Search results for "Operations Management and Data Analytics Modelling"
Items we need to order for you
Introduction to Optimization with Applications in Machine Learning and Data Analytics
The primary goal of this text is a practical one. Equipping students with the enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of optimization. The initial goal of this text is to develop a marketable skill set for mathematics majors but also for students of engineering, computer science, economics, statistics, and business. Optimization reaches into many different fields.This text provides a balance where one is needed. Mathematics optimization books are often too heavy on theory without enough applications; texts aimed at business students are often strong on applications, but weak on math. The book represents an attempt at overcoming this imbalance for all students taking such a course.The book contains many practical applications but also explains the mathematics behind the techniques, including stating definitions and proving theorems. Optimization techniques are at the heart of the first spam filters, are used in self-driving cars, play a great role in machine learning, and can be used in such places as determining a batting order in a Major League Baseball game. Additionally, it has seemingly limitless other applications in business and industry.Many of the problems rely on using a computer. Microsoft’s Excel is most often used as this is common in business, but Python and other languages are considered. The consideration of other programming languages permits experienced mathematics and engineering students to use MATLAB or Mathematica or computer science students to write their own programs in Java or Python.
R 5,673.15
Handbook of Healthcare Operations Management
From the Preface:Collectively, the chapters in this book address application domains including inpatient and outpatient services, public health networks, supply chain management, and resource constrained settings in developing countries. Many of the chapters provide specific examples or case studies illustrating the applications of operations research methods across the globe, including Africa, Australia, Belgium, Canada, the United Kingdom, and the United States.Chapters 1-4 review operations research methods that are most commonly applied to health care operations management including: queuing, simulation, and mathematical programming. Chapters 5-7 address challenges related to inpatient services in hospitals such as surgery, intensive care units, and hospital wards. Chapters 8-10 cover outpatient services, the fastest growing part of many health systems, and describe operations research models for primary and specialty care services, and how to plan for patient no-shows. Chapters 12 – 16 cover topics related to the broader integration of health services in the context of public health, including optimizing the location of emergency vehicles, planning for mass vaccination events, and the coordination among different parts of a health system. Chapters 17-18 address supply chain management within hospitals, with a focus on pharmaceutical supply management, and the challenges of managing inventory for nursing units. Finally, Chapters 19-20 provide examples of important and emerging research in the realm of humanitarian logistics.
R 8,010.03
Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis
This book comprehensively covers the topic of COVID-19 and other pandemics and epidemics data analytics using computational modelling. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care. The new era of pandemics and epidemics bring tremendous opportunities and challenges due to the plentiful and easily available medical data allowing for further analysis. The aim of pandemics and epidemics research is to ensure high-quality, efficient healthcare, better treatment and quality of life by efficiently analyzing the abundant medical, and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. In the past, it was a common requirement to have domain experts for developing models for biomedical or healthcare. However, recent advances in representation learning algorithms allow us to automatically learn the pattern and representation of the given data for the development of such models. Medical Image Mining, a novel research area (due to its large amount of medical images) are increasingly generated and stored digitally. These images are mainly in the form of: computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions related to health care. Image mining in medicine can help to uncover new relationships between data and reveal new and useful information that can be helpful for scientists and biomedical practitioners. Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis will play a vital role in improving human life in response to pandemics and epidemics. The state-of-the-art approaches for data mining-based medical and health related applications will be of great value to researchers and practitioners working in biomedical, health informatics, and artificial intelligence..
