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Analysing Quantitative Data for Business and Management Students
In Analysing Quantitative Data, Charles A. Scherbaum and Kristen M. Shockley guide the reader through Understanding Quantitative Data Analysis, Basic Components of Quantitative Data Analysis, Conducting Quantitative Data Analysis, Examples of Quantitative Data Analysis and Conclusions. An appendix contains Excel Formulas. Ideal for Business and Management students reading for a Master’s degree, each book in the series may also serve as reference books for doctoral students and faculty members interested in the method. Part of SAGE’s Mastering Business Research Methods Series, conceived and edited by Bill Lee, Mark N. K. Saunders and Vadake K. Narayanan and designed to support researchers by providing in-depth and practical guidance on using a chosen method of data collection or analysis.
R 4,499.44
R 1,596.00
Data Analytics and Digital Transformation
Understanding the significance of data analytics is paramount for digital transformation but in many organisations they are separate units without fully aligned goals. As organisations are applying digital transformations to be adaptive and agile in a competitive environment, data analytics can play a critical role for their success. This book explores the crossroads between them and how to leverage their connection for improved business outcomes. The need to collaborate and share data is becoming an integral part of digital transformation. This creates new opportunities but also requires well considered and continuously assessed decision making as competitiveness is at stake. This book details approaches, concepts and frameworks, as well as actionable insights and good practices, including combined data management and agile concepts. Critical issues are discussed such as data quality and data governance, as well as compliance, privacy, and ethics. It also offers insights into how both private and public organisations can innovate and keep up with growing data volumes and increasing technological developments in the short, mid and long term. This book will be of direct appeal to global researchers and students across a range of business disciplines, including technology and innovation management, organisational studies and strategic management. It is also relevant for policy makers, regulators and executives of private and public organisations looking to implement successful transformation policies.
R 9,312.77
Data and Analytics Playbook
The Data and Analytics Playbook: Proven Methods for Governed Data and Analytic Quality explores the way in which data continues to dominate budgets, along with the varying efforts made across a variety of business enablement projects, including applications, web and mobile computing, big data analytics, and traditional data integration. The book teaches readers how to use proven methods and accelerators to break through data obstacles to provide faster, higher quality delivery of mission critical programs. Drawing upon years of practical experience, and using numerous examples and an easy to understand playbook, Lowell Fryman, Gregory Lampshire, and Dan Meers discuss a simple, proven approach to the execution of multiple data oriented activities. In addition, they present a clear set of methods to provide reliable governance, controls, risk, and exposure management for enterprise data and the programs that rely upon it. In addition, they discuss a cost-effective approach to providing sustainable governance and quality outcomes that enhance project delivery, while also ensuring ongoing controls. Example activities, templates, outputs, resources, and roles are explored, along with different organizational models in common use today and the ways they can be mapped to leverage playbook data governance throughout the organization.
R 1,764.00
R 439.00
Marketing Analytics
Marketing Analytics provides guidelines in the application of statistics using IBM SPSS Statistics Software (SPSS) for students and professionals using quantitative methods in marketing and consumer behavior. With simple language and a practical, screenshot-led approach, the book presents 11 multivariate techniques and the steps required to perform analysis. Each chapter contains a brief description of the technique, followed by the possible marketing research applications. One of these applications is then used in detail to illustrate its applicability in a research context, including the needed SPSS commands and illustrations. Each chapter also includes practical exercises that require the readers to perform the technique and interpret the results, equipping students with the necessary skills to apply statistics by means of SPSS in marketing and consumer research. Finally, there is a list of articles employing the technique that can be used for further reading. This textbook provides introductory material for advanced undergraduate and postgraduate students studying marketing and consumer analytics, teaching methods along with practical software-applied training using SPSS. Support material includes two real data sets to illustrate the techniques’ applications and PowerPoint slides providing a step-by-step guide to the analysis and commented outcomes. Professionals are invited to use the book to select and use the appropriate analytics for their specific context.
R 6,930.45
Python and Matplotlib Essentials for Scientists and Engineers
This book provides an introduction to the core features of the Python programming language and Matplotlib plotting routings for scientists and engineers (or students of either discipline) who want to use Python™ to analyse data, simulate physical processes, and render publication-quality plots. No previous programming experience is needed before reading the first page.Readers will learn the core features of the Python programming language in under a day. They will be able to immediately use Python to implement codes that solve their own problems and make beautiful plots and animations. Python code is extremely fast to prototype, allowing users to achieve results quickly and accurately. The examples within the book are available for download.Python and Matplotlib Essentials for Scientists and Engineers is accessible for motivated high-school students, but will likely be most useful for undergraduate and graduate students as well as working professionals who have some background with the basic mathematical concepts. This book is intended for technical people who want to get things done.
