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Granular Computing and Big Data Advancements
In an era defined by the deluge of data, navigating the complexities of decision-making under conditions of uncertainty has emerged as a formidable challenge for scholars and practitioners alike. The sheer volume and velocity of information inundating decision-makers often leads to paralysis or misguided choices, amplifying the risks inherent in uncertain environments. Granular Computing and Big Data Advancements provides insights and solutions in this challenging landscape. This comprehensive publication addresses the fundamental dilemma decision-makers face: how to harness the power of big data amidst uncertainty to make informed choices that drive success. By delving into the intricacies of big data analytics, uncertainty modeling, and advanced quantification techniques, the book equips scholars with the necessary tools to extract meaningful insights from vast datasets and confidently navigate uncertainty. Through real-world case studies spanning diverse domains such as finance, healthcare, and marketing, readers gain invaluable practical insights into applying these methodologies in their contexts. The impact of Granular Computing and Big Data Advancements reverberates across the research community, offering a cohesive resource that bridges the gap between theory and practice. With its interdisciplinary approach and emphasis on innovation, the book fosters collaboration and empowers scholars to tackle complex challenges head-on. Whether researchers seek novel methodologies, practitioners aim to enhance decision-making processes, or students embark on their academic journey, this publication serves as a cornerstone in the quest for effective decision-making amidst the uncertainties of the modern world.
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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 5,831.92
Focus On Finance: Everything You Need To Know About Financial Management
An up-to-date introductory textbook, Focus on Finance offers a concise, enthusiastic and highly focused approach to introducing finance to the uninitiated student. It integrates theory with practice in an easy-to-read and concise style and covers a variety of topics ranging from financial forecasting to new technologies such as fintech and cryptocurrencies and their implications for the future of finance.Designed to be used by students of all backgrounds, this book requires no preliminary knowledge of finance. It is a must-read for readers eager to get a better understanding of the financial realm.
R 4,202.63
Data Analytics Made Easy
Learn how to gain insights from your data as well as machine learning and become a presentation pro who can create interactive dashboardsKey FeaturesEnhance your presentation skills by implementing engaging data storytelling and visualization techniquesLearn the basics of machine learning and easily apply machine learning models to your dataImprove productivity by automating your data processesBook DescriptionData Analytics Made Easy is an accessible beginner’s guide for anyone working with data. The book interweaves four key elements:Data visualizations and storytelling – Tired of people not listening to you and ignoring your results? Don’t worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience.Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You’ll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components. Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You’ll not only be able to understand data scientists’ machine learning models; you’ll be able to challenge them and build your own.Creating interactive dashboards – Follow the book’s simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results.What you will learnUnderstand the potential of data and its impact on your businessImport, clean, transform, combine data feeds, and automate your processesInfluence business decisions by learning to create engaging presentationsBuild real-world models to improve profitability, create customer segmentation, automate and improve data reporting, and moreCreate professional-looking and business-centric visuals and dashboardsOpen the lid on the black box of AI and learn about and implement supervised and unsupervised machine learning modelsWho this book is forThis book is for beginners who work with data and those who need to know how to interpret their business/customer data. The book also covers the high-level concepts of data workflows, machine learning, data storytelling, and visualizations, which are useful for managers.No previous math, statistics, or computer science knowledge is required.
R 1,140.00
Geographic Data Science with Python
This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data.Key Features:● Showcases the excellent data science environment in Python.● Provides examples for readers to replicate, adapt, extend, and improve.● Covers the crucial knowledge needed by geographic data scientists.It presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts.Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field.
R 2,711.00
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.
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Analyzing Analytics
This book aims to achieve the following goals: (1) to provide a high-level survey of key analytics models and algorithms without going into mathematical details; (2) to analyze the usage patterns of these models; and (3) to discuss opportunities for accelerating analytics workloads using software, hardware, and system approaches. The book first describes 14 key analytics models (exemplars) that span data mining, machine learning, and data management domains. For each analytics exemplar, we summarize its computational and runtime patterns and apply the information to evaluate parallelization and acceleration alternatives for that exemplar. Using case studies from important application domains such as deep learning, text analytics, and business intelligence (BI), we demonstrate how various software and hardware acceleration strategies are implemented in practice. This book is intended for both experienced professionals and students who are interested in understanding core algorithms behind analytics workloads. It is designed to serve as a guide for addressing various open problems in accelerating analytics workloads, e.g., new architectural features for supporting analytics workloads, impact on programming models and runtime systems, and designing analytics systems.
