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Causal Analytics for Applied Risk Analysis
Causal analytics methods can revolutionize the use of data to make effective decisions by revealing how different choices affect probabilities of various outcomes. This book presents and illustrates models, algorithms, principles, and software for deriving causal models from data and for using them to optimize decisions with uncertain outcomes. It discusses how to describe and summarize situations; detect changes; evaluate effects of policies or interventions; learn what works best under different conditions; predict values of as-yet unobserved quantities from available data; and identify the most likely explanations for observed outcomes, including surprises and anomalies. The book resents practical techniques for causal modeling and analytics that practitioners can apply to improve understanding of how choices affect probabilities of consequences and, based on this understanding, to recommend choices that are more likely to accomplish their intended objectives.The book begins with a survey of modern analytics methods, focusing mainly on techniques useful for decision, risk, and policy analysis. Chapter 2 introduces free in-browser software, including the Causal Analytics Toolkit (CAT) software, to enable readers to perform the analyses described and to apply modern analytics methods easily to their own data sets. Chapters 3 through 11 show how to apply causal analytics and risk analytics to practical risk analysis challenges, mainly related to public and occupational health risks from pathogens in food or from pollutants in air. Chapters 12 through 15 turn to broader questions of how to improve risk management decision-making by individuals, groups, organizations, institutions, and multi-generation societies with different cultures and norms for cooperation. These chapters examine organizational learning, community resilience, societal risk management, and intergenerational collaboration and justice in managing risks.
R 11,007.31
E-Enabled Operations Management
Although the theory of operations management has been presented in many textbooks published in the last two decades, the subject of e-enabled operations management is rather short of easily accessible literature. The approach to operations management described in this book is unusual with respect to what is found in standard textbooks. Information and Communication Technologies (ICT) impact the ways firms are organised and managed, and as a consequence change the practical means used to conduct business operations. The features of this book are threefold. System approach to business modelling: Business activities, controlling functions and associated information systems are described within a coherent analytical system framework allowing a clear understanding of the various current control and costing concepts. Operations costing is not usually included in textbooks as part of operations management, but it should be. Cost targeting has become an integral part of good practice of business management. Validity of models: Apparently simple models are analyzed in depth. Students must be fully aware of the assumptions made when models are formulated and of their conditions of validity. Applying a model implies automatically that assumptions of a sort are taken for granted. Logistics, procurement and quality management: These three business functions are critical key success factors for managing e-enabled supply chains from suppliers to customers. That is why their main tools are introduced in this document.
R 5,795.00
Competing on Analytics: Updated, with a New Introduction
The New Edition of a Business ClassicThis landmark work, the first to introduce business leaders to analytics, reveals how analytics are rewriting the rules of competition.Updated with fresh content, Competing on Analytics provides the road map for becoming an analytical competitor, showing readers how to create new strategies for their organizations based on sophisticated analytics. Introducing a five-stage model of analytical competition, Davenport and Harris describe the typical behaviors, capabilities, and challenges of each stage. They explain how to assess your company’s capabilities and guide it toward the highest level of competition. With equal emphasis on two key resources, human and technological, this book reveals how even the most highly analytical companies can up their game.With an emphasis on predictive, prescriptive, and autonomous analytics for marketing, supply chain, finance, M&A, operations, R&D, and HR, the book contains numerous new examples from different industries and business functions, such as Disney’s vacation experience, Google’s HR, UPS’s logistics, the Chicago Cubs’ training methods, and Firewire Surfboards’ customization. Additional new topics and research include:Data scientists and what they doBig data and the changes it has wroughtHadoop and other open-source software for managing and analyzing dataData products—new products and services based on data and analyticsMachine learning and other AI technologiesThe Internet of Things and its implicationsNew computing architectures, including cloud computingEmbedding analytics within operational systemsVisual analyticsThe business classic that turned a generation of leaders into analytical competitors, Competing on Analytics is the definitive guide for transforming your company’s fortunes in the age of analytics and big data.
R 798.00
Data Envelopment Analysis
Using the neo-classical theory of production economics as the analytical framework, this book, first published in 2004, provides a unified and easily comprehensible, yet fairly rigorous, exposition of the core literature on data envelopment analysis (DEA) for readers based in different disciplines. The various DEA models are developed as nonparametric alternatives to the econometric models. Apart from the standard fare consisting of the basic input- and output-oriented DEA models formulated by Charnes, Cooper, and Rhodes, and Banker, Charnes, and Cooper, the book covers developments such as the directional distance function, free disposal hull (FDH) analysis, non-radial measures of efficiency, multiplier bounds, mergers and break-up of firms, and measurement of productivity change through the Malmquist total factor productivity index. The chapter on efficiency measurement using market prices provides the critical link between DEA and the neo-classical theory of a competitive firm. The book also covers several forms of stochastic DEA in detail.