R 1,466.00
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.
R 4,397.10
Mathematical Underpinnings of Analytics
Analytics is the application of mathematical and statistical concepts to large data sets so as to distil insights that offer the owner some options for action and competitive advantage or value. This makes it the most desirable and valuable part of big data science.Driven by the increased data capture from digital platforms, commercial fields are becoming data rich and analytics is growing in many sectors. This book presents analytics within a framework of mathematical theory and concepts building upon firm theory and foundations of probability theory, graphs and networks, random matrices, linear algebra, optimization, forecasting, discrete dynamical systems, and more. Following on from the theoretical considerations, applications are given to data from commercially relevant interests: supermarket baskets; loyalty cards; mobile phone call records; smart meters; 'omic' data; sales promotions; social media; and microblogging. Each chapter tackles a topic in analytics: social networks and digital marketing; forecasting; clustering and segmentation; inverse problems; Markov models of behavioural changes; multiple hypothesis testing and decision-making; and so on. Chapters start with background mathematical theory explained with a strong narrative and then give way to practical considerations and then to exemplar applications. Exercises (and solutions), external data resources, and suggestions for project work are given. The book includes an appendix giving a crash course in Bayesian reasoning, for both ease and completeness.
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R for Business Analytics
R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages. With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. The book tries to expose the reader to a breadth of business analytics topics without burying the user in needless depth. The included references and links allow the reader to pursue business analytics topics. This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses. Business Intelligence (BI) is the seamless dissemination of information through the organization, which primarily involves business metrics both past and current for the use of decision support in businesses. Data Mining (DM) is the process of discovering new patterns from large data using algorithms and statistical methods. To differentiate between the three, BI is mostly current reports, BA is models to predict and strategize and DM matches patterns in big data. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy. The book utilizes Albert Einstein’s famous remarks on making things as simple as possible, but no simpler. This book will blow the last remaining doubts in your mind about using R in your business environment. Even non-technical users will enjoy the easy-to-use examples. The interviews with creators and corporate users of R make the book very readable. The author firmly believes Isaac Asimov was a better writer in spreading science than any textbook or journal author.
R 3,663.00
Advanced Data Analytics in Health
This book introduces readers to the methods, types of data, and scale of analysis used in the context of health. The challenges of working with big data are explored throughout the book, while the benefits are also emphasized through the discoveries made possible by linking large datasets. Methods include thorough case studies from statistics, as well as the newest facets of data analytics: data visualization, modeling and simulation, and machine learning. The diversity of datasets is illustrated through chapters on networked data, image processing, and text, in addition to typical structured numerical datasets. While the methods, types of data, and scale have been individually covered elsewhere, by bringing them all together under one “umbrella” the book highlights synergies, while also helping scholars fluidly switch between tools as needed. New challenges and emerging frontiers are also discussed, helping scholars grasp how methods will need to change in response to the latest challenges in health.
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R 397.00
Mathematical Models in Financial Technologies
The book explores the mathematical and computational techniques used in Decentralized Finance (DeFi) and Financial Technology (FinTech).DeFi operates on smart contracts, which are self-executing programs that automate the necessary actions for conducting transactions on a blockchain.FinTech encompasses a broader range of services, including digital payments, mobile banking, and financial analytics, often powered by machine learning and data science.This book integrates mathematical concepts such as cryptographic algorithms, stochastic modeling, optimization, and game theory with computer science principles of Consensus mechanisms in cryptocurrency networks that include:Proof-of-Work (PoW): Users solve complex computational puzzles to add new blocks to the blockchain.Proof-of-Stake (PoS): Users validate transactions based on the number of coins they hold and are willing to 'stake' for network security. The aim is to use such a combination to enhance the security, stability, and efficiency of automated systems for decentralized exchanges, lending protocols, and automated markets. Furthermore, advancements in computation—such as artificial intelligence, big data, and cloud computing—improve DeFi by enabling the prediction of asset movements, analyzing user behavior, and supporting scalable decentralized applications. Despite rapid advancements, challenges remain, particularly in areas like smart contract vulnerabilities, market volatility, and regulatory compliance. Ongoing interdisciplinary research is focused on addressing these issues through improved composability and integration of mathematics with computation. These innovations promote global participation, transparency, and financial inclusion.