R 4,405.17
System Analysis and Modelling in Air Transport
This book presents a comprehensive analysis and modelling of demand, capacity, quality of services, economics, and sustainability of the air transport system and its main components - - airports, airlines, and ATC/ATM (Air Traffic Control/Management). Airports consist of the airside and landside area characterized by their capacities for handling demand such as aircraft, air passengers, and air freight/cargo shipments. Regarding spatial configuration, airlines generally operate hub-and-spoke (conventional or legacy airlines) and point-to-point (LCCs - Low Cost Carriers) air route networks. Their fleets consisting of different aircraft types provide transport capacity for serving demand including air passengers and freight/cargo shipments. The ATC/ATM includes the controlled airspace, traffic management and control facilities and equipment on the ground, space, and on board aircraft, and the ATC Controllers). They all provide capacity to handle demand consisting of the flights between origin and destination airports carried out by airline aircraft. The outcome from the interrelationships between demand and capacity at these components materializes as the quality of services. At airports and airlines this is generally expressed by congestion and delays of aircraft, air passengers, and freight/cargo shipments. At ATC/ATM, this is expressed by delays, horizontal and vertical in-efficiency, and safety of flights. Economics of each component relate to its revenues, costs, and profits from handling demand, i.e., providing services of given quality. The sustainability of air transport system has become increasingly important issue for many internal and external actors/stakeholders involved to deal with. This has implied increasing the system’s overall social-economic effects/benefits while reducing or maintaining constant impacts/costs on the environment and society at both global and regional/local scale under conditions of continuous medium- to long term growth.
R 3,810.00
Research Methods for Operations and Supply Chain Management
Research Methods for Operations and Supply Chain Management, third edition, is a toolkit of research approaches primarily for advanced students and beginner researchers, but also a reference book for any researcher in OSCM.Many students begin their career in research limited by the one or few approaches taken by their department. The concise, accessible overviews found here equip them with an understanding of a variety of methods and how to use them, enabling students to tailor their research project to their own strengths and goals. The more seasoned researcher will find comprehensive descriptions and analyses on a wide variety of research approaches.This updated and enhanced edition responds to the latest developments in OSCM, including the growing prominence of services and production of intangible products, and the increasing use of secondary data and of mixed approaches. Alternative research approaches are included and explored to help with the planning of research. This edition also includes expanded literature reviews and analysis to guide students towards the next steps in their reading, and more detailed step-by-step advice to tie theory with the research.Including contributions from an impressive range of the field’s leading thinkers in OSCM research, this is a guide that no-one embarking on an OSCM research project should be without.
R 7,702.57
Introduction to IoT Analytics
This book covers techniques that can be used to analyze data from IoT sensors and addresses questions regarding the performance of an IoT system. It strikes a balance between practice and theory so one can learn how to apply these tools in practice with a good understanding of their inner workings. This is an introductory book for readers who have no familiarity with these techniques.The techniques presented in An Introduction to IoT Analytics come from the areas of machine learning, statistics, and operations research. Machine learning techniques are described that can be used to analyze IoT data generated from sensors for clustering, classification, and regression. The statistical techniques described can be used to carry out regression and forecasting of IoT sensor data and dimensionality reduction of data sets. Operations research is concerned with the performance of an IoT system by constructing a model of the system under study and then carrying out a what-if analysis. The book also describes simulation techniques.Key Features IoT analytics is not just machine learning but also involves other tools, such as forecasting and simulation techniques. Many diagrams and examples are given throughout the book to fully explain the material presented. Each chapter concludes with a project designed to help readers better understand the techniques described. The material in this book has been class tested over several semesters. Practice exercises are included with solutions provided online at www.routledge.com/9780367686314Harry G. Perros is a Professor of Computer Science at North Carolina State University, an Alumni Distinguished Graduate Professor, and an IEEE Fellow. He has published extensively in the area of performance modeling of computer and communication systems.
R 6,960.00
Using R for Modelling and Quantitative Methods in Fisheries
Using R for Modelling and Quantitative Methods in Fisheries has evolved and been adapted from an earlier book by the same author and provides a detailed introduction to analytical methods commonly used by fishery scientists, ecologists, and advanced students using the open-source software R as a programming tool. Some knowledge of R is assumed, as this is a book about using R, but an introduction to the development and working of functions, and how one can explore the contents of R functions and packages, is provided. The example analyses proceed step-by-step using code listed in the book and from the book’s companion R package, MQMF, available from GitHub and the standard archive, CRAN. The examples are designed to be simple to modify so the reader can quickly adapt the methods described to use with their own data. A primary aim of the book is to be a useful resource to natural resource practitioners and students.Featured Chapters: Model Parameter Estimation provides a detailed explanation of the requirements and steps involved in fitting models to data, using R and, mainly, maximum likelihood methods. On Uncertainty uses R to implement bootstrapping, likelihood profiles, asymptotic errors, and Bayesian posteriors to characterize any uncertainty in an analysis. The use of the Monte Carlo Markov Chain methodology is examined in some detail. Surplus Production Models applies all the methods examined in the earlier parts of the book to conducting a stock assessment. This included fitting alternative models to the available data, characterizing the uncertainty in different ways, and projecting the optimum models forward in time as the basis for providing useful management advice.