R 1,457.96
Effective Trading in Financial Markets Using Technical Analysis
This book provides a comprehensive guide to effective trading in the financial markets through the application of technical analysis through the following: Presenting in-depth coverage of technical analysis tools (including trade set-ups) as well as backtesting and algorithmic trading Discussing advanced concepts such as Elliott Waves, time cycles and momentum, volume, and volatility indicators from the perspective of the global markets and especially India Blending practical insights and research updates for professional trading, investments, and financial market analyses Including detailed examples, case studies, comparisons, figures, and illustrations from different asset classes and markets in simple languageThe book will be essential for scholars and researchers of finance, economics and management studies, as well as professional traders and dealers in financial institutions (including banks) and corporates, fund managers, investors, and anyone interested in financial markets.
R 2,642.08
Computational Frameworks for Political and Social Research with Python
This book is intended to serve as the basis for a first course in Python programming for graduate students in political science and related fields. The book introduces core concepts of software development and computer science such as basic data structures (e.g. arrays, lists, dictionaries, trees, graphs), algorithms (e.g. sorting), and analysis of computational efficiency. It then demonstrates how to apply these concepts to the field of political science by working with structured and unstructured data, querying databases, and interacting with application programming interfaces (APIs). Students will learn how to collect, manipulate, and exploit large volumes of available data and apply them to political and social research questions. They will also learn best practices from the field of software development such as version control and object-oriented programming. Instructors will be supplied with in-class example code, suggested homework assignments (with solutions), and material for practical lab sessions.
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Statistical Data Analysis for the Physical Sciences
Data analysis lies at the heart of every experimental science. Providing a modern introduction to statistics, this book is ideal for undergraduates in physics. It introduces the necessary tools required to analyse data from experiments across a range of areas, making it a valuable resource for students. In addition to covering the basic topics, the book also takes in advanced and modern subjects, such as neural networks, decision trees, fitting techniques and issues concerning limit or interval setting. Worked examples and case studies illustrate the techniques presented, and end-of-chapter exercises help test the reader's understanding of the material.
R 1,224.39
Essentials of Excel, Excel VBA, SAS and Minitab for Statistical and Financial Analyses
This introductory textbook for business statistics teaches statistical analysis and research methods via business case studies and financial data using Excel, Minitab, and SAS. Every chapter in this textbook engages the reader with data of individual stock, stock indices, options, and futures.One studies and uses statistics to learn how to study, analyze, and understand a data set of particular interest. Some of the more popular statistical programs that have been developed to use statistical and computational methods to analyze data sets are SAS, SPSS, and Minitab. Of those, we look at Minitab and SAS in this textbook. One of the main reasons to use Minitab is that it is the easiest to use among the popular statistical programs. We look at SAS because it is the leading statistical package used in industry. We also utilize the much less costly and ubiquitous Microsoft Excel to do statistical analysis, as the benefits of Excel have become widely recognized in the academic world and its analytical capabilities extend to about 90 percent of statistical analysis done in the business world. We demonstrate much of our statistical analysis using Excel and double check the analysis and outcomes using Minitab and SAS—also helpful in some analytical methods not possible or practical to do in Excel.
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Cleaning Data for Effective Data Science
Think about your data intelligently and ask the right questionsKey FeaturesMaster data cleaning techniques necessary to perform real-world data science and machine learning tasksSpot common problems with dirty data and develop flexible solutions from first principlesTest and refine your newly acquired skills through detailed exercises at the end of each chapterBook DescriptionData cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way. In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with. Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses.What you will learnIngest and work with common data formats like JSON, CSV, SQL and NoSQL databases, PDF, and binary serialized data structuresUnderstand how and why we use tools such as pandas, SciPy, scikit-learn, Tidyverse, and BashApply useful rules and heuristics for assessing data quality and detecting bias, like Benford’s law and the 68-95-99.7 ruleIdentify and handle unreliable data and outliers, examining z-score and other statistical propertiesImpute sensible values into missing data and use sampling to fix imbalancesUse dimensionality reduction, quantization, one-hot encoding, and other feature engineering techniques to draw out patterns in your dataWork carefully with time series data, performing de-trending and interpolationWho this book is forThis book is designed to benefit software developers, data scientists, aspiring data scientists, teachers, and students who work with data. If you want to improve your rigor in data hygiene or are looking for a refresher, this book is for you.Basic familiarity with statistics, general concepts in machine learning, knowledge of a programming language (Python or R), and some exposure to data science are helpful.