R 7,231.00
Correlated Data Analysis: Modeling, Analytics, and Applications
This book covers recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to cover a broader range of data types than the traditional generalized linear models. The reader is provided with a systematic treatment for the topic of estimating functions, and both generalized estimating equations (GEE) and quadratic inference functions (QIF) are studied as special cases. In addition to the discussions on marginal models and mixed-effects models, this book covers new topics on joint regression analysis based on Gaussian copulas.
R 4,396.06
Introduction to Data Science for Social and Policy Research
Real-world data sets are messy and complicated. Written for students in social science and public management, this authoritative but approachable guide describes all the tools needed to collect data and prepare it for analysis. Offering detailed, step-by-step instructions, it covers collection of many different types of data including web files, APIs, and maps; data cleaning; data formatting; the integration of different sources into a comprehensive data set; and storage using third-party tools to facilitate access and shareability, from Google Docs to GitHub. Assuming no prior knowledge of R and Python, the author introduces programming concepts gradually, using real data sets that provide the reader with practical, functional experience.
R 5,272.10
Causal Analytics for Applied Risk Analysis
Causal analytics methods can revolutionize the use of data to make effective decisions by revealing how different choices affect probabilities of various outcomes. This book presents and illustrates models, algorithms, principles, and software for deriving causal models from data and for using them to optimize decisions with uncertain outcomes. It discusses how to describe and summarize situations; detect changes; evaluate effects of policies or interventions; learn what works best under different conditions; predict values of as-yet unobserved quantities from available data; and identify the most likely explanations for observed outcomes, including surprises and anomalies. The book resents practical techniques for causal modeling and analytics that practitioners can apply to improve understanding of how choices affect probabilities of consequences and, based on this understanding, to recommend choices that are more likely to accomplish their intended objectives.The book begins with a survey of modern analytics methods, focusing mainly on techniques useful for decision, risk, and policy analysis. Chapter 2 introduces free in-browser software, including the Causal Analytics Toolkit (CAT) software, to enable readers to perform the analyses described and to apply modern analytics methods easily to their own data sets. Chapters 3 through 11 show how to apply causal analytics and risk analytics to practical risk analysis challenges, mainly related to public and occupational health risks from pathogens in food or from pollutants in air. Chapters 12 through 15 turn to broader questions of how to improve risk management decision-making by individuals, groups, organizations, institutions, and multi-generation societies with different cultures and norms for cooperation. These chapters examine organizational learning, community resilience, societal risk management, and intergenerational collaboration and justice in managing risks.
R 11,007.31
Cult of Analytics
Cult of Analytics enables professionals to build an analytics driven culture into their business or organization. Marketers will learn how to turn tried and tested tactics into an actionable plan to change their culture to one that uses web analytics on a day to day basis.Through use of the fictitious ACME PLC case, Steve Jackson provides working examples based on real life situations from the various companies he has worked with, such as Nokia, KONE, Rovio, Amazon, Expert, IKEA, Vodafone, and EMC. These examples will give the reader practical techniques for their own business regardless of size or situation making Cult of Analytics a must have for any would-be digital marketer.This new edition has been thoroughly updated, now including examples out of how to get the best from Google analytics, as well as ways to use social media data, big data, tag management and advanced persona segmentation to drive real value in your organisation. It's also been expanded to include exercises and new cases for students and tutors using the book as a text.
R 2,274.10
AI FINANCE: FINANCIAL MANAGEMENT - AI DRIVER® SERIES
AI Finance: Financial Management represents a paradigm shift in financial management education. Rather than training students to compete with AI in analysis, this textbook teaches them to build financial solutions with AI as a collaborator, applying the DRIVER® Framework (Define&Discover, Represent, Implement, Validate, Evolve, Reflect) throughout.The curriculum spans foundational corporate finance concepts—from financial statements and time value of money through capital budgeting, securities valuation, and business valuation—while integrating Python coding, AI-powered problem-solving, and systematic product development.Students build three progressive mini-projects and a capstone innovation project, creating a portfolio of working financial applications rather than theoretical exercises. The textbook is classroom-tested at Purdue University with multiple sections serving 200+ students.This textbook prepares students for careers where AI handles routine analysis by emphasizing survival skills: building financial tools, understanding market dynamics, managing capital allocation, and operating AI systematically. The integration of real-world case studies (Apple, Tesla, Netflix, WeWork), hands-on Python labs, and video-based assessment ensures authentic learning and career readiness for modern finance roles.