R 4,350.04
Data Analytics and Management in Data Intensive Domains
This book constitutes the post-conference proceedings of the 23rd International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2021, held in Moscow, Russia, in October 2021*.The 16 revised full papers were carefully reviewed and selected from 61 submissions. The papers are organized in the following topical sections: problem solving infrastructures, experiment organization, and machine learning applications; data analysis in astronomy; data analysis in material and earth sciences; information extraction from text* The conference was held virtually due to the COVID-19 pandemic.
R 3,506.13
Predictive Analytics, Data Mining and Big Data
This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.
R 1,550.49
Big Data Analytics
Successfully navigating the data-driven economy presupposes a certain understanding of the technologies and methods to gain insights from Big Data. This book aims to help data science practitioners to successfully manage the transition to Big Data. Building on familiar content from applied econometrics and business analytics, this book introduces the reader to the basic concepts of Big Data Analytics. The focus of the book is on how to productively apply econometric and machine learning techniques with large, complex data sets, as well as on all the steps involved before analysing the data (data storage, data import, data preparation). The book combines conceptual and theoretical material with the practical application of the concepts using R and SQL. The reader will thus acquire the skills to analyse large data sets, both locally and in the cloud. Various code examples and tutorials, focused on empirical economic and business research, illustrate practical techniques to handle and analyse Big Data. Key Features: - Includes many code examples in R and SQL, with R/SQL scripts freely provided online. - Extensive use of real datasets from empirical economic research and business analytics, with data files freely provided online. - Leads students and practitioners to think critically about where the bottlenecks are in practical data analysis tasks with large data sets, and how to address them. The book is a valuable resource for data science practitioners, graduate students and researchers who aim to gain insights from big data in the context of research questions in business, economics, and the social sciences.
R 2,308.00
Management Support Systeme und Business Intelligence
Die Autoren präsentieren Systeme und Konzepte, die betriebliche Entscheidungen nachhaltig unterstützen und verbessern. Die Entwicklung ist zwar seit mehr als 30 Jahren in Gang, doch jetzt gibt es einen allgemeinen Kriterienkatalog. Anhand dessen lassen sich unterschiedliche, bewährte System- und Konzeptkategorien klassifizieren und charakterisieren. Im Fokus: Aktuelle technologische Strömungen wie Data Warehouse-Architekturkonzepte, Online Analytical Processing-Lösungen und Data Mining-Systeme.
R 2,520.28
R 2,786.61
Contemporary Operations and Logistics
This edited collection collates the most up-to-date and important research within the area of operations and logistics management. Boasting the combined expertise of one of the largest logistics and operations management academic teams in Europe, it provides both depth and diversity in a balanced portfolio. The first two sections are concerned with key contemporary issues in the subject area, providing a current and up-to-date overview of the field. Section three presents a selection of important cross-cutting themes that impinge upon and inform teaching, research and practice, while the final section includes a celebration of research highlights and showcases cutting-edge applications from leaders in the field. Invaluable to students, researchers and academics alike, this book is compulsory reading for those active within operations and logistics research.