R 1,498.00
Data Analytics: A Theoretical and Practical View from the EDISON Project
Building upon the knowledge introduced in The Data Science Framework, this book provides a comprehensive and detailed examination of each aspect of Data Analytics, both from a theoretical and practical standpoint. The book explains representative algorithms associated with different techniques, from their theoretical foundations to their implementation and use with software tools.Designed as a textbook for a Data Analytics Fundamentals course, it is divided into seven chapters to correspond with 16 weeks of lessons, including both theoretical and practical exercises. Each chapter is dedicated to a lesson, allowing readers to dive deep into each topic with detailed explanations and examples. Readers will learn the theoretical concepts and then immediately apply them to practical exercises to reinforce their knowledge. And in the lab sessions, readers will learn the ins and outs of the R environment and data science methodology to solve exercises with the R language. With detailed solutions provided for all examples and exercises, readers can use this book to study and master data analytics on their own. Whether you're a student, professional, or simply curious about data analytics, this book is a must-have for anyone looking to expand their knowledge in this exciting field.
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Analysis for Financial Management ISE
Like its predecessors, the thirteenth edition of Analysis for Financial Management is for nonfinancial executives and business students interested in the practice of financial management. It introduces standard techniques and recent advances in a practical, intuitive way. This product assumes no prior background beyond a rudimentary and perhaps rusty familiarity with financial statements—although a healthy curiosity about what makes business tick is also useful. The emphasis throughout is on the managerial implications of financial analysis.Analysis for Financial Management should prove valuable to individuals interested in sharpening their managerial skills and to executive program participants. This product has also found a home in university classrooms as the sole text in Executive MBA and applied finance courses, as a companion text in case-oriented courses, and as a supplementary reading in more theoretical courses.
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Profit Driven Business Analytics
Maximize profit and optimize decisions with advanced business analytics Profit-Driven Business Analytics provides actionable guidance on optimizing the use of data to add value and drive better business. Combining theoretical and technical insights into daily operations and long-term strategy, this book acts as a development manual for practitioners seeking to conceive, develop, and manage advanced analytical models. Detailed discussion delves into the wide range of analytical approaches and modeling techniques that can help maximize business payoff, and the author team draws upon their recent research to share deep insight about optimal strategy. Real-life case studies and examples illustrate these techniques at work, and provide clear guidance for implementation in your own organization. From step-by-step instruction on data handling, to analytical fine-tuning, to evaluating results, this guide provides invaluable guidance for practitioners seeking to reap the advantages of true business analytics. Despite widespread discussion surrounding the value of data in decision making, few businesses have adopted advanced analytic techniques in any meaningful way. This book shows you how to delve deeper into the data and discover what it can do for your business. Reinforce basic analytics to maximize profitsAdopt the tools and techniques of successful integrationImplement more advanced analytics with a value-centric approachFine-tune analytical information to optimize business decisions Both data stored and streamed has been increasing at an exponential rate, and failing to use it to the fullest advantage equates to leaving money on the table. From bolstering current efforts to implementing a full-scale analytics initiative, the vast majority of businesses will see greater profit by applying advanced methods. Profit-Driven Business Analytics provides a practical guidebook and reference for adopting real business analytics techniques.