R 2,125.00
Mathematical and Statistical Methods for Actuarial Sciences and Finance
The cooperation and contamination between mathematicians, statisticians and econometricians working in actuarial sciences and finance is improving the research on these topics and producing numerous meaningful scientific results. This volume presents new ideas, in the form of four- to six-page papers, presented at the International Conference eMAF2020 – Mathematical and Statistical Methods for Actuarial Sciences and Finance. Due to the now sadly famous COVID-19 pandemic, the conference was held remotely through the Zoom platform offered by the Department of Economics of the Ca’ Foscari University of Venice on September 18, 22 and 25, 2020.eMAF2020 is the ninth edition of an international biennial series of scientific meetings, started in 2004 at the initiative of the Department of Economics and Statistics of the University of Salerno. The effectiveness of this idea has been proven by wide participation in all editions, which have been held in Salerno (2004, 2006, 2010 and 2014), Venice (2008, 2012 and 2020), Paris (2016) and Madrid (2018).This book covers a wide variety of subjects: artificial intelligence and machine learning in finance and insurance, behavioral finance, credit risk methods and models, dynamic optimization in finance, financial data analytics, forecasting dynamics of actuarial and financial phenomena, foreign exchange markets, insurance models, interest rate models, longevity risk, models and methods for financial time series analysis, multivariate techniques for financial markets analysis, pension systems, portfolio selection and management, real-world finance, risk analysis and management, trading systems, and others.This volume is a valuable resource for academics, PhD students, practitioners, professionals and researchers. Moreover, it is also of interest to other readers with quantitative background knowledge.
R 6,361.97
Aligning Business Strategies and Analytics
This book examines issues related to the alignment of business strategies and analytics. Vast amounts of data are being generated, collected, stored, processed, analyzed, distributed and used at an ever-increasing rate by organizations. Simultaneously, managers must rapidly and thoroughly understand the factors driving their business. Business Analytics is an interactive process of analyzing and exploring enterprise data to find valuable insights that can be exploited for competitive advantage. However, to gain this advantage, organizations need to create a sophisticated analytical climate within which strategic decisions are made. As a result, there is a growing awareness that alignment among business strategies, business structures, and analytics are critical to effectively develop and deploy techniques to enhance an organization’s decision-making capability. In the past, the relevance and usefulness of academic research in the area of alignment is often questioned by practitioners, but this book seeks to bridge this gap.Aligning Business Strategies and Analytics: Bridging Between Theory and Practice is comprised of twelve chapters, divided into three sections. The book begins by introducing business analytics and the current gap between academic training and the needs within the business community. Chapters 2 - 5 examines how the use of cognitive computing improves financial advice, how technology is accelerating the growth of the financial advising industry, explores the application of advanced analytics to various facets of the industry and provides the context for analytics in practice. Chapters 6 - 9 offers real-world examples of how project management professionals tackle big-data challenges, explores the application of agile methodologies, discusses the operational benefits that can be gained by implementing real-time, and a case study on human capital analytics. Chapters 10 - 11 reviews the opportunities and potential shortfall and highlights how new media marketing and analytics fostered new insights. Finally the book concludes with a look at how data and analytics are playing a revolutionary role in strategy development in the chemical industry.
R 4,056.49
Data Analytics and Digital Transformation
Understanding the significance of data analytics is paramount for digital transformation but in many organisations they are separate units without fully aligned goals. As organisations are applying digital transformations to be adaptive and agile in a competitive environment, data analytics can play a critical role for their success. This book explores the crossroads between them and how to leverage their connection for improved business outcomes. The need to collaborate and share data is becoming an integral part of digital transformation. This creates new opportunities but also requires well considered and continuously assessed decision making as competitiveness is at stake. This book details approaches, concepts and frameworks, as well as actionable insights and good practices, including combined data management and agile concepts. Critical issues are discussed such as data quality and data governance, as well as compliance, privacy, and ethics. It also offers insights into how both private and public organisations can innovate and keep up with growing data volumes and increasing technological developments in the short, mid and long term. This book will be of direct appeal to global researchers and students across a range of business disciplines, including technology and innovation management, organisational studies and strategic management. It is also relevant for policy makers, regulators and executives of private and public organisations looking to implement successful transformation policies.