R 2,635.87
Operations Management
See - Understand - Discuss - PracticeOperations Management makes it easy to:· identify the relevance of operations in the real-world; · understand the theory underlying the subject; · discuss and think critically about operations; · consolidate learning through practice. Aware that students taking their first module in Operations Management often have little first-hand experience of a working environment, the authors introduce all the core topics to students in a lively and engaging manner, making OM relevant and meaningful. Over 80 cases spanning local businesses to global companies showcase real-life operations and challenge students to think about the issues they may encounter in their future career. Cases include:, Microsoft, HP, Dominos, ING Bank, EasyJet,Ticketmaster, Apple, Boeing, IKEA, NHS, Marriott, BP, and Sytner.Research insights point students in the direction of seminal and recent research in the field to further their reading, while learning outcomes and chapter summaries help to consolidate understanding and structure revision. The text is also augmented by extensive online resources such as animated diagrams, practice activities, video interviews, and quizzes. Relevant materials are signposted from each chapter, providing a truly holistic approach to the subject. Additional online resources include:For students:Animated diagrams from the book, with audio narration to help explain the concepts being depicted.Curated library of links to footage of 'Operations in Action'.Web-based activities.Multiple choice questions.Links to seminal paper.Flashcard glossary.For lecturers:Bespoke video case material consisting of interviews and processes tied to each chapter. Packaged as 5 minute clips, these can either be shown in relation to a chapter topic, or as a whole film to demonstrate how one company utilises many aspects of OM. Customizable PowerPoint slides.Tutor guide.Tutorial activities.Answers to discussion questions.Test bank.
R 3,178.00
Engineering Data Analytics
Engineering Data Analytics introduces students to foundational concepts within the discipline, the centrality of models in analysis methodologies, and the significance of probability in dealing with uncertain quantities. The textbook provides engineering students with the skillsets necessary to evaluate complex systems—whether physical or operational—when closed form or simulation approaches are unavailable or inadequate, as is often the case.The book sheds light on the complex tapestry of engineering data analytics, covering statistical quality control to experimental design strategies. It offers a practical approach by including Python code for implementing various analytical models, illustrating the intersection of theoretical understanding with practical application. Key topics such as probability mass functions, cumulative distribution functions, and the interpretation of ANOVA using the concept of sample variance are given due attention to ensure a comprehensive coverage of the subject matter.Engineering Data Analytics is designed to support coursework at the undergraduate level and is suitable for students who are pursuing degrees in engineering disciplines that necessitate a solid grasp of data analytics principles. It can also serve as a fundamental resource for graduate-level studies, where a more profound dive into the mechanisms and advanced applications of engineering data analytics is required.
R 3,607.63
Anti-Analytics
Meet Matt! Matt is the Mathematical and Analytical Thinker as well as the narrator of Anti-Analytics: How Not to and How to Use Data Analytics in Business. Matt explains how managers can successfully apply data analytics to business problems.Matt shares insight into developing a business analytics mindset that fosters the conditions under which organizations can develop business intelligence, create value, and build sustainable competitive advantage. The book introduces the concept of Anti-Analytics, which are the practices and processes preventing the proper adoption and adaptation of analytics-based approaches. The book explains how to:Enhance the sustainability of data analytics projects to achieve higher levels of strategic intelligenceDetermine the best of the mix of managers and technology to create organizational valueReduce errors in the analysis and design of data analytics systemsAnti-analytics and the business analytics mind set can also help organizations manage the uncertainty surrounding AI’s application and integration into broader organizational and societal systems. Because generative AI models and basic AI solutions are widely accessible, organizations can easily adopt this technology. The real advantage from AI as well as data analytics comes from users with an analytical mindset who can manage and operate organizations in ways that solve increasingly complex problems. This book shows how to develop this analytical mindset that can create business solutions delivering value and competitive advantage now and in the future.
R 8,572.00
Heterogeneous Data Management, Polystores, and Analytics for Healthcare
This book constitutes the refereed post-conference proceedings of International Workshops: Polystores and other Systems for Heterogeneous Data, Poly 2018, and Data Management and Analytics for Medicine and Healthcare, DMAH 2018, in Rio de Janeiro, Brazil, in August 2018, held in conjunction with the 44th International Conference on Very Large Data Bases, VLDB 2018. The 11 regular papers presented together with 2 invited papers and 1 abstract of a keynote talk were carefully reviewed and selected from 16 initial submissions. The Poly 2018 Workshop focus on growing a larger and more diverse research agenda around data system solutions for heterogeneous data. The DMAH 2018 Workshop aims to foster exchange of information and discussions on innovative data management and analytics technologies.
R 1,538.31
Operations Management for Business Excellence
In a context of heightened global disruption, accelerating digital transformation, and increasing sustainability imperatives, the fifth edition of Operations Management for Business Excellence, provides a contemporary and analytically grounded framework for understanding how operations and supply chain management contribute to long-term organisational performance.The text integrates foundational principles of operations and supply chain management with recent developments in automation, artificial intelligence, robotics, connected systems, and real-time analytics. It examines how these technologies are reshaping operational decision-making, blurring traditional boundaries between planning and execution, and raising new questions concerning governance, ethics, and the role of human judgement in technology-enabled systems. Each chapter combines conceptual clarity with applied examples, linking operations strategy to practical challenges such as warehouse design in the era of Industry 5.0, omnichannel logistics, returns management, and supply network resilience. A consistent five-pillar strategic framework – adaptability, customer-driven innovation, continuous competitive recalibration, proactive risk management, and workforce empowerment – provides a structured lens through which advanced supply chain concepts are analysed.Written for undergraduate and postgraduate students, executives, and practitioners, the book balances analytical rigour with accessibility. It encourages critical evaluation of how organisations can design effective, resilient, and responsible operations, positioning operations management as a discipline that integrates people, technology, and sustainability to create enduring value.
