Applied Quantitative Methods in Technology Foresight
A Graduate Guide to Advanced Analytics, Machine Learning Applications, and AI-Enhanced Approaches
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Beschreibung
This book provides a comprehensive guide to applying advanced quantitative methods and artificial intelligence in technology foresight, bridging traditional statistical approaches with emerging AI-enabled techniques. It offers graduate students and researchers a structured pathway to understand, implement, and integrate modern analytical tools for analyzing and forecasting technological developments.
The book responds to the growing need for sophisticated methods in an era of rapid technological change. It progresses from fundamental statistical concepts to advanced machine learning applications, ensuring a strong foundation while introducing state-of-the-art techniques.
Key features include coverage of bibliometric analysis, patent analytics, and technology mining; integration of machine learning and deep learning approaches; practical implementation using Python and R; and real-world case studies.
Designed primarily for students in technology management, innovation studies, and business analytics, it also serves as a reference for researchers and practitioners. Basic knowledge of statistics and programming is recommended.
This book provides a comprehensive guide to applying advanced quantitative methods and artificial intelligence in technology foresight, bridging traditional statistical approaches with emerging AI-enabled techniques. It offers graduate students and researchers a structured pathway to understand, implement, and integrate modern analytical tools for analyzing and forecasting technological developments.
The book responds to the growing need for sophisticated methods in an era of rapid technological change. It progresses from fundamental statistical concepts to advanced machine learning applications, ensuring a strong foundation while introducing state-of-the-art techniques.
Key features include coverage of bibliometric analysis, patent analytics, and technology mining; integration of machine learning and deep learning approaches; practical implementation using Python and R; and real-world case studies.
Designed primarily for students in technology management, innovation studies, and business analytics, it also serves as a reference for researchers and practitioners. Basic knowledge of statistics and programming is recommended.
Delivers structured progression from foundational statistics to new AI applications in foresight studies Presents a unique hybrid framework combining bibliometrics, patent analytics, and machine learning approaches Includes learning materials such as code repositories, datasets, video tutorials, solution guides, and more
Autor*in
Serhat Burmaoglu
Themen in »Applied Quantitative Methods in Technology Foresight«
Machine learning for patent analytics Deep learning for technology prediction Advanced patent analysis methods Technology emergence detection techniques Quantitative innovation analysis methods AI-enabled technology assessment Innovation analytics and foresight Quantitative technology foresight methods Technology foresight data analysis techniques Technology mining with Python Data-driven technology foresight Statistical methods for technology planning Emerging technology detection methods AI in technology forecasting Bibliometric analysis in technology assessment