R 2,478.15
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.
R 4,396.20
Python for Linguists
Specifically designed for linguists, this book provides an introduction to programming using Python for those with little to no experience of coding. Python is one of the most popular and widely-used programming languages as it's also available for free and runs on any operating system. All examples in the text involve language data and can be adapted or used directly for language research. The text focuses on key language-related issues: searching, text manipulation, text encoding and internet data, providing an excellent resource for language research. More experienced users of Python will also benefit from the advanced chapters on graphical user interfaces and functional programming.
R 4,844.00
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.
R 9,316.15
It's All Analytics!
It's All Analytics! The Foundations of AI, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government (978-0-367-35968-3, 325690)Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially in the last 25 years, there has been an explosion of terms and methods that automate and improve decision-making and operations. One term, "analytics," is an overarching description of a compilation of methodologies. But AI (artificial intelligence), statistics, decision science, and optimization, which have been around for decades, have resurged. Also, things like business intelligence, online analytical processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology and terminology?This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject. The authors include the basics such as algorithms, mental concepts, models, and paradigms in addition to the benefits of machine learning. The book also includes a chapter on data and the various forms of data. The authors wrap up this book with a look at the next frontiers such as applications and designing your environment for success, which segue into the topics of the next two books in the series.
R 3,668.18
Time Series in Economics and Finance
This book presents the principles and methods for the practical analysis and prediction of economic and financial time series. It covers decomposition methods, autocorrelation methods for univariate time series, volatility and duration modeling for financial time series, and multivariate time series methods, such as cointegration and recursive state space modeling. It also includes numerous practical examples to demonstrate the theory using real-world data, as well as exercises at the end of each chapter to aid understanding. This book serves as a reference text for researchers, students and practitioners interested in time series, and can also be used for university courses on econometrics or computational finance.
R 4,161.94
People Analytics
This book addresses the topic of HR analytics focusing especially on demystifying analytics for HR managers, from both statistical and computing points of view. It presents the evolution and the importance of people analytics in today’s workforce management and highlights the motivation for studying business analytics, shows the different levels of analytic maturity and discusses real world success stories of business analytics in action through case studies. The volume provides the readers with a step-by-step guide to analyze HR data and interpret the results for strategy formulation. Each chapter of the book includes a vignette, data-driven exercises, and real-world case studies. The authors also introduce the readers to data visualization software Microsoft Power BI as well as elaborates on how to use Tableau Desktop.This book would be useful to the students of human resource management, organizational behaviour, applied psychology, social work and sociology. It would also serve as very good handbook for practicing business executives helping them to implement data analytics for effective decision making in all kinds of business situations around the world.
R 2,496.80
Analysis, Geometry, and Modeling in Finance
Analysis, Geometry, and Modeling in Finance: Advanced Methods in Option Pricing is the first book that applies advanced analytical and geometrical methods used in physics and mathematics to the financial field. It even obtains new results when only approximate and partial solutions were previously available.Through the problem of option pricing, the author introduces powerful tools and methods, including differential geometry, spectral decomposition, and supersymmetry, and applies these methods to practical problems in finance. He mainly focuses on the calibration and dynamics of implied volatility, which is commonly called smile. The book covers the Black–Scholes, local volatility, and stochastic volatility models, along with the Kolmogorov, Schrödinger, and Bellman–Hamilton–Jacobi equations.Providing both theoretical and numerical results throughout, this book offers new ways of solving financial problems using techniques found in physics and mathematics.
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R 1,221.00
Delivering Data Analytics
The importance of data analytics is well known, but how can you get end users to engage with analytics and business intelligence (BI) when adoption of new technology can be frustratingly slow or may not happen at all? Avoid wasting time on dashboards and reports that no one uses with this practical guide to increasing analytics adoption by focusing on people and process, not technology. Pulling together agile, UX and change management principles, Delivering Data Analytics outlines a step-by-step, technology agnostic process designed to shift the organizational data culture and gain buy-in from users and stakeholders at every stage of the project. This book outlines how to succeed and build trust with stakeholders amid the politics, ambiguity and lack of engagement in business. With case studies, templates, checklists and scripts based on the author's considerable experience in analytics and data visualisation, this book covers the full cycle from requirements gathering and data assessment to training and launch. Ensure lasting adoption, trust and, most importantly, actionable business value with this roadmap to creating user-centric analytics projects.