R 8,572.00
Procurement Analytics
This unique textbook explicitly addresses the intersection of advanced analytics and procurement. It is motivated by one core question: How can firms generate (economic) value from procurement data? It demonstrates that procurement is one of the major functions within a firm where data analytics, artificial intelligence, and operations research can successfully be leveraged to reduce cost and risk and to achieve resilience and sustainability goals.The book provides a methods-based overview of data-driven optimization of purchasing decisions. Besides presenting key concepts and applications, it particularly focuses on implementation, so as to help (future) procurement managers and data scientists quickly evaluate the value generated by a given data-driven solution. What sets this textbook apart is its combination of rigorous, state-of-the-art methodologies from academic research and first-hand experience from various application-oriented consulting projects in a range of industries. Though primarily intended for graduate students with a major in procurement and supply chain management, the book will also benefit purchasing managers with and without specific knowledge of advanced analytics techniques, and data scientists with and without specific experience in procurement.
R 5,634.34
Operational Risk Modelling and Management
Taking into account the standards of the Basel Accord, Operational Risk Modelling and Management presents a simulation model for generating the loss distribution of operational risk. It also examines a multitude of management issues that must be considered when adjusting the quantitative results of a comprehensive model.The book emphasizes techniques that can be understood and applied by practitioners. In the quantitative portions of the text, the author supplies key concepts and definitions without stating theorems or delving into mathematical proofs. He also offers references for readers looking for further background information. In addition, the book includes a Monte Carlo simulation of risk capital in the form of a run-through example of risk calculations based on data from a quantitative impact study. Since the computations are too complicated for a scripting language, a prototypical software program can be downloaded from www.garrulus.comHelping you navigate the tricky world of risk calculation and management, this book presents two main building blocks for determining how much capital needs to be reserved for operational risk. It employs the loss distribution approach as a model for calculating the risk capital figure and explains risk mitigation through management and management’s actuations.
R 4,478.99
Modelling and Simulation in Management
This book is a comprehensive introduction of the reader into the simulation and modelling techniques and their application in the management of organisations. The book is rooted in the thorough understanding of systems theory applied to organisations and focuses on how this theory can apply to econometric models used in the management of organisations. The econometric models in this book employ linear and dynamic programming, graph theory, queuing theory, game theory, etc. and are presented and analysed in various fields of application, such as investment management, stock management, strategic decision making, management of production costs and the lifecycle costs of quality and non-quality products, production quality Management, etc.
R 6,602.79
R 1,741.00
Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease
Data analytics is proving to be an ally for epidemiologists as they join forces with data scientists to address the scale of crises. Analytics examined from many sources can derive insights and be used to study and fight global outbreaks. Pandemic analytics is a modern way to combat a problem as old as humanity itself: the proliferation of disease. Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease explores different types of data and discusses how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more by applying cutting edge technology such as machine learning and data analytics in the wake of the COVID-19 pandemic. Covering a range of topics such as mental health analytics during COVID-19, data analysis and machine learning using Python, and statistical model development and deployment, it is ideal for researchers, academicians, data scientists, technologists, data analysts, diagnosticians, healthcare professionals, computer scientists, and students.
R 17,477.68
Data-Driven Insights and Analytics for Measurable Sustainable Development Goals
Data-Driven Insights and Analytics for Measurable Sustainable Development Goals discusses the growing imperative to understand, measure, and guide actions using data-driven insights. The SDGs encompass a broad spectrum of global challenges, from eradicating poverty and hunger to preserving the environment and fostering peace. To address these issues, one should be able to measure and analyze progress. This book bridges the gap between qualitative and quantitative assessments, recognizing that goals are not solely about numbers but also encompass complex social, environmental, and economic dynamics. By merging data science with qualitative analysis, readers can explore how SDGs intersect and influence each other.The book provides readers with an understanding of how to effectively leverage data science models and algorithms using descriptive analytics, allowing us to assess the current state of SDG performance and offering valuable insights into where we stand on these critical goals. Prescriptive analytics guides actions by offering actionable recommendations, while predictive analytics anticipates future trends and challenges, helping us navigate our path toward the SDGs effectively.