R 1,139.00
Mastering Python for Networking and Security
Master Python scripting to build a network and perform security operationsKey FeaturesLearn to handle cyber attacks with modern Python scriptingDiscover various Python libraries for building and securing your networkUnderstand Python packages and libraries to secure your network infrastructureBook DescriptionIt’s becoming more and more apparent that security is a critical aspect of IT infrastructure. A data breach is a major security incident, usually carried out by just hacking a simple network line. Increasing your network’s security helps step up your defenses against cyber attacks. Meanwhile, Python is being used for increasingly advanced tasks, with the latest update introducing many new packages. This book focuses on leveraging these updated packages to build a secure network with the help of Python scripting. This book covers topics from building a network to the different procedures you need to follow to secure it. You’ll first be introduced to different packages and libraries, before moving on to different ways to build a network with the help of Python scripting. Later, you will learn how to check a network’s vulnerability using Python security scripting, and understand how to check vulnerabilities in your network. As you progress through the chapters, you will also learn how to achieve endpoint protection by leveraging Python packages along with writing forensic scripts. By the end of this book, you will be able to get the most out of the Python language to build secure and robust networks that are resilient to attacks.What you will learnDevelop Python scripts for automating security and pentesting tasksDiscover the Python standard library's main modules used for performing security-related tasksAutomate analytical tasks and the extraction of information from serversExplore processes for detecting and exploiting vulnerabilities in serversUse network software for Python programmingPerform server scripting and port scanning with PythonIdentify vulnerabilities in web applications with PythonUse Python to extract metadata and forensicsWho this book is forThis book is ideal for network engineers, system administrators, or any security professional looking at tackling networking and security challenges. Programmers with some prior experience in Python will get the most out of this book. Some basic understanding of general programming structures and Python is required.
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Health Analytics
A hands-on, analytics road map for health industry leaders The industry-wide transformation taking place across the health and life sciences ecosystem is mandating that organizations adopt new decision-making capabilities, based on science and real-world information. Analytics will be a required competency for the modern health enterprise; this book is about how to "cross the chasm." The ultimate analytics guide for the health industry leader, this essential book equips business leaders with little-to-no experience in analytics to understand how to incorporate analytics as a cornerstone of their 21st century competitive business strategy. Paints the picture for a new health enterprise, one focused on the patientExplores the financial components of this new operating model, using analytics to optimize the tradeoffs between cost and valueDeals with the rising role of the consumer, using analytics to create a completely new health engagement model with individual recipients of careLooks at how analytics can drive innovations in care practice, patient-experienced medical outcomes, and analytically driven novel therapies optimized for the individual patientPresents a variety of text, tables, and graphics illustrating the various concepts being described Within each section and chapter, Health Analytics assesses the current landscape, proposing a new model/concept, sharing real-world stories of how the old and new world come together, and framing a "how-to" for the reader in terms of growing that particular set of capabilities in their own enterprises.
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Big Data Analytics for Intelligent Healthcare Management
Big Data Analytics for Intelligent Healthcare Management covers both the theory and application of hardware platforms and architectures, the development of software methods, techniques and tools, applications and governance, and adoption strategies for the use of big data in healthcare and clinical research. The book provides the latest research findings on the use of big data analytics with statistical and machine learning techniques that analyze huge amounts of real-time healthcare data.
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Artificial Intelligence in Economic and Financial Analysis
Artificial Intelligence in Economic and Financial Analysis: Machine Learning for Forecasting, Modelling and Decision-Making examines the growing role of artificial intelligence (AI) and machine learning (ML) in transforming economics and finance in increasingly digital, data-driven global economies.The book explores how intelligent technologies are reshaping forecasting, modelling, financial analysis, and decision-making through predictive analytics, automation, and computational intelligence. It provides a comprehensive discussion of the theoretical foundations of AI and ML before examining their applications across macroeconomic forecasting, labour market analytics, algorithmic trading, insurance analytics, fraud detection, credit scoring, and natural language processing. The book further analyses how AI-driven systems are influencing financial services, economic policy analysis, digital finance, and intelligent governance while also addressing ethical and regulatory concerns relating to transparency, explainability, cybersecurity, accountability, and algorithmic bias. In addition, emerging developments such as generative AI, autonomous systems, and intelligent financial technologies are discussed to provide insight into the future of economics and finance.Designed for academics, researchers, postgraduate students, policymakers, and industry professionals, this book offers both theoretical and practical perspectives on how AI and ML are reshaping economic and financial systems in the Fourth Industrial Revolution.
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Using Applied Mathematical Models for Business Transformation
Traditional business practices have been left behind due to the increased use of data analytics and information technology in companies worldwide. This development has led to businesses implementing transformative projects that use these new technologies in their decision-making systems. Altering the entire architecture of a company is a daunting task; however, researchers are finding methods through applied mathematics that can make it easier on companies. Implementing analytical models into current business processes is vital for professionals across the globe.Using Applied Mathematical Models for Business Transformation is an essential reference source that discusses the advancement of decision-making systems in business environments with the use of applied mathematics, algorithms, and information technology. Featuring research on topics such as decision-making systems, critical success factors, and global enterprise architecture, this book is ideally designed for project managers, financial analysts, business strategists, software engineers, technical architects, students, researchers, and educators seeking coverage on the transformation of business practices using applied mathematics and information technology.
