This book explores emerging research and pedagogy in analytics and data science that have become core to many businesses as they work to derive value from data. The chapters examine the role of analytics and data science to create, spread, develop and utilize analytics applications for practice. Selected chapters provide a good balance between discussing research advances and pedagogical tools in key topic areas in analytics and data science in a systematic manner. This book also focuses on several business applications of these emerging technologies in decision making, i.e., business analytics. The chapters in Analytics and Data Science: Advances in Research and Pedagogy are written by leading academics and practitioners that participated at the Business Analytics Congress 2015.
Applications of analytics and data science technologies in various domains are still evolving. For instance, the explosive growth in big data and social media analytics requires examination of the impact of these technologies and applications on business and society. As organizations in various sectors formulate their IT strategies and investments, it is imperative to understand how various analytics and data science approaches contribute to the improvements in organizational information processing and decision making. Recent advances in computational capacities coupled by improvements in areas such as data warehousing, big data, analytics, semantics, predictive and descriptive analytics, visualization, and real-time analytics have particularly strong implications on the growth of analytics and data science.
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 PerspectiveRudolph 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 MediaJuheng 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