R 5,297.00
Digital Transformation in Industrial and Logistics Systems
This book provides decision-makers, researchers, and practitioners with actionable insights to create intelligent, resilient, and human-centered industrial and logistics systems in complex and uncertain environments. By bridging advanced digital technologies with operational and organizational challenges, it provides concrete frameworks, models, and case studies applicable to real industrial contexts.The originality of this book lies in its integrated perspective, combining Industry 4.0 and Industry 5.0 principles with artificial intelligence, data analytics, quality management, sustainability, and ethical considerations. Instead of addressing these dimensions separately, the book highlights how their synergy enhances performance, resilience, and responsible value creation, particularly in automotive and aeronautical supply chains.Covering a wide range of topics from AI-driven decision support and forecasting to digital maturity, standards, circular economy practices, and energy transition, this book covers both strategic and operational levels. It is intended for academics, Ph.D. students, engineers, supply chain managers, and policy-makers seeking evidence-based approaches, methodological guidance, and practical tools to support digital transformation initiatives and future-oriented industrial and logistics systems.
R 9,440.50
Engineering and Management of Data Science, Analytics, and AI/ML Projects
This book presents a dual perspective on modern research and praxis on Data Science, Analytics, and AI/Machine Learning (DSA-AI/ML) system with small or big data. Consequently, potential readers—academics, researchers and practitioners interested in the systematic development and implementation of DSA-AI/ML systems—can be benefited with the high-quality conceptual and empirical research chapters focused on:Foundations, Development Platforms, and Tools on Engineering and Management of DSA-AI/ML Projects:DSA-AI/ML reference architectures.Data visualization principles for DSA-AI/ML.Federated Learning in large-scale DSA-AI/ML systems.Achievements, Challenges, Trends, and Future Research Directions on DSA-AI/ML Projects:Large multimodal model-based simulation game for DSA-AI/ML systems.Value stream analysis and design applied to DSA-AI/ML systems.Quality management 4.0 and AI for DSA-AI/ML systems.Hence, this research-oriented co-edited book contributes to achieve the systematic development and implementation of Data Science, Analytics, and AI/ML systems.
R 8,018.47
Advances in Production Management Systems. The Path to Digital Transformation and Innovation of Production Management Systems
The two-volume set IFIP AICT 591 and 592 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2020, held in Novi Sad, Serbia, in August/September 2020.The 164 papers presented were carefully reviewed and selected from 199 submissions. They discuss globally pressing issues in smart manufacturing, operations management, supply chain management, and Industry 4.0. The papers are organized in the following topical sections:Part I: advanced modelling, simulation and data analytics in production and supply networks; advanced, digital and smart manufacturing; digital and virtual quality management systems; cloud-manufacturing; cyber-physical production systems and digital twins; IIOT interoperability; supply chain planning and optimization; digital and smart supply chain management; intelligent logistics networks management; artificial intelligence and blockchain technologies in logistics and DSN; novel production planning and control approaches; machine learning and artificial intelligence; connected, smart factories of the future; manufacturing systems engineering: agile, flexible, reconfigurable; digital assistance systems: augmented reality and virtual reality; circular products design and engineering; circular, green, sustainable manufacturing; environmental and social lifecycle assessments; socio-cultural aspects in production systems; data-driven manufacturing and services operations management; product-service systems in DSN; and collaborative design and engineeringPart II: the Operator 4.0: new physical and cognitive evolutionary paths; digital transformation approaches in production management; digital transformation for more sustainable supply chains; data-driven applications in smart manufacturing and logistics systems; data-driven services: characteristics, trends and applications; the future of lean thinking and practice; digital lean manufacturing and its emerging practices; new reconfigurable, flexible or agile production systems in the era of industry 4.0; operations management in engineer-to-order manufacturing; production management in food supply chains; gastronomic service system design; product and asset life cycle management in the circular economy; and production ramp-up strategies for product
R 3,105.77
Analytics and Knowledge Management
The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations.The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics.Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms.
R 6,118.00
Transforming Healthcare with Big Data
Healthcare organizations are utilizing big data for improved data analytics and performance measurements. This book provides a comprehensive framework integrating 41 components, emphasizing data completeness for better healthcare outcomes. It provides guidelines for selecting appropriate technology and implementing solutions.Transforming Healthcare with Big Data: A Practical Guide for Big Data in Healthcare Systems presents a six-layer framework for healthcare systems, integrating organization, people, process, data, technology, and outcomes. It discusses big data analytics, predictive risk assessments, and the benefits of combining it with artificial intelligence, machine learning, and deep learning technologies. It also guides planning big data analytics solutions, tracking KPIs, and optimizing healthcare operations for cost reduction without compromising care quality.Healthcare executives, administrators, IT professionals, data scientists, analysts, clinical and operations managers, consultants, researchers, academics, policymakers, and graduate students in health informatics and industrial engineering can all benefit from this book by gaining a comprehensive and practical framework for effectively implementing big data analytics in healthcare. This will enable improved patient outcomes, cost efficiency, and operational excellence.
R 3,600.58
Network Science Models for Data Analytics Automation
This book explains network science and its applications in data analytics for critical infrastructures, engineered systems, and knowledge acquisition. Each chapter describes step-by-step processes of how network science enables and automates data analytics through examples. The book not only dissects modeling techniques and analytical results but also explores the intrinsic development of these models and analyses. This unique approach bridges the gap between theory and practice and channels’ managerial and problem-solving skills.Engineers, researchers, and managers would benefit from the extensive theoretical background and practical examples discussed in this book. Advanced undergraduate students and graduate students in mathematics, statistics, engineering, business, public health, and social science may use this book as a one-semester textbook or a reference book. Readers who are more interested in applications may skip Chapter 1 and peruse through the rest of the book with ease.