R 6,228.00
Business Analytics with Python
Your essential textbook for mastering business analytics through Python.Business Analytics with Python by Bowei Chen and Gerhard Kling is the definitive guide for upper-level undergraduate and postgraduate students studying business, management or finance. Designed to support analytics modules that prioritize practical application, this textbook introduces students to data-driven decision-making through Python, without assuming a background in computer science. It aligns with course outcomes by integrating statistical, mathematical and machine learning techniques into a unified business context. This textbook takes a holistic approach to business analytics, exploring how Python can be used to interpret and solve real-world problems. From foundational coding skills to the implementation of supervised and unsupervised machine learning methods, students learn how to translate data into insight across key business functions. Through industry-relevant case studies, including customer churn analysis, fraud detection and sales forecasting, learners build confidence in applying analytics to real organizational challenges. Pedagogical features include: - A running case study that reinforces practical learning across chapters - Clear learning objectives and chapter summaries to track progress - Step-by-step exercises and coding activities to build analytical fluency - Examples grounded in real business applications for immediate relevance Whether preparing for exams or building analytical capability for a future career, this textbook equips students with the tools to turn business data into strategic advantage.
R 2,353.68
Sustainable Operations and Supply Chain Management
This textbook explores the environmental impact of business systems, particularly emissions resulting from logistics and intra-company operations. It highlights how transportation activities between suppliers, companies, and end users contribute to emissions, while intra-logistics within companies consume significant energy to move components and subassemblies.By emphasizing efficient use of material handling equipment and machines, the book:· Provides actionable strategies to reduce energy consumption and minimize carbon emissions at power generation units.· Offers practical approaches to achieving sustainable operations in industries.· Uses Case studies reinforce key concepts, along with chapter summaries, keywords and review questions· Illustrates concepts clearly using tables and figuresAn engaging and practical resource for readers, this book would be ideal for students, researchers, and faculty in supply chain management, operations management, and industrial engineering at both graduate and research levels. It would also serve as a valuable resource for consultants and practitioners, providing insights into models and modeling systems to support better supply chain decisions.
R 2,235.00
Analytics in a Big Data World
The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments. The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic. Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analyticsOffers the results of research and the author's personal experience in banking, retail, and governmentContains an overview of the visionary ideas and current developments on the strategic use of analytics for businessCovers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.
R 1,186.00
Creating Actionable Insights Using CRM Analytics
Leverage CRM Analytics to generate valuable business insights and solve business problems efficientlyKey FeaturesExtract, combine, transform, and visualize your data to derive business insights using CRM AnalyticsGain hands-on experience as you walk through practical dashboard use cases in CRM AnalyticsLearn how to build best-in-class dashboards from a CRM Analytics thought leaderBook DescriptionCRM Analytics, formerly known as Tableau CRM and Einstein Analytics, is a powerful and versatile data analytics platform that enables organizations to extract, combine, transform, and visualize their data to create valuable business insights. Creating Actionable Insights Using CRM Analytics provides a hands-on approach to CRM Analytics implementation and associated methodologies that will have you up and running and productive in no time. The book provides you with detailed explanations of essential concepts to help you to gain confidence and become competent in using the CRM Analytics platform for data extraction, combination, transformation, visualization, and action. As you make progress, you'll understand what CRM Analytics is and where it provides business value. You'll also learn how to bring your data together in CRM Analytics, build datasets and lenses for data analysis, create effective analytics dashboards for visualization and consumption by end users, and build dashboard actions that take the user from data to insight to action with ease. By the end of this book, you'll be able to solve business problems using CRM Analytics and design, build, test, and deploy analytics dashboards efficiently.What you will learnImplement and configure CRM Analytics from scratchBuild your first CRM Analytics app and embed your CRM Analytics dashboards in Salesforce to enhance user adoptionConnect Salesforce and external data with CRM Analytics and create datasetsCreate a data recipe and get familiar with the recipe UIBuild a custom dashboard in CRM Analytics using the dashboard editorUse lenses to create a CRM Analytics dashboardConfigure and implement data security and governanceBuild configured record actions to automate data directly in SalesforceWho this book is forThis book is for data analysts, business analysts, BI professionals, and Salesforce users who want to explore CRM Analytics' capabilities and features. Basic knowledge of Salesforce and data analytics is assumed to get the most out of this book.
R 1,596.00
Data Analytics for Internal Auditors
There are many webinars and training courses on Data Analytics for Internal Auditors, but no handbook written from the practitioner’s viewpoint covering not only the need and the theory, but a practical hands-on approach to conducting Data Analytics. The spread of IT systems makes it necessary that auditors as well as management have the ability to examine high volumes of data and transactions to determine patterns and trends. The increasing need to continuously monitor and audit IT systems has created an imperative for the effective use of appropriate data mining tools. This book takes an auditor from a zero base to an ability to professionally analyze corporate data seeking anomalies.