R 1,715.68
Data Analysis with Machine Learning for Psychologists
The power of data drives the digital economy of the 21st century. It has been argued that data is as vital a resource as oil was during the industrial revolution. An upward trend in the number of research publications using machine learning in some of the top journals in combination with an increasing number of academic recruiters within psychology asking for Python knowledge from applicants indicates a growing demand for these skills in the market.While there are plenty of books covering data science, rarely, if ever, books in the market address the need of social science students with no computer science background. They are typically written by engineers or computer scientists for people of their discipline. As a result, often such books are filled with technical jargon and examples irrelevant to psychological studies or projects. In contrast, this book was written by a psychologist in a simple, easy-to-understand way that is brief and accessible. The aim for this book was to make the learning experience on this topic as smooth as possible for psychology students/researchers with no background in programming or data science.Completing this book will also open up an enormous amount of possibilities for quantitative researchers in psychological science, as it will enable them to explore newer types of research questions.
R 3,928.76
Simplify Big Data Analytics with Amazon EMR
Design scalable big data solutions using Hadoop, Spark, and AWS cloud native servicesKey FeaturesBuild data pipelines that require distributed processing capabilities on a large volume of dataDiscover the security features of EMR such as data protection and granular permission managementExplore best practices and optimization techniques for building data analytics solutions in Amazon EMRBook DescriptionAmazon EMR, formerly Amazon Elastic MapReduce, provides a managed Hadoop cluster in Amazon Web Services (AWS) that you can use to implement batch or streaming data pipelines. By gaining expertise in Amazon EMR, you can design and implement data analytics pipelines with persistent or transient EMR clusters in AWS. This book is a practical guide to Amazon EMR for building data pipelines. You'll start by understanding the Amazon EMR architecture, cluster nodes, features, and deployment options, along with their pricing. Next, the book covers the various big data applications that EMR supports. You'll then focus on the advanced configuration of EMR applications, hardware, networking, security, troubleshooting, logging, and the different SDKs and APIs it provides. Later chapters will show you how to implement common Amazon EMR use cases, including batch ETL with Spark, real-time streaming with Spark Streaming, and handling UPSERT in S3 Data Lake with Apache Hudi. Finally, you'll orchestrate your EMR jobs and strategize on-premises Hadoop cluster migration to EMR. In addition to this, you'll explore best practices and cost optimization techniques while implementing your data analytics pipeline in EMR. By the end of this book, you'll be able to build and deploy Hadoop- or Spark-based apps on Amazon EMR and also migrate your existing on-premises Hadoop workloads to AWS.What you will learnExplore Amazon EMR features, architecture, Hadoop interfaces, and EMR StudioConfigure, deploy, and orchestrate Hadoop or Spark jobs in productionImplement the security, data governance, and monitoring capabilities of EMRBuild applications for batch and real-time streaming data analytics solutionsPerform interactive development with a persistent EMR cluster and NotebookOrchestrate an EMR Spark job using AWS Step Functions and Apache AirflowWho this book is forThis book is for data engineers, data analysts, data scientists, and solution architects who are interested in building data analytics solutions with the Hadoop ecosystem services and Amazon EMR. Prior experience in either Python programming, Scala, or the Java programming language and a basic understanding of Hadoop and AWS will help you make the most out of this book.
R 1,661.00
Advanced Modelling in Finance using Excel and VBA
This volume demonstrates that Excel and VBA can play an important role in the explanation and implementation of numerical methods across finance. The text provides a comprehensive look at equities, options on equities and options on bonds from the early 1950s to the late 1990s. It adopts a step-by-step approach to understanding the more sophisticated aspects of Excel macros and VBA programming, showing how these programming techniques can be used to model and manipulate financial data, as applied to equities, bonds and options. Thework is useful for financial practitioners who need to develop their financial modelling skill sets as there is an increase in the need to analyse and develop ever more complex "what if" scenarios.;It specifically applies Excel and VBA to the financial markets and is packaged with a CD containing the software from the examples throughout the book.