R 3,823.82
Anti-Analytics
Meet Matt! Matt is the Mathematical and Analytical Thinker as well as the narrator of Anti-Analytics: How Not to and How to Use Data Analytics in Business. Matt explains how managers can successfully apply data analytics to business problems.Matt shares insight into developing a business analytics mindset that fosters the conditions under which organizations can develop business intelligence, create value, and build sustainable competitive advantage. The book introduces the concept of Anti-Analytics, which are the practices and processes preventing the proper adoption and adaptation of analytics-based approaches. The book explains how to:Enhance the sustainability of data analytics projects to achieve higher levels of strategic intelligenceDetermine the best of the mix of managers and technology to create organizational valueReduce errors in the analysis and design of data analytics systemsAnti-analytics and the business analytics mind set can also help organizations manage the uncertainty surrounding AI’s application and integration into broader organizational and societal systems. Because generative AI models and basic AI solutions are widely accessible, organizations can easily adopt this technology. The real advantage from AI as well as data analytics comes from users with an analytical mindset who can manage and operate organizations in ways that solve increasingly complex problems. This book shows how to develop this analytical mindset that can create business solutions delivering value and competitive advantage now and in the future.
R 3,114.00
Essential Analytics for Hospital Managers
This book provides practical applications of statistical and mathematical concepts to resolve common issues in hospital management. Each chapter discusses a key component of hospital operations, such as maximizing hospital profitability through pricing optimization, forecasting facility requirements from historical data, and determining optimal patient schedules to fully utilize hospital resources in order to eliminate over-crowding in the emergency department. Structured by the degree of mathematical complexity, this professional book utilizes problem-solving methodologies ranging from basic statistical concepts (means and standard deviations) to more advanced statistics (Poisson distributions and queuing theory). Concluding with computer applications and simulations, the practical examples will help hospital managers to optimally and innovatively make use of linear programming. The book’s main goal is to make hospital personnel more aware of the benefits of management science methodologies that are not usually employed in today’s hospitals.
R 2,064.14
Operations Research: Introduction To Models And Methods
This attractive textbook with its easy-to-follow presentation provides a down-to-earth introduction to operations research for students in a wide range of fields such as engineering, business analytics, mathematics and statistics, computer science, and econometrics. It is the result of many years of teaching and collective feedback from students.The book covers the basic models in both deterministic and stochastic operations research and is a springboard to more specialized texts, either practical or theoretical. The emphasis is on useful models and interpreting the solutions in the context of concrete applications.The text is divided into several parts. The first three chapters deal exclusively with deterministic models, including linear programming with sensitivity analysis, integer programming and heuristics, and network analysis. The next three chapters primarily cover basic stochastic models and techniques, including decision trees, dynamic programming, optimal stopping, production planning, and inventory control. The final five chapters contain more advanced material, such as discrete-time and continuous-time Markov chains, Markov decision processes, queueing models, and discrete-event simulation.Each chapter contains numerous exercises, and a large selection of exercises includes solutions.
R 5,812.91
Graph-Powered Analytics and Machine Learning with TigerGraph
With the rapid rise of graph databases, organizations are now implementing advanced analytics and machine learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database models available.You'll explore a three-stage approach to deriving value from connected data: connect, analyze, and learn. Victor Lee, Xinyu Chan, and Gaurav Deshpande from TigerGraph present real use cases covering several contemporary business needs. By diving into hands-on exercises using TigerGraph Cloud, you'll quickly become proficient at designing and managing advanced analytics and machine learning solutions for your organization.Use graph thinking to connect, analyze, and learn from data for advanced analytics and machine learningLearn how graph analytics and machine learning can deliver key business insights and outcomesUse five core categories of graph algorithms to drive advanced analytics and machine learningDeliver a real-time 360-degree view of core business entities, including customer, product, service, supplier, and citizenDiscover insights from connected data through machine learning and advanced analytics
R 1,638.00
Decision Analytics Applications in Industry
This book presents a range of qualitative and quantitative analyses in areas such as cybersecurity, sustainability, multivariate analysis, customer satisfaction, parametric programming, software reliability growth modeling, and blockchain technology, to name but a few. It also highlights integrated methods and practices in the areas of machine learning and genetic algorithms. After discussing applications in supply chains and logistics, cloud computing, six sigma, production management, big data analysis, satellite imaging, game theory, biometric systems, quality, and system performance, the book examines the latest developments and breakthroughs in the field of science and technology, and provides novel problem-solving methods.The themes discussed in the book link contributions by researchers and practitioners from different branches of engineering and management, and hailing from around the globe. These contributions provide scholars with a platform to derive maximum utility in the area of analytics by subscribing to the idea of managing business through system sciences, operations, and management. Managers and decision-makers can learn a great deal from the respective chapters, which will help them devise their own business strategies and find real-world solutions to complex industrial problems.