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Introduction to Python for Humanists
This book will introduce digital humanists at all levels of education to Python. It provides background and guidance on learning the Python computer programming language, and as it presumes no knowledge on the part of the reader about computers or coding concepts allows the reader to gradually learn the more complex tasks that are currently popular in the field of digital humanities. This book will be aimed at undergraduates, graduates, and faculty who are interested in learning how to use Python as a tool within their workflow. An Introduction to Python for Digital Humanists will act as a primer for students who wish to use Python, allowing them to engage with more advanced textbooks. This book fills a real need, as it is first Python introduction to be aimed squarely at humanities students, as other books currently available do not approach Python from a humanities perspective. It will be designed so that those experienced in Python can teach from it, in addition to allowing those who are interested in being self-taught can use it for that purpose.Key Features: Data analysis Data science Computational humanities Digital humanities Python Natural language processing Social network analysisApp development
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Financial Mathematics for Cryptocurrencies
Master the quantitative foundations you need to successfully invest in and trade digital assets Financial Mathematics for Cryptocurrencies by Tom J. Espel combines two of today's most dynamic fields – quantitative finance and cryptocurrencies – in a comprehensive guide that addresses the unique mathematical challenges faced by everyone involved in the crypto markets. Espel draws on his extensive experience in frontier assets to explain the analytical frameworks you’ll need to make informed investment decisions, identify pricing opportunities, and manage risk in this volatile asset class. The book adapts relevant quantitative finance methodologies specifically for digital assets, bridging the gap between traditional financial mathematics and the distinctive characteristics of blockchain-based instruments. Espel introduces three essential constructs for DeFi pricing theory: network time, the validator account as a new numéraire, and wrapped token frameworks for cross-chain valuation. Its modular structure allows readers to navigate directly to relevant sections, covering everything from blockchain fundamentals to advanced valuation models, staking contract mathematics, and liquidity cost analysis in cryptocurrency markets. You’ll find: Mathematical frameworks for staking contracts, liquid staking derivatives, and yield farming strategies with rigorous ex-ante and ex-post valuations Comprehensive coverage of network valuation methods including Metcalfe's Law, Reed's Law, and the ZBOT framework specifically applied to digital assetsPricing theory extending arbitrage-free pricing to blockchain assets through the validator account and blockchain measure (B-measure)Expert insights from an author specializing in quantitative strategies for electronic and illiquid assets, with expertise in market microstructure and volatility modelingAccessible mathematical solutions designed for practitioners in applied mathematics and quantitative finance, with clear and rigorous explanations Perfect for quantitative analysts, traders, portfolio managers, cryptocurrency researchers, and finance students, Financial Mathematics for Cryptocurrencies is an indispensable resource for applying established financial mathematics to the digital asset ecosystem. It’s a must-read for everyone developing trading algorithms and pricing models, conducting digital asset analysis, or researching cryptocurrency markets.
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Sport Business Analytics
Developing and implementing a systematic analytics strategy can result in a sustainable competitive advantage within the sport business industry. This timely and relevant book provides practical strategies to collect data and then convert that data into meaningful, value-added information and actionable insights. Its primary objective is to help sport business organizations utilize data-driven decision-making to generate optimal revenue from such areas as ticket sales and corporate partnerships. To that end, the book includes in-depth case studies from such leading sports organizations as the Orlando Magic, Tampa Bay Buccaneers, Duke University, and the Aspire Group.The core purpose of sport business analytics is to convert raw data into information that enables sport business professionals to make strategic business decisions that result in improved company financial performance and a measurable and sustainable competitive advantage. Readers will learn about the role of big data and analytics in:Ticket pricingSeason ticket member retentionFan engagementSponsorship valuationCustomer relationship managementDigital marketingMarket researchData visualization.This book examines changes in the ticketing marketplace and spotlights innovative ticketing strategies used in various sport organizations. It shows how to engage fans with social media and digital analytics, presents techniques to analyze engagement and marketing strategies, and explains how to utilize analytics to leverage fan engagement to enhance revenue for sport organizations.Filled with insightful case studies, this book benefits both sports business professionals and students. The concluding chapter on teaching sport analytics further enhances its value to academics.