R 4,657.00
AI-Powered Business Analytics
This book offers an integrated framework that actively leverages AI-driven analytics, empowering businesses to transform raw data into strategic insights, actionable intelligence, and informed decision-making. It expertly covers core AI techniques and presents real-world case studies from diverse industries. Furthermore, the book provides step-by-step methodologies for constructing effective AI analytics solutions, guiding readers through the practical application of these powerful tools.The author empowers the reader to adopt strategies that actively increase predictive accuracy, automate complex analyses, and improve business processes, ultimately leading to better outcomes. The clear and structured chapters facilitate understanding, starting with the basics and progressing to advanced machine learning algorithms, equipping readers with hands-on skills for future practical applications. Key topics covered include neural networks, deep learning fundamentals, and convolutional and recurrent networks. The book illustrates this essential knowledge with real-world applications for solving complex business problems. Furthermore, it delves into Natural Language Processing (NLP) methods, sentiment analysis, text classification, and document analytics within the context of business cases like customer feedback analysis and market intelligence. By actively engaging with the material, the reader will learn and understand the tools for both predictive and prescriptive analytics, gaining valuable competitive insights and enhanced strategic decision-making capabilities. This comprehensive approach ensures readers can confidently apply AI to drive tangible business improvements.This book is targeted at upper-level undergraduates and graduates in Business Analytics, Data Science, AI in Business, MBA in Analytics, and Digital Transformation. Industry professionals like business analysts, data analysts, data scientists, AI specialists, and decision-makers seeking strategic AI analytics implementation will also benefit.
R 2,674.00
R 8,975.00
AI FINANCE: FINANCIAL MANAGEMENT - AI DRIVER® SERIES
AI Finance: Financial Management represents a paradigm shift in financial management education. Rather than training students to compete with AI in analysis, this textbook teaches them to build financial solutions with AI as a collaborator, applying the DRIVER® Framework (Define&Discover, Represent, Implement, Validate, Evolve, Reflect) throughout.The curriculum spans foundational corporate finance concepts—from financial statements and time value of money through capital budgeting, securities valuation, and business valuation—while integrating Python coding, AI-powered problem-solving, and systematic product development.Students build three progressive mini-projects and a capstone innovation project, creating a portfolio of working financial applications rather than theoretical exercises. The textbook is classroom-tested at Purdue University with multiple sections serving 200+ students.This textbook prepares students for careers where AI handles routine analysis by emphasizing survival skills: building financial tools, understanding market dynamics, managing capital allocation, and operating AI systematically. The integration of real-world case studies (Apple, Tesla, Netflix, WeWork), hands-on Python labs, and video-based assessment ensures authentic learning and career readiness for modern finance roles.
R 5,056.00
Transforming the Financial Landscape With ICTs
In modern finance, academic scholars face a pressing dilemma - how to grapple with the sweeping influence of Information and Communication Technology (ICT). As ICT continues to transform financial practices, transactions, and data analysis, scholars are confronted with the daunting challenge of comprehending and navigating this rapidly changing terrain. Moreover, the disruptive potential of ICT introduces a range of intricate issues, including cybersecurity vulnerabilities, ethical quandaries, and privacy concerns. In the absence of a comprehensive guide, scholars find themselves adrift, searching for direction amidst the tumultuous waves of the digital revolution. Transforming the Financial Landscape With ICTs is a beacon of knowledge amid the turbulence of ICT-driven financial transformation. This meticulously curated book presents a vast array of research papers and case studies that delve deep into the multifaceted impact of ICT on the finance sector. It not only illuminates the path forward but also underscores the positive potential of embracing ICT as a catalyst for progress, championing efficiency, transparency, and inclusivity within finance. This book offers a comprehensive exploration of the digital financial landscape, covering a broad spectrum of topics, from online banking to blockchain, AI-powered chatbots to big data analytics. It provides a holistic view of how ICT is reshaping finance. Bridging the gap between theory and practice provides actionable insights that are invaluable for both scholars and practitioners, facilitating a deeper understanding of ICT's real-world applications. Beyond mere exposition, this book critically examines the challenges and risks associated with the ICT revolution in finance, addressing issues such as cybersecurity, privacy, and ethics, thereby equipping scholars with a well-rounded perspective. Transforming the Financial Landscape With ICTs is more than a book; it is a roadmap for academic scholars seeking to navigate the uncharted territory of ICT's impact on finance. By offering both theoretical foundations and practical guidance, it empowers scholars to confront the challenges and seize the opportunities presented by the digital era, ensuring they remain at the forefront of financial research and innovation.