R 5,918.72
Mobility Patterns, Big Data and Transport Analytics
Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling, Second Edition provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing, and controlling mobility patterns—a key aspect of transportation modeling. The book features prominent international experts who provide overviews on new analytical frameworks, applications, and concepts in mobility analysis and transportation systems. Fields covered are evolving rapidly, and this new edition updates existing material and provides new chapters that reflect recent developments in the field (such as the emergence of active, transfer and reinforcement learning).Users will find a detailed, mobility ‘structural’ analysis and a look at the extensive behavioral characteristics of transport, observability requirements, limitations for realistic transportation applications, and transportation systems analysis that are related to complex processes and phenomena. This book bridges the gap between big data, data science, and transportation systems analysis with a study of big data’s impact on mobility and an introduction to the tools necessary to apply new techniques.
R 3,678.00
Maximizing Management Performance and Quality with Service Analytics
Service analytics studies the collection of business analytics models and tools for the improvement of IT service management processes. By analyzing related quality, cost, and productivity metrics, as well as customer interactions and social factors, organizations can effectively exploit these resources to reveal valuable insights in support of business goals, maximizing performance, quality of service, and customer satisfaction.Maximizing Management Performance and Quality with Service Analytics offers a selection of service analytics solutions for process modeling and optimization proven to drive excellence in IT service management. This book is for practitioners engaged in IT service management who are interested in delivering high-quality and cost-competitive IT services, as well as academic and industrial researchers in the fields of information technology and computer science who are advancing data analysis, modeling, and optimization methods to new emerging fields.
R 9,890.97
Operations Management in Healthcare
This thoroughly revised and updated second edition of Operations Management in Healthcare: Strategy and Practice describes how healthcare organizations can cultivate a competitive lead by developing superior operations using a strategic perspective. In clearly demonstrating the "how-tos" of effectively managing a healthcare organization, this new edition also addresses the "why" of providing quality and value-based care. Comprehensive and practice-oriented, chapters illustrate how to excel in the four competitive priorities - quality, cost, delivery, and flexibility - in order to build a cumulative model of healthcare operations in which all concepts and tools fit together. This textbook encourages a hands-on approach and integrates mind maps to connect concepts, icons for quick reference, dashboards for measurement and tracking of progress, and newly updated end-of-chapter problems and assignments to reinforce creative and critical thinking.Written with the diverse learning needs in mind for programs in health administration, public health, business administration, public administration, and nursing, the textbook equips students with essential high-level problem-solving and process improvement skills. The book reveals concepts and tools through a series of short vignettes of a fictitious healthcare organization as it embarks on its journey to becoming a highly reliable organization. This second edition also includes a strong emphasis on the patient's perspective as well as expanded and added coverage of Lean Six Sigma, value-based payment models, vertical integration, mergers and acquisitions, artificial intelligence, population health, and more to reflect evolving innovations in the healthcare environment across the United States. Complete with a full and updated suite of Instructor Resources, including Instructor’s Manual, PowerPoints, and test bank in addition to data sets, tutorial videos, and Excel templates for students.Key Features:Demonstrates the "how-tos" of effectively managing a healthcare organizationSharpens problem-solving and process improvement skills through use of an extensive toolkit developed throughout the textPrepares students for Lean Six Sigma certification with expanded coverage of concepts, tools, and analyticsHighlights new trends in healthcare management with coverage of value-based payments, mergers and acquisitions, population health, telehealth, and moreIntertwines concepts with vivid vignettes to describe human dynamics, organizational challenges, and applications of toolsEmploys boxed features and YouTube videos to address frequently asked questions and real-world instances of operations in practicePurchase includes digital access for use on most mobile devices or computers, as well as full suite of instructor and student resources
R 4,267.00
Operations Management
Operations management has an important role in improving healthcare. Some of its core concepts and tools, such as Lean and statistical process control, have their own Elements in this series. In this Element, the authors offer an overview of three major topics in healthcare operations management: capacity and demand, focus, and people and process. They demonstrate how queuing theory reveals counterintuitive insights about capacity utilisation and waiting times, examine how strategic focus can achieve significant productivity gains while creating potential inequities, and explore why process improvements must account for human behaviours like multitasking and workarounds. Using practical examples, the authors illustrate both the critical role and the limitations of operations management against a backdrop of high demand and resource constraints. This title is also available as open access on Cambridge Core.
R 853.44
Analytics based Human Resource Management
This book examines HR Analytics from the perspective of a Human Resource practitioner. The focus is primarily on how analytics can enhance processes and strengthen the HR function, and to empower readers with the tools to utilize them in practice.The book helps develop an insightful perspective on HR Analytics and what it can deliver. The application areas covered include HR Scorecards, HR Cost Analysis with respect to benefit and impact, Workforce segmentation, analysis of appraisal data, conducting surveys and analyzing HR survey data, improving Training and Development and Hiring processes using relevant metrics and hypothesis testing, designing forecasting and predictive models for attrition and performance, designing tests for selection, root cause analysis of HR problems and putting to practice frameworks like LAMP and HCM 21.This book would be useful to students of Human Resource Management. It would also serve as a handy resource for practicing human resource professionals – executives and managers and data science professionals working in the HR domain.