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Data Analytics and Business Intelligence
Business Analytics (BA) is an evolving phenomenon that showcases the increasing importance of using huge volumes of data to generate value for businesses. Advances in BA have offered great opportunities for organisations to improve, innovate, and develop existing or new processes, products, and services. BA is the process of transforming data into actionable insight by using statistical and mathematical analysis, descriptive, prescriptive, and predictive models, machine learning, information systems and network science methods, among others, along with a variety of data, expert knowledge, and fact-based management to support better and faster decision-making.BA and Business Intelligence (BI) generate capabilities for companies to compete in the market effectively and has become one of the main functional areas in most companies. BA tools are used in diverse ways, for example, to identify consumer behaviour patterns and market trends, to derive valuable insights on the performance of stocks, to find information on the attrition rate of employees, to analyse and solve healthcare problems, to offer insight into inventory management and supply chain management, to analyse data from social networks, and to infer traffic behaviour and develop traffic management policy, among others.BA and BI have become one of the most popular research areas in academic circles, as well as in the industry, driven by the increasing demand in the business world. This book aims to become a stimulus for innovative business solutions covering a wide range of aspects of business analytics, such as management science, information technology, descriptive, prescriptive, and predictive models, machine learning, network science, mathematical and statistical techniques. The book will encompass a valuable collection of chapters exploring and discussing computational frameworks, practices, and applications of BA that can assist industries and relevant stakeholders in decision-making and problem-solving exercises, with a view to driving competitive advantage.
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Data-Enabled Analytics
This book explores the novel uses and potentials of Data Envelopment Analysis (DEA) under big data. These areas are of widespread interest to researchers and practitioners alike. Considering the vast literature on DEA, one could say that DEA has been and continues to be, a widely used technique both in performance and productivity measurement, having covered a plethora of challenges and debates within the modelling framework.
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Big Data Analytics
Big Data Analytics: Digital Marketing and Decision-Making covers the advances related to marketing and business analytics. Investment marketing analytics can create value through proper allocation of resources and resource orchestration processes. The use of data analytics tools can be used to improve and speed decision-making processes.Chapters examining analytics for decision-making cover such topics as: Big data analytics for gathering business intelligenceData analytics and consumer behaviorThe role of big data analytics in organizational decision-makingThis book also looks at digital marketing and focuses on such areas as: The prediction of marketing by consumer analyticsWeb analytics for digital marketingSmart retailingLeveraging web analytics for optimizing digital marketing strategiesBig Data Analytics: Digital Marketing and Decision-Making aims to help organizations increase their profits by making better decisions on time through the use of data analytics. It is written for students, practitioners, industry professionals, researchers, and faculty working in the field of commerce and marketing, big data analytics, and organizational decision-making.
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Business Economics and Finance with MATLAB, GIS, and Simulation Models
This book takes recent theoretical advances in Finance and Economics and shows how they can be implemented in the real world. It presents tactics for using mathematical and simulation models to solve complex tasks of forecasting income, valuing businesses, predicting retail sales, and evaluating markets and tax and regulatory problems.Business Economics and Finance with Matlab, GIS, and Simulation Models provides a unique overview of sophisticated business and financial applications. It describes models that have been developed for analysis of retail sales, tax policy, location, economic impact, public policy issues, and other challenges faced by executives, investors, and economists on a daily basis. It also offers groundbreaking insight into the many calculation and modeling tools that can be remotely hosted and run over the Internet, resulting in substantial user benefits and cost savings. This book is the first to fully explore the capabilities of MATLAB in the field of business economics, and explain how the benefits of sophisticated mathematical models can be provided to users via the Internet, using a thin-client environment. Many techniques directly incorporate geographic information and GIS in a way that was impossible until quite recently. Some techniques, such as fuzzy logic, retail sales, economic and fiscal impact models, and other Matlab and Simulink models, are described for the first time in print in this book. The sections on business income and value break new ground by directly incorporating uncertainty, real option value, and prediction of variables using Ito and jump processes. Using dozens of examples, hundreds of references, and rigorous explanations of both theory and practice, it will become a prized reference for analysts demanding the best techniques.
