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Analytics and Data Science = Advance...
~
Jones, Mary C.
Analytics and Data Science = Advances in Research and Pedagogy /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Analytics and Data Science/ edited by Amit V. Deokar, Ashish Gupta, Lakshmi S. Iyer, Mary C. Jones.
Reminder of title:
Advances in Research and Pedagogy /
other author:
Deokar, Amit V.
Description:
VIII, 297 p. 47 illus.online resource. :
Contained By:
Springer Nature eBook
Subject:
Management information systems. -
Online resource:
https://doi.org/10.1007/978-3-319-58097-5
ISBN:
9783319580975
Analytics and Data Science = Advances in Research and Pedagogy /
Analytics and Data Science
Advances in Research and Pedagogy /[electronic resource] :edited by Amit V. Deokar, Ashish Gupta, Lakshmi S. Iyer, Mary C. Jones. - 1st ed. 2018. - VIII, 297 p. 47 illus.online resource. - Annals of Information Systems,1934-3221. - Annals of Information Systems,18.
Chapter 1. Exploring the Analytics Frontiers through Research and Pedagogy Amit V. Deokar, Ashish Gupta, Lakshmi Iyer, and Mary C. Jones Chapter 2. Introduction: Research and Research-in-Progress Anna Sidorova, Babita Gupta, and Barbara Dinter Chapter 3. Business Intelligence Capabilities Thiagarajan Ramakrishnan, Jiban Khuntia, Terence Saldanha, and Abhishek Kathuria Chapter 4.Big Data Capabilities: An Organizational Information Processing Perspective ÖyküIsik Chapter 5. Business Analytics Capabilities and Use: A Value Chain Perspective Rudolph T. Bedeley, TorupallabGhoshal, Lakshmi S. Iyer, and JoyenduBhadury Chapter 6.Critical Value Factors in Business Intelligence Systems Implementations Paul P. Dooley, Yair Levy, Raymond A. Hackney, and James L. Parrish Chapter 7. Business Intelligence Systems Use in Chinese Organizations Yutong Song, David Arnott, and ShijiaGao Chapter 8. The Impact of Customer Reviews on Product Innovation: Empirical Evidence in Mobile Apps Zhilei Qiao, G. Alan Wang, Mi Zhou, and Weiguo Fan Chapter 9. Whispering on Social Media Juheng Zhang Chapter 10. Does Social Media Reflect Metropolitan Attractiveness? Behavioral Information from Twitter Activity in Urban Areas Johannes Bendler, Tobias Brandt, and Dirk Neumann Chapter 11. The Competitive Landscape of Mobile Communications Industry in Canada – Predictive Analytic Modeling with Google Trends and Twitter Michal Szczech and OzgurTuretken Chapter 12. Scale Development Using Twitter Data: Applying Contemporary Natural Language Processing Methods in IS Research David Agogo and Traci J. Hess Chapter 13. Information Privacy on Online Social Networks: Illusion-in-Progress in the Age of Big Data? Shwadhin Sharma and Babita Gupta Chapter 14. Online Information Processing of Scent-Related Words and Implications for Decision Making Meng-Hsien (Jenny) Lin, Samantha N.N. Cross, William J. Jones, and Terry L. Childers Chapter 15. Say It Right: IS Prototype to Enable Evidence-Based Communication Using Big Data Simon Alfano Chapter 16. Introduction: Pedagogy in Analytics and Data Science Nicholas Evangelopoulos, Joseph W. Clark, and Sule Balkan Chapter 17. Tools for Academic Business Intelligence & Analytics Teaching – Results of an Evaluation Christoph Kollwitz, Barbara Dinter, and Robert Krawatzeck Chapter 18. Neural Net Tutorial Brian R. Huguenard, and Deborah J. Ballou Chapter 19. An Examination of ERP Learning Outcomes: A Text Mining Approach Mary M. Dunaway Chapter 20. Data Science for All: A University-Wide Course in Data Literacy David Schuff.
ISBN: 9783319580975
Standard No.: 10.1007/978-3-319-58097-5doiSubjects--Topical Terms:
561123
Management information systems.
LC Class. No.: HD30.2
Dewey Class. No.: 658.4038
Analytics and Data Science = Advances in Research and Pedagogy /
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Chapter 1. Exploring the Analytics Frontiers through Research and Pedagogy Amit V. Deokar, Ashish Gupta, Lakshmi Iyer, and Mary C. Jones Chapter 2. Introduction: Research and Research-in-Progress Anna Sidorova, Babita Gupta, and Barbara Dinter Chapter 3. Business Intelligence Capabilities Thiagarajan Ramakrishnan, Jiban Khuntia, Terence Saldanha, and Abhishek Kathuria Chapter 4.Big Data Capabilities: An Organizational Information Processing Perspective ÖyküIsik Chapter 5. Business Analytics Capabilities and Use: A Value Chain Perspective Rudolph T. Bedeley, TorupallabGhoshal, Lakshmi S. Iyer, and JoyenduBhadury Chapter 6.Critical Value Factors in Business Intelligence Systems Implementations Paul P. Dooley, Yair Levy, Raymond A. Hackney, and James L. Parrish Chapter 7. Business Intelligence Systems Use in Chinese Organizations Yutong Song, David Arnott, and ShijiaGao Chapter 8. The Impact of Customer Reviews on Product Innovation: Empirical Evidence in Mobile Apps Zhilei Qiao, G. Alan Wang, Mi Zhou, and Weiguo Fan Chapter 9. Whispering on Social Media Juheng Zhang Chapter 10. Does Social Media Reflect Metropolitan Attractiveness? Behavioral Information from Twitter Activity in Urban Areas Johannes Bendler, Tobias Brandt, and Dirk Neumann Chapter 11. The Competitive Landscape of Mobile Communications Industry in Canada – Predictive Analytic Modeling with Google Trends and Twitter Michal Szczech and OzgurTuretken Chapter 12. Scale Development Using Twitter Data: Applying Contemporary Natural Language Processing Methods in IS Research David Agogo and Traci J. Hess Chapter 13. Information Privacy on Online Social Networks: Illusion-in-Progress in the Age of Big Data? Shwadhin Sharma and Babita Gupta Chapter 14. Online Information Processing of Scent-Related Words and Implications for Decision Making Meng-Hsien (Jenny) Lin, Samantha N.N. Cross, William J. Jones, and Terry L. Childers Chapter 15. Say It Right: IS Prototype to Enable Evidence-Based Communication Using Big Data Simon Alfano Chapter 16. Introduction: Pedagogy in Analytics and Data Science Nicholas Evangelopoulos, Joseph W. Clark, and Sule Balkan Chapter 17. Tools for Academic Business Intelligence & Analytics Teaching – Results of an Evaluation Christoph Kollwitz, Barbara Dinter, and Robert Krawatzeck Chapter 18. Neural Net Tutorial Brian R. Huguenard, and Deborah J. Ballou Chapter 19. An Examination of ERP Learning Outcomes: A Text Mining Approach Mary M. Dunaway Chapter 20. Data Science for All: A University-Wide Course in Data Literacy David Schuff.
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