Computing predictive analytics, business intelligence, and economics : modeling techniques with startups and incubators / edited by Cyrus F. Nourani, PhD.
Material type:
TextSeries: Innovation management and computingPublisher: Oakville, ON, Canada ; Palm Bay, Florida, USA : Apple Academic Press, 2019Description: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 0429465467; 9780429465468; 9780429878718; 0429878710; 9780429878701; 0429878702; 9780429878695; 0429878699Subject(s): Management -- Statistical methods | Business intelligence | New business enterprises | COMPUTERS / Database Management / Data Mining | SCIENCE / GeneralDDC classification: 658.1/1011 LOC classification: HD30.215Online resources: Taylor & Francis | OCLC metadata license agreement Summary: "This volume, Computing Predictive Analytics, Business Intelligence, and Economics: Modeling Techniques with Startups and Incubators, brings together research and system designs that address the scientific basis and the practical systems design issues that support areas ranging from intelligent business interfaces and predictive analytics to economics modeling. Applications for management science and IT have been of interest areas for business schools and computing experts during recent years. Among the areas that are being treated are modern analytics, heterogeneous computing, business intelligence, ERP (enterprise resource planning), and decision science. Consumers have been pledging their love for data visualizations for a while now, and data is the area being explored, such as B2B and EC (E-commerce), E-business and the Intelligent Web, CRM (customer relationship management), infrastructures, and more. The digitization implications of these many new applications are described and explored in this informative volume. The chapter authors address diverse issues in conjunction with computing predictive analytics, business intelligence, and economics, including university startup incubators, innovation in business, high-tech startups, strategic leadership, developing management systems, sustainable manufacturing and services, strategic decision trees, identifying business competencies, and more. The volume will prove informative to business managers, economists, technology leaders, start-ups, business school faculty and students, IT specialists, high-tech entrepreneurial groups, and others."-- Provided by publisher.
"This volume, Computing Predictive Analytics, Business Intelligence, and Economics: Modeling Techniques with Startups and Incubators, brings together research and system designs that address the scientific basis and the practical systems design issues that support areas ranging from intelligent business interfaces and predictive analytics to economics modeling. Applications for management science and IT have been of interest areas for business schools and computing experts during recent years. Among the areas that are being treated are modern analytics, heterogeneous computing, business intelligence, ERP (enterprise resource planning), and decision science. Consumers have been pledging their love for data visualizations for a while now, and data is the area being explored, such as B2B and EC (E-commerce), E-business and the Intelligent Web, CRM (customer relationship management), infrastructures, and more. The digitization implications of these many new applications are described and explored in this informative volume. The chapter authors address diverse issues in conjunction with computing predictive analytics, business intelligence, and economics, including university startup incubators, innovation in business, high-tech startups, strategic leadership, developing management systems, sustainable manufacturing and services, strategic decision trees, identifying business competencies, and more. The volume will prove informative to business managers, economists, technology leaders, start-ups, business school faculty and students, IT specialists, high-tech entrepreneurial groups, and others."-- Provided by publisher.
OCLC-licensed vendor bibliographic record.