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Meta-Analytics
Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behavior than the use of traditional analytics approaches. The book is ‘meta’ to analytics, covering general analytics in sufficient detail for readers to engage with, and understand, hybrid or meta- approaches. The book has relevance to machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance. Inn addition, the analytics within can be applied to predictive algorithms for everyone from police departments to sports analysts.
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Introduction to Data Analysis using Aggregation Functions in R
This textbook helps future data analysts comprehend aggregation function theory and methods in an accessible way, focusing on a fundamental understanding of the data and summarization tools. Offering a broad overview of recent trends in aggregation research, it complements any study in statistical or machine learning techniques. Readers will learn how to program key functions in R without obtaining an extensive programming background.Sections of the textbook cover background information and context, aggregating data with averaging functions, power means, and weighted averages including the Borda count. It explains how to transform data using normalization or scaling and standardization, as well as log, polynomial, and rank transforms. The section on averaging with interaction introduces OWS functions and the Choquet integral, simple functions that allow the handling of non-independent inputs. The final chapters examine software analysis with an emphasis on parameter identification rather than technical aspects.This textbook is designed for students studying computer science or business who are interested in tools for summarizing and interpreting data, without requiring a strong mathematical background. It is also suitable for those working on sophisticated data science techniques who seek a better conception of fundamental data aggregation. Solutions to the practice questions are included in the textbook.
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Numerical Methods for Finance
Featuring international contributors from both industry and academia, Numerical Methods for Finance explores new and relevant numerical methods for the solution of practical problems in finance. It is one of the few books entirely devoted to numerical methods as applied to the financial field.Presenting state-of-the-art methods in this area, the book first discusses the coherent risk measures theory and how it applies to practical risk management. It then proposes a new method for pricing high-dimensional American options, followed by a description of the negative inter-risk diversification effects between credit and market risk. After evaluating counterparty risk for interest rate payoffs, the text considers strategies and issues concerning defined contribution pension plans and participating life insurance contracts. It also develops a computationally efficient swaption pricing technology, extracts the underlying asset price distribution implied by option prices, and proposes a hybrid GARCH model as well as a new affine point process framework. In addition, the book examines performance-dependent options, variance reduction, Value at Risk (VaR), the differential evolution optimizer, and put-call-futures parity arbitrage opportunities.Sponsored by DEPFA Bank, IDA Ireland, and Pioneer Investments, this concise and well-illustrated book equips practitioners with the necessary information to make important financial decisions.
R 3,829.89
What Every Engineer Should Know About Data-Driven Analytics
What Every Engineer Should Know About Data-Driven Analytics provides a comprehensive introduction to the theoretical concepts and approaches of machine learning that are used in predictive data analytics. By introducing the theory and by providing practical applications, this text can be understood by every engineering discipline. It offers a detailed and focused treatment of the important machine learning approaches and concepts that can be exploited to build models to enable decision making in different domains.Utilizes practical examples from different disciplines and sectors within engineering and other related technical areas to demonstrate how to go from data, to insight, and to decision makingIntroduces various approaches to build models that exploits different algorithmsDiscusses predictive models that can be built through machine learning and used to mine patterns from large datasetsExplores the augmentation of technical and mathematical materials with explanatory worked examplesIncludes a glossary, self-assessments, and worked-out practice exercisesWritten to be accessible to non-experts in the subject, this comprehensive introductory text is suitable for students, professionals, and researchers in engineering and data science.
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Using Applied Mathematical Models for Business Transformation
Traditional business practices have been left behind due to the increased use of data analytics and information technology in companies worldwide. This development has led to businesses implementing transformative projects that use these new technologies in their decision-making systems. Altering the entire architecture of a company is a daunting task; however, researchers are finding methods through applied mathematics that can make it easier on companies. Implementing analytical models into current business processes is vital for professionals across the globe.Using Applied Mathematical Models for Business Transformation is an essential reference source that discusses the advancement of decision-making systems in business environments with the use of applied mathematics, algorithms, and information technology. Featuring research on topics such as decision-making systems, critical success factors, and global enterprise architecture, this book is ideally designed for project managers, financial analysts, business strategists, software engineers, technical architects, students, researchers, and educators seeking coverage on the transformation of business practices using applied mathematics and information technology.
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