R 2,151.77
Supervised and Unsupervised Statistical Data Analysis
The contributions in this book offer new insights into the theoretical and practical challenges of supervised and unsupervised learning, highlighting the remarkable breadth of contemporary statistical research while maintaining methodological rigor. Innovative approaches to statistical modeling, addressing spatial dependencies and circular data structures, are presented alongside significant advances in interpretable machine learning that reconcile statistical precision with algorithmic transparency. Particularly noteworthy is the volume's treatment of complex data structures, including novel methods for network analysis, high-dimensional clustering, temporal pattern recognition and optimization techniques. The volume interweaves methodological innovation and practical relevance, and the applications span diverse domains, including the social sciences and biomedical engineering, each demonstrating the effective translation of statistical theory into real-world impact. The book contains peer-reviewed contributions presented at the special edition of the 15th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, namely the International Scientific Joint Meeting of the Italian and Dutch-Flemish Classification Societies (CLADAG-VOC 2025), held in Naples, Italy, September 8-10, 2025. The conference provided fresh perspectives on the current state of research in clustering, classification and data analysis, and underpinned the value and significance of international collaboration, addressing the emerging needs of an increasingly complex data landscape and offering novel solutions to long-standing challenges in statistical data analysis.
R 8,059.00
Causal Models and Intelligent Data Management
The need to electronically store, manipulate and analyze large-scale, high-dimensional data sets requires new computational methods. This book presents new intelligent data management methods and tools, including new results from the field of inference. Leading experts also map out future directions of intelligent data analysis. This book will be a valuable reference for researchers exploring the interdisciplinary area between statistics and computer science as well as for professionals applying advanced data analysis methods in industry.
R 2,015.00
R 336.00
R 915.00
Data Analytics and Adaptive Learning
Data Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings. In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments. This set of rich, illuminating, international studies spans quantitative, qualitative, and mixed-methods research in higher education, K-12, and adult/continuing education contexts. By exploring the issues of definition and pedagogical practice that permeate teaching and learning and concluding with recommendations for the future research and practice necessary to support educators at all levels, this book will prepare researchers, developers, and graduate students of instructional technology to produce evidence for the benefits and challenges of data-driven learning.
R 2,574.36
Data Analytics for Intelligent Transportation Systems
Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems that includes detailed coverage of the tools needed to implement these methods using big data analytics and other computing techniques. The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce. It explores collecting, archiving, processing, and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies. Users will learn how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning.
R 3,825.00
Operations and Supply Management 4.0
Fierce competition, globalisation and the permanent liberalisation of markets have changed the face of supply chains and operations drastically. Companies, which want to survive in a hostile environment, must establish the optimum combination of supply and operations. This book provides a holistic and practical approach to operations management 4.0 and supply management 4.0. It combines operations and supply best practices across the value chain. It explains comprehensively, how these new paradigms enable companies to concentrate on value-adding activities and processes to achieve a long-term sustainable and competitive advantage.The book contains a variety of best practices, industry examples and case studies. Focusing on best-in-class examples, the book offers the ideal guide for any enterprise in operations and supply in order to achieve a competitive advantage across all business functions focusing on value-adding activities.
R 2,896.77
Optimal Decision Making in Operations Research and Statistics
The book provides insights in the decision-making for implementing strategies in various spheres of real-world issues. It integrates optimal policies in various decisionmaking problems and serves as a reference for researchers and industrial practitioners. Furthermore, the book provides sound knowledge of modelling of real-world problems and solution procedure using the various optimisation and statistical techniques for making optimal decisions. The book is meant for teachers, students, researchers and industrialists who are working in the field of materials science, especially operations research and applied statistics.