R 2,235.00
Policy Analytics, Modelling, and Informatics
This book provides a comprehensive approach to the study of policy analytics, modelling and informatics. It includes theories and concepts for understanding tools and techniques used by governments seeking to improve decision making through the use of technology, data, modelling, and other analytics, and provides relevant case studies and practical recommendations. Governments around the world face policy issues that require strategies and solutions using new technologies, new access to data and new analytical tools and techniques such as computer simulation, geographic information systems, and social network analysis for the successful implementation of public policy and government programs. Chapters include cases, concepts, methodologies, theories, experiences, and practical recommendations on data analytics and modelling for public policy and practice, and addresses a diversity of data tools, applied to different policy stages in several contexts, and levels and branches of government. This book will be of interest of researchers, students, and practitioners in e-government, public policy, public administration, policy analytics and policy informatics.
R 6,228.00
Data Analytics in the Era of the Industrial Internet of Things
This book presents the characteristics and benefits industrial organizations can reap from the Industrial Internet of Things (IIoT). These characteristics and benefits include enhanced competitiveness, increased proactive decision-making, improved creativity and innovation, augmented job creation, heightened agility to respond to continuously changing challenges, and intensified data-driven decision making. In a straightforward fashion, the book also helps readers understand complex concepts that are core to IIoT enterprises, such as Big Data, analytic architecture platforms, machine learning (ML) and data science algorithms, and the power of visualization to enrich the domains experts’ decision making. The book also guides the reader on how to think about ways to define new business paradigms that the IIoT facilitates, as well how to increase the probability of success in managing analytic projects that are the core engine of decision-making in the IIoT enterprise. The book starts by defining an IIoT enterprise and the framework used to efficiently operate. A description of the concepts of industrial analytics, which is a major engine for decision making in the IIoT enterprise, is provided. It then discusses how data and machine learning (ML) play an important role in increasing the competitiveness of industrial enterprises that operate using the IIoT technology and business concepts. Real world examples of data driven IIoT enterprises and various business models are presented and a discussion on how the use of ML and data science help address complex decision-making problems and generate new job opportunities. The book presents in an easy-to-understand manner how ML algorithms work and operate on data generated in the IIoT enterprise. Useful for any industry professional interested in advanced industrial software applications, including business managers and professionals interested in how data analytics can help industries and to develop innovative business solutions, as well as data and computer scientists who wish to bridge the analytics and computer science fields with the industrial world, and project managers interested in managing advanced analytic projects.
R 3,508.78
Data Analytics in Project Management
This book aims to help the reader better understand the importance of data analysis in project management. Moreover, it provides guidance by showing tools, methods, techniques and lessons learned on how to better utilize the data gathered from the projects. First and foremost, insight into the bridge between data analytics and project management aids practitioners looking for ways to maximize the practical value of data procured. The book equips organizations with the know-how necessary to adapt to a changing workplace dynamic through key lessons learned from past ventures. The book’s integrated approach to investigating both fields enhances the value of research findings.
R 2,118.70
Scalable Data Analytics with Azure Data Explorer
Write efficient and powerful KQL queries to query and visualize your data and implement best practices to improve KQL execution performanceKey FeaturesApply Azure Data Explorer best practices to manage your data at scale and reduce KQL execution timeDiscover how to query and visualize your data using the powerful KQLManage cluster performance and monthly costs by understanding how to size your ADX cluster correctlyBook DescriptionAzure Data Explorer (ADX) enables developers and data scientists to make data-driven business decisions. This book will help you rapidly explore and query your data at scale and secure your ADX clusters.The book begins by introducing you to ADX, its architecture, core features, and benefits. You'll learn how to securely deploy ADX instances and navigate through the ADX Web UI, cover data ingestion, and discover how to query and visualize your data using the powerful Kusto Query Language (KQL). Next, you'll get to grips with KQL operators and functions to efficiently query and explore your data, as well as perform time series analysis and search for anomalies and trends in your data. As you progress through the chapters, you'll explore advanced ADX topics, including deploying your ADX instances using Infrastructure as Code (IaC). The book also shows you how to manage your cluster performance and monthly ADX costs by handling cluster scaling and data retention periods. Finally, you'll understand how to secure your ADX environment by restricting access with best practices for improving your KQL query performance.By the end of this Azure book, you'll be able to securely deploy your own ADX instance, ingest data from multiple sources, rapidly query your data, and produce reports with KQL and Power BI.What you will learnBecome well-versed with the core features of the Azure Data Explorer architectureDiscover how ADX can help manage your data at scale on AzureGet to grips with deploying your ADX environment and ingesting and analyzing your dataExplore KQL and learn how to query your dataQuery and visualize your data using the ADX UI and Power BIIngest structured and unstructured data types from an array of sourcesUnderstand how to deploy, scale, secure, and manage ADXWho this book is forThis book is for data analysts, data engineers, and data scientists who are responsible for analyzing and querying their team's large volumes of data on Azure. SRE and DevOps engineers who deploy, maintain, and secure infrastructure will also find this book useful. Prior knowledge of Azure and basic data querying will help you to get the most out of this book.
