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Data Science for Business and Decision Making

than existing books by applying data to decision making. Not only is the book useful for undergraduates, but it can also help business owners;

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Business Analytics, Volume I

decision making approach that uses statistical and quantitative analysis along with data mining, management science, and fact-based data to measure;

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Decision Support, Analytics, and Business Intelligence

data and how is it useful? What is business intelligence? How can predictive analytics support decision making? What is the impact of decision;

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Evidence-Based Decision-Making

Evidence-Based Decision-Making: How to Leverage Available Data and Avoid Cognitive Biases examines how a wide range of factual evidence;

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Data Analytics in the Era of the Industrial Internet of Things

engine for decision making in the IIoT enterprise, is provided. It then discusses how data and machine learning (ML) play an important role;

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Data Analytics in the Era of the Industrial Internet of Things

engine for decision making in the IIoT enterprise, is provided. It then discusses how data and machine learning (ML) play an important role;

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Data Analytics for Organisational Development - Unleashing the Potential of Your Data

businesses, making it an increasingly necessary skill for every manager and for all functions across an organisation. Data Analytics for;

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Analytics, Data Science, & Artificial Intelligence

For courses in decision support systems, computerised decision-making tools, and management support systems. Market-leading guide to;

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Big Data Analytics

. The fourth part of the book examines analytics for marketing decision-making and the roles of big data analytics for market intelligence and;

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Big Data Analytics

. The fourth part of the book examines analytics for marketing decision-making and the roles of big data analytics for market intelligence and;

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Analytics and Data Science

in decision making, i.e., business analytics. The chapters in Analytics and Data Science: Advances in Research and Pedagogy are written by leading;

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Data Science for Business and Decision Making

Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on;

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Big Data Analytics Using Multiple Criteria Decision-Making Models

era of Big Data and Business Analytics. In this volume, the focus will be on modelling-based tools for Business Analytics (BA), with exclusive;

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Big Data Analytics Using Multiple Criteria Decision-Making Models

era of Big Data and Business Analytics. In this volume, the focus will be on modelling-based tools for Business Analytics (BA), with exclusive;

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Recent Trends in Decision Science and Management

techniques like machine learning, which explore decision-making for cross-platforms that contain heterogeneous data associated with complex assets;

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Business Analytics for Decision Making

, understanding, and decision making. Business analytics is about using data and models to solve various kinds of decision problems. There are three;

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Statistics, Data Analysis And Decision Modeling

applications are emphasized throughout the book for business decision-making; a comprehensive database is developed, with marketing, financial, and;

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Decision-Making in Management

analysis (MCDA), multidimensional comparative analysis (MCA), decision games and data acquisition techniques for decision making (declarative;

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Data Science and Multiple Criteria Decision Making Approaches in Finance

This book considers and assesses essential financial issues by utilizing data science and fuzzy multiple criteria decision making (MCDM;

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Data Science and Multiple Criteria Decision Making Approaches in Finance

This book considers and assesses essential financial issues by utilizing data science and fuzzy multiple criteria decision making (MCDM;

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Decision-Making Management

Decision-Making Management: A Tutorial and Applications provides practical guidance for researchers seeking to optimizing business;

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Applications of Management Science

of management science methodologies, data envelopment analysis and multi-criteria decision making. The first section is focused on data envelopment;

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Modeling Decisions for Artificial Intelligence

sections: aggregation operators and decision making; data science and data mining; and data privacy and security.;

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Smart Data

decision making and prediction services support for business, science, and engineering. It also examines the latest research trends in fields;

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Research and Evaluation for Business

of the text is on decision making based on data analysis. This is a real data, and problem--oriented text that teaches the actual practice;

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Breakthroughs in Decision Science and Risk Analysis

innovations in the field of decision analysis (DA), Breakthroughs in Decision Science and Risk Analysis presents theories and methods for making;

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Machine Learning for Intelligent Decision Science

The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for;

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