF ANALYTICS AND AI
  • 14. Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts

Notă biografică

Ramesh Sharda (MBA, PhD, University of Wisconsin-Madison) is Vice Dean for Research and Graduate Programs, Watson/ConocoPhillips Chair, and Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University. His research has been published in major journals in management science and information systems, including Management Science, Operations Research, Information Systems Research, Decision Support Systems, Decision Sciences Journal, EJIS, JMIS, Interfaces, INFORMS Journal on Computing, and ACM Database. Dr. Sharda is a member of the editorial boards of journals such as Decision Support Systems, Decision Sciences, and ACM Database. He has worked on many sponsored research projects with government and industry, and has been a consultant to many organizations. He also serves as the faculty director of Teradata University Network. Dr. Sharda received the 2013 INFORMS Computing Society HG Lifetime Service Award, and was inducted into the Oklahoma Higher Education Hall of Fame in 2016. He is a fellow of INFORMS. Dursun Delen (PhD, Oklahoma State University) is the Spears and Patterson Chair in Business Analytics, Director of Research for the Center for Health Systems Innovation, and Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University. Prior to his academic career, he worked for a privately owned research and consultancy company, Knowledge Based Systems, Inc. in College Station, Texas, as a research scientist for five years, during which time he led a number of decision support and other information systems-related research projects funded by federal agencies such as DoD, NASA, NIST, and DOE. Dr. Delen's research has appeared in major journals, including Decision Sciences, Decision Support Systems, Communications of the ACM, Computers and Operations Research, Computers in Industry, Journal of Production Operations Management, Journal of American Medical Informatics Association, Artificial Intelligence in Medicine, and Expert Systems with Applications. He has published eight books and textbooks and more than 100 peer-reviewed journal articles, and is often invited to deliver keynote addresses at national and international conferences on topics related to business analytics, Big Data, data/text mining, business intelligence, decision support systems, and knowledge management. Dr. Delen served as the general co-chair for the 4th International Conference on Network Computing and Advanced Information Management in Seoul, South Korea, and regularly serves as chair on tracks and mini-tracks at various business analytics and information systems conferences. He is the co-editor-in-chief of the Journal of Business Analytics, the area editor for Big Data and Business Analytics on the Journal of Business Research, and chief editor, senior editor, associate editor, and editorial board member on more than a dozen other journals. His consultancy, research, and teaching interests are in business analytics, data and text mining, health analytics, decision support systems, knowledge management, systems analysis and design, and enterprise modeling. Efraim Turban (MBA, PhD, University of California, Berkeley) is a visiting scholar at the Pacific Institute for Information System Management, University of Hawaii. Prior to this, he was on the staff of several universities, including City University of Hong Kong; Lehigh University; Florida International University; California State University, Long Beach; Eastern Illinois University; and the University of Southern Cali