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Introduction to Management Science
Businesses have to cut costs, increase revenue and be profitable. The aim of this book is to introduce Management Science to analyse business challenges and to find solutions analytically. Important topics in modelling, optimisation and probability are covered. These include: linear and integer programming, network flows and transportation; essential statistics, queueing systems and inventory models. The overall objectives are: to enable the reader to increase the efficiency and productivity of businesses; to observe and define challenges in a concise, precise and logical manner; to be familiar with a number of classical and state-of-the art operational research techniques and tools; to devise solutions, algorithms and methods that offer competitive advantage to businesses and organisations; and to provide results to management for decision making and implementation. Numerous examples and problems with solutions are given to demonstrate how these concepts can be applied in a business context.
Data Analytics for Business
We are drowning in data but are starved for knowledge. Data Analytics is the discipline of extracting actionable insights by structuring, processing, analysing and visualising data using methods and software tools. Hence, we gain knowledge by understanding the data. A roadmap to achieve this is encapsulated in the knowledge discovery in databases (KDD) process. Databases help us to store data in a structured way. The Structure Query Language (SQL) allows us to gain first insights about business opportunities. Visualising the data using Business Intelligence tools and Data Science languages deepens our understanding of the key performance indicators and business characteristics. This can be u...
Data Analytics for Business
We are drowning in data but are starved for knowledge. Data Analytics is the discipline of extracting actionable insights by structuring, processing, analysing and visualising data using methods and software tools. Hence, we gain knowledge by understanding the data. A roadmap to achieve this is encapsulated in the knowledge discovery in databases (KDD) process. Databases help us store data in a structured way. The structure query language (SQL) allows us to gain first insights about business opportunities. Visualising the data using business intelligence tools and data science languages deepens our understanding of the key performance indicators and business characteristics. This can be used...
Cybersecurity for Decision Makers
This book is aimed at managerial decision makers, practitioners in any field, and the academic community. The chapter authors have integrated theory with evidence-based practice to go beyond merely explaining cybersecurity topics. To accomplish this, the editors drew upon the combined cognitive intelligence of 46 scholars from 11 countries to present the state of the art in cybersecurity. Managers and leaders at all levels in organizations around the globe will find the explanations and suggestions useful for understanding cybersecurity risks as well as formulating strategies to mitigate future problems. Employees will find the examples and caveats both interesting as well as practical for e...
Ethical Marketing Through Data Governance Standards and Effective Technology
Marketing on digital platforms requires critical thinking on data management systems, machine learning methods, and attributes like customer trust, societal ethics, and managing consumer feedback with the utmost utilization of technology in different ways. The pursuit for a unified source of information is fundamental for marketers in digital marketing. Ethical Marketing Through Data Governance Standards and Effective Technology delves into the intricacies of achieving this unity by addressing the challenges and presenting solutions in a structured manner. The book explores the fundamental necessity for an effective data governance strategy. It emphasizes the eradication of silos and the est...
Artificial Intelligence: A Systems Approach
This book offers students and AI programmers a new perspective on the study of artificial intelligence concepts. The essential topics and theory of AI are presented, but it also includes practical information on data input & reduction as well as data output (i.e., algorithm usage). Because traditional AI concepts such as pattern recognition, numerical optimization and data mining are now simply types of algorithms, a different approach is needed. This “sensor / algorithm / effecter” approach grounds the algorithms with an environment, helps students and AI practitioners to better understand them, and subsequently, how to apply them. The book has numerous up to date applications in game programming, intelligent agents, neural networks, artificial immune systems, and more. A CD-ROM with simulations, code, and figures accompanies the book.