R 2,931.00
Industrial Engineering and Operations Management I
Based on the 2018 International Joint Conference on Industrial Engineering and Operations Management (IJCIEOM) conference that took place in Lisbon, Portugal, this proceedings volume is the first of two focusing on mathematical applications in digital transformation. The different contributions in this volume explore topics such as modelling, simulation, logistics, innovation, sustainability, health care, supply chain, lean manufacturing, operations management, quality and digital. Written by renowned scientists from around the world, this multidisciplinary volume serves as a reference on industrial engineering and operations management and as a source on current findings for researchers and students aiming to work on industrial-related problems.
R 6,654.41
Secure big-data analytics for emerging healthcare in 5G and beyond
Healthcare systems today are increasingly reliant on data gathered from multiple hospital systems, patient records or IoT devices. As more information is gathered, there is a need to ensure the data is kept and used securely. This edited book looks at secure big data analytics for healthcare and how the wealth of information is disseminated through open wireless channels to provide seamless coverage so that people can access and analyse the results obtained and intelligently manage and respond to a patient's needs. The editors cover current and emerging frameworks, architectures, and solutions that address the requirements of secure big data analytics for the healthcare industry. The book will cover the challenges of deploying security-based healthcare analytics for massive BDA (big data analytics) applications, through smart optimized network communication infrastructures, dense connectivity, and AI-driven models. Topics include big data analytics, trustworthy data sharing, security challenges and privacy preserving techniques, authentication and access control schemes, deep learning models, risk modelling, and blockchain integration. The book provides a great reference for researchers in academia, network professionals, healthcare industry professionals, and researchers working towards emerging secure BDA solutions in 5G and beyond networks.
R 4,559.00
Foundations of Programming, Statistics, and Machine Learning for Business Analytics
Business Analysts and Data Scientists are in huge demand, as global companies seek to digitally transform themselves and leverage their data resources to realize competitive advantage.This book covers all the fundamentals, from statistics to programming to business applications, to equip you with the solid foundational knowledge needed to progress in business analytics. Assuming no prior knowledge of programming or statistics, this book takes a simple step-by-step approach which makes potentially intimidating topics easy to understand, by keeping Maths to a minimum and including examples of business analytics in practice.Key features:· Introduces programming fundamentals using R and Python· Covers data structures, data management and manipulation and data visualization· Includes interactive coding notebooks so that you can build up your programming skills progressivelySuitable as an essential text for undergraduate and postgraduate students studying Business Analytics or as pre-reading for students studying Data Science.Ram Gopal is Pro-Dean and Professor of Information Systems at the University of Warwick.Daniel Philps is an Artificial Intelligence Researcher and Head of Rothko Investment Strategies.Tillman Weyde is Senior Lecturer at City, University of London.
R 3,237.00
Long View of Research and Practice in Operations Research and Management Science
From the Foreword by Marshall Fisher, The Wharton School, University of Pennsylvania:As generation of academics and practitioners follows generation, it is worthwhile tocompile long views of the research and practice in the past to shed light on researchand practice going forward. This collection of peer-reviewed articles is intended toprovide such a long view.This book contains a collection of chapters written by leading scholars/practitionerswho have continued their efforts in developing and/or implementing innovativeOR/MS tools for solving real world problems. In this book, the contributors sharetheir perspectives about the past, present and future of OR/MS theoretical development, solution tools, modeling approaches, and applications. Specifically, this book collects chapters that offer insights about the following topics:• Survey articles taking a long view over the past two or more decades to arriveat the present state of the art while outlining ideas for future research. Surveysfocus on use of a particular OR/MS approach, e.g., mathematical programming(LP, MILP, etc.) and solution methods for particular family of application, e.g.,distribution system design, distribution planning system, health care.• Autobiographical or biographical accounts of how particular inventions (e.g.,Structured Modeling) were made. These could include personal experiences inearly development of OR/MS and an overview of what has happened since.• Development of OR/MS mathematical tools (e.g., stochastic programming, optimizationtheory).• Development of OR/MS in a particular industry sector such as global supplychain management.• Modeling systems for OR/MS and their development over time as well as speculationon future development (e.g., LINDO, LINGO, and What’sBest!)• New applications of OR/MS models (e.g., happiness)The target audience of this book is young researchers, graduate/advanced undergraduatestudents from OR/MS and related fields like computer science, engineering,and management as well as practitioners who want to understand how OR/MSmodeling came about over the past few decades and what research topics or modelingapproaches they could pursue in research or application.