R 1,889.00
Data Analytics Applied to the Mining Industry
Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book: Explains how to implement advanced data analytics through case studies and examples in mining engineering Provides approaches and methods to improve data-driven decision making Explains a concise overview of the state of the art for Mining Executives and Managers Highlights and describes critical opportunity areas for mining optimization Brings experience and learning in digital transformation from adjacent sectors
R 11,133.27
Elements of Joint Learning and Optimization in Operations Management
This book examines recent developments in Operations Management, and focuses on four major application areas: dynamic pricing, assortment optimization, supply chain and inventory management, and healthcare operations. Data-driven optimization in which real-time input of data is being used to simultaneously learn the (true) underlying model of a system and optimize its performance, is becoming increasingly important in the last few years, especially with the rise of Big Data.
R 4,762.00
R 2,412.00
Data Analytics
If you are a manager who receives the results of any data analyst’s work to help with your decision-making, this book is for you. Anyone playing a role in the field of analytics can benefit from this book as well. In the two decades the editors of this book spent teaching and consulting in the field of analytics, they noticed a critical shortcoming in the communication abilities of many analytics professionals. Specifically, analysts have difficulty in articulating in business terms what their analyses showed and what actionable recommendations were made. When analysts made presentations, they tended to lapse into the technicalities of mathematical procedures, rather than focusing on the strategic and tactical impact and meaning of their work. As analytics has become more mainstream and widespread in organizations, this problem has grown more acute.Data Analytics: Effective Methods for Presenting Results tackles this issue. The editors have used their experience as presenters and audience members who have become lost during presentation. Over the years, they experimented with different ways of presenting analytics work to make a more compelling case to top managers. They have discovered tried and true methods for improving presentations, which they share. The book also presents insights from other analysts and managers who share their own experiences. It is truly a collection of experiences and insight from academics and professionals involved with analytics.The book is not a primer on how to draw the most beautiful charts and graphs or about how to perform any specific kind of analysis. Rather, it shares the experiences of professionals in various industries about how they present their analytics results effectively. They tell their stories on how to win over audiences. The book spans multiple functional areas within a business, and in some cases, it discusses how to adapt presentations to the needs of audiences at different levels of management.
R 4,439.30
Data Analytics and Management in Data Intensive Domains
This book constitutes the post-conference proceedings of the 21st International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2019, held in Kazan, Russia, in October 2019.The 11 revised full papers presented together with four invited papers were carefully reviewed and selected from 52 submissions. The papers are organized in the following topical sections: advanced data analysis methods; data infrastructures and integrated information systems; models, ontologies and applications; data analysis in astronomy; information extraction from text; distributed computing; data science for education.
R 2,167.86
R 1,317.21
Management Decision-Making, Artificial Intelligence and Analytics
Both accessible and concise, this text examines data analytics, big data and AI from a managerial and organizational perspective and looks at how they can help managers become more effective decision-makers. The second edition of this book has been fully revised and updated, including more content on AI throughout and more case studies from industry. Chapters look at emerging technologies and the ethical, security, privacy and legal aspects of data-driven decision-making. Key updates include content on: - How will AI impact management - AI and automation- Emerging technologies including GenAI - How AI may augment, change and replace human decision-making.- Ethical AI decision-making and responsible AISuitable for management students studying business analytics and decision-making or business and artificial intelligence at undergraduate, postgraduate and MBA levels.
R 2,024.00
Data Analytics
Large data sets arriving at every increasing speeds require a new set of efficient data analysis techniques. Data analytics are becoming an essential component for every organization and technologies such as health care, financial trading, Internet of Things, Smart Cities or Cyber Physical Systems. However, these diverse application domains give rise to new research challenges. In this context, the book provides a broad picture on the concepts, techniques, applications, and open research directions in this area. In addition, it serves as a single source of reference for acquiring the knowledge on emerging Big Data Analytics technologies.
R 2,311.59
Analytics based Human Resource Management
This book examines HR Analytics from the perspective of a Human Resource practitioner. The focus is primarily on how analytics can enhance processes and strengthen the HR function, and to empower readers with the tools to utilize them in practice.The book helps develop an insightful perspective on HR Analytics and what it can deliver. The application areas covered include HR Scorecards, HR Cost Analysis with respect to benefit and impact, Workforce segmentation, analysis of appraisal data, conducting surveys and analyzing HR survey data, improving Training and Development and Hiring processes using relevant metrics and hypothesis testing, designing forecasting and predictive models for attrition and performance, designing tests for selection, root cause analysis of HR problems and putting to practice frameworks like LAMP and HCM 21.This book would be useful to students of Human Resource Management. It would also serve as a handy resource for practicing human resource professionals – executives and managers and data science professionals working in the HR domain.
R 8,572.00
