Business Analytics The Science Of Data Driven Decision Making By U Dinesh Kumar
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Business Analytics
Written with the aim of becoming the primary resource for students of business analytics, this book provides a holistic perspective of analytics with theoretical foundations and applications of the theory using examples across several industries.
Building Business Models with Machine Learning
Organizations worldwide grapple with the complexities of incorporating machine learning into their business models while ensuring sustainability. Decision-makers, data scientists, and business executives face the challenge of navigating this terrain to drive innovation and maintain a competitive edge. Building Business Models with Machine Learning provides a comprehensive solution, offering practical insights and strategies for integrating machine learning into organizational plans. By bridging the gap between theory and practice, we empower readers to leverage machine learning effectively, enabling them to develop resilient and flexible business models. The book serves as a vital resource for those seeking to understand the nuances of sustainable management in a volatile, uncertain, complex, and ambiguous (VUCA) world. It addresses key challenges such as irrational decision-making and the need for adaptive systems in modern business environments. Through a combination of theoretical frameworks and empirical research findings, our book equips readers with the knowledge and tools needed to navigate these challenges successfully. Whether you are a seasoned professional, a postgraduate MBA program, or a managerial sciences student, this book offers invaluable insights that will significantly enhance your understanding and application of machine learning in business models.
Natural Language Analytics with Generative Large-Language Models
Author: Francisco S. Marcondes
language: en
Publisher: Springer Nature
Release Date: 2025-02-13
This book explores the application of generative Large Language Models (LLMs) for extracting and analyzing data from natural language artefacts. Unlike traditional uses of LLMs, such as translation and summarization, this book focuses on utilizing these models to convert unstructured text into data that can be processed through the data science pipeline to generate actionable insights. The content is designed for professionals in diverse fields including cognitive science, linguistics, management, and information systems. It combines insights from both industry and academia to provide a comprehensive understanding of how LLMs can be effectively used for natural language analytics (NLA). The book details practical methodologies for implementing LLMs locally using open-source tools, ensuring data privacy and feasibility without the need for expensive infrastructure. Key topics include interpretant, mindset and cultural analysis, emphasizing the use of LLMs to derive soft data—qualitative information crucial for nuanced decision-making. The text also outlines the technical aspects of LLMs, including their architecture, token embeddings, and the differences between encoder-based and decoder-based models. By providing a case study and practical examples, the authors show how LLMs can be used to meet various analytical needs, making this book a valuable resource for anyone looking to integrate advanced natural language processing techniques into their data analysis workflows.