R 3,041.00
BI Without the BS
Stop asking stakeholders what they want—start uncovering what they truly need. Many business intelligence (BI) projects fail—not because of bad technology, but because of poor requirements. Stakeholders often don't know what they need, and project teams assume too much. The result? Costly delays, endless rework, and disappointing solutions. BI Without the BS offers a practical, proven framework to turn that around. Drawing from decades of experience in telecom, finance, retail, and global organizations, Eva Polini and Karina Ghozali guide readers through the real-world challenges of gathering BI requirements—clearly, confidently, and effectively. Whether you're a business analyst, data visualization specialist, project manager, or BI leader, this book will help you: Understand the foundations of business analysis and requirement engineering—then how to apply them to BI, Go beyond surface-level requests to uncover core business needs, Deliver BI assets that are adopted, trusted, and aligned with strategic goals, Build stronger relationships with stakeholders while reducing friction and rework. Through relatable actionable steps, the authors empower analysts to become proactive change agents who design solutions that genuinely support decision-making. Clear, accessible, and rich with insight, this book is your roadmap to building BI solutions that work the first time—on time and on budget. If you're ready to go beyond gathering requirements—and start driving results—this book is for you.
R 1,041.47
Practical Guide to Operations Management
Practical Guide To Operations Management This book discusses the practical and useful methods for operations management. It describes the ways the managers and employees need to accomplish their work. It discusses the administration, planning, strategy methods for the operations management. The book shows the operational environmental effects and causes. Operations project management is discussed with its trends, planning, implementation and leading. It focuses on the operational management of a firm or corporation. A discussion of the products and services of this operational management is accomplished. The Total quality management is described with the ISO 9000 and the operations financial management. The book could be unique because it could be a guide for managers and employees with practical consideration in how to make the job done, in operations fields. It concern in practical methods and procedures that could be followed, with some theoretical principals for general and operations management.
R 5,642.00
R 306.00
Soft Computing for Data Analytics, Classification Model, and Control
This book presents a set of soft computing approaches and their application in data analytics, classification model, and control. The basics of fuzzy logic implementation for advanced hybrid fuzzy driven optimization methods has been covered in the book. The various soft computing techniques, including Fuzzy Logic, Rough Sets, Neutrosophic Sets, Type-2 Fuzzy logic, Neural Networks, Generative Adversarial Networks, and Evolutionary Computation have been discussed and they are used on variety of applications including data analytics, classification model, and control.The book is divided into two thematic parts. The first thematic section covers the various soft computing approaches for text classification and data analysis, while the second section focuses on the fuzzy driven optimization methods for the control systems. The chapters has been written and edited by active researchers, which cover hypotheses and practical considerations; provide insights into the design of hybrid algorithms for applications in data analytics, classification model, and engineering control.
R 6,015.37
Cross-Disciplinary Models and Applications of Database Management
Cross-Disciplinary Models and Applications of Database Management: Advancing Approaches is an updated look at the latest tools and technology within the burgeoning field of database management. Perfect for the network administrator, technician, information technology specialist or consultant, or for academics and students, this volume presents the latest the field has to offer by way of cases and new research. As database languages, models, and systems change, it’s vital for practitioners within the field to stay abreast of the latest research and methods being used around the world, and this book offers the most current advances available.
R 7,611.34
Big Data Analytics in Bioinformatics and Healthcare
As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information.Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. Complete with interdisciplinary research resources, this publication is an essential reference source for researchers, practitioners, and students interested in the fields of biological computation, database management, and health information technology, with a special focus on the methodologies and tools to manage massive and complex electronic information.
R 9,907.12
Data Base Management: Theory and Applications
This volume is based on lectures presented at the N.A.T.O. Advanced Studies Institute on Data Base Management Theory and Applications. The meeting took place in Estoril Portugal for a two week periQd in June 1981. The lecturers represented distinguished research centers in industry, gvvernment and academia. Lectures presented basic material in data base management, as well as sharing recent developments in the field. The participants were drawn from data processing groups in government, industry and academia, located in N.A.T.O. countries. All participants had a common goal of learning about the exciting new developments in the field of data base management with the potential for application to their fields of interest. In addition to formal lectures and the informal discussions among participants, which are characteristic of N.A.T.O. AS! gatherings, participants had the opportunity for hands-on experience in building application systems with a data base management system. Participants were organized into groups that designed and implemented application systems using data base technology on micro computers. The collection of papers is organized into four major sections. The first section deals with various aspects of data modeling from the conceptual and logical perspectives. These issues are crucial in the initial design of application systems.
R 4,671.19
Management and Business Research
Having sold over 70,000 copies, this market-leading textbook provides readers with a clear and comprehensive overview of methods for conducting management and business research. It combines practical guidance with academic depth and uses the much-loved visual metaphor of a tree to guide students through the research process. The eighth edition has been fully updated and includes: Enhanced accessibility and improved flow throughout, with clearer explanations for international students and an expanded glossaryMore diverse examples and research in action textboxes showcasing how research is done all over the world, as well as cover topics such as researching and writing as a non-native speakerExpanded and up-to-date coverage of Generative AI, data analytics, and visual, virtual and participatory methodsExpanded content on case study research Strengthened practical guidance on ethics, academic integrity (including authorship, GenAI and plagiarism) and writing and dissemination.This book is suitable for both undergraduate and postgraduate students studying research methods as part of a business and management course.LENA J. JASPERSEN is a Lecturer in Innovation and Qualitative Research Methods at the University of Leeds.DANAT VALIZADE is a Professor in Quantitative Methods at the University of Leeds.RICHARD THORPE is an Emeritus Professor at the University of Leeds.
R 2,068.00
Exploring Advances in Interdisciplinary Data Mining and Analytics
Exploring Advances in Interdisciplinary Data Mining and Analytics: New Trends is an updated look at the state of technology in the field of data mining and analytics. As processor speeds, database size, network capabilities, artificial intelligence, and most fields of hardware and software continue to improve at a staggering rate of increased capability and pace, it is vital for practitioners to stay abreast of the current issues and research in the field. This volume is perfect for IT specialists, data analysts, practitioners and academics alike, offering the latest technological, analytical, ethical, and commercial perspectives on topics in data mining.
R 7,566.05
High-Performance Big-Data Analytics
This book presents a detailed review of high-performance computing infrastructures for next-generation big data and fast data analytics. Features: includes case studies and learning activities throughout the book and self-study exercises in every chapter; presents detailed case studies on social media analytics for intelligent businesses and on big data analytics (BDA) in the healthcare sector; describes the network infrastructure requirements for effective transfer of big data, and the storage infrastructure requirements of applications which generate big data; examines real-time analytics solutions; introduces in-database processing and in-memory analytics techniques for data mining; discusses the use of mainframes for handling real-time big data and the latest types of data management systems for BDA; provides information on the use of cluster, grid and cloud computing systems for BDA; reviews the peer-to-peer techniques and tools and the common information visualization techniques, used in BDA.
R 3,061.41
R 4,474.00
Decision Maker's Handbook to Data Science
Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. This third edition delves into the latest advancements in AI, particularly focusing on large language models (LLMs), with clear distinctions made between AI and traditional data science, including AI's ability to emulate human decision-making. Author Stylianos Kampakis introduces you to the critical aspect of ethics in AI, an area of growing importance and scrutiny. The narrative examines the ethical considerations intrinsic to the development and deployment of AI technologies, including bias, fairness, transparency, and accountability. You’ll be provided with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated edition also includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists.Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker’s Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide.What You Will LearnIntegrate AI with other innovative technologies Explore anticipated ethical, regulatory, and technical landscapes that will shape the future of AI and data scienceDiscover how to hire and manage data scientistsBuild the right environment in order to make your organization data-drivenWho This Book Is ForStartup founders, product managers, higher level managers, and any other non-technical decision makers who are thinking to implement data science in their organization and hire data scientists. A secondary audience includes people looking for a soft introduction into the subject of data science.
R 1,254.00