R 8,319.25
Analytics at Work
Most companies have massive amounts of data at their disposal, yet fail to utilize it in any meaningful way. But a powerful new business tool - analytics - is enabling many firms to aggressively leverage their data in key business decisions and processes, with impressive results. In their previous book, Competing on Analytics, Thomas Davenport and Jeanne Harris showed how pioneering firms were building their entire strategies around their analytical capabilities. Rather than "going with the gut" when pricing products, maintaining inventory, or hiring talent, managers in these firms use data, analysis, and systematic reasoning to make decisions that improve efficiency, risk-management, and profits. Now, in Analytics at Work, Davenport, Harris, and coauthor Robert Morison reveal how any manager can effectively deploy analytics in day-to-day operations--one business decision at a time. They show how many types of analytical tools, from statistical analysis to qualitative measures like systematic behavior coding, can improve decisions about everything from what new product offering might interest customers to whether marketing dollars are being most effectively deployed.Based on all-new research and illustrated with examples from companies including Humana, Best Buy, Progressive Insurance, and Hotels.com, this implementation-focused guide outlines the five-step DELTA model for deploying and succeeding with analytical initiatives. You'll learn how to: * Use data more effectively and glean valuable analytical insights * Manage and coordinate data, people, and technology at an enterprise level * Understand and support what analytical leaders do * Evaluate and choose realistic targets for analytical activity * Recruit, hire, and manage analysts Combining the science of quantitative analysis with the art of sound reasoning, Analytics at Work provides a road map and tools for unleashing the potential buried in your company's data.
R 798.00
Python for Signal Processing
This book covers the fundamental concepts in signal processing illustrated with Python code and made available via IPython Notebooks, which are live, interactive, browser-based documents that allow one to change parameters, redraw plots, and tinker with the ideas presented in the text. Everything in the text is computable in this format and thereby invites readers to “experiment and learn” as they read. The book focuses on the core, fundamental principles of signal processing. The code corresponding to this book uses the core functionality of the scientific Python toolchain that should remain unchanged into the foreseeable future. For those looking to migrate their signal processing codes to Python, this book illustrates the key signal and plotting modules that can ease this transition. For those already comfortable with the scientific Python toolchain, this book illustrates the fundamental concepts in signal processing and provides a gateway to further signal processing concepts.
R 3,205.21
Handbook of Blockchain Analytics
This handbook delves into the multifaceted domain of blockchain technology and its applications, offering insights across technological, economic, and practical dimensions. It is divided into several parts, reflecting analysis techniques specific to different fields of application, including digital finance, security and supply chains, among others. The book begins with a foundational exploration of blockchain technology, providing a comprehensive understanding of its core mechanisms. The analysis extends to cutting-edge topics such as Non-Fungible Tokens (NFTs) and visualization technologies, which reveal the dynamic interplay between digital assets and technological innovation. Cryptocurrency liquidity and market dynamics are also examined, employing principles like Metcalfe's Law and Log-Periodic Power Laws to unravel complex market behaviors. Empirical studies shed light on volatility forecasting and the impact of sudden market jumps, enriching the predictive frameworks for cryptocurrency trading. In the realm of blockchain analytics, advanced methodologies like process mining are applied to uncover patterns in blockchain applications, while network analytics provide tools for fraud detection and heuristic evaluation. Decision-making in blockchain transactions is explored through the lens of mempool dynamics, providing practical insights for optimizing blockchain operations. The narrative extends to business applications, highlighting strategies for blockchain-driven development and the legal and financial implications of Distributed Ledger Technology (DLT). Blockchain's transformative potential in healthcare is showcased, alongside a survey of federated learning frameworks that leverage blockchain for secure and decentralized machine learning. Security remains a pivotal theme, with a focus on the role of blockchain in cybersecurity and compliance functions. The investigation addresses decentralized finance (DeFi) vulnerabilities through case studies on attacks, offering lessons to enhance system robustness. Finally, the text envisions a future where decentralized models democratize foundational technologies, and blockchain facilitates innovations in maritime supply chains, driving efficiency and transparency in global logistics. This compilation serves as a gateway to understanding the theoretical and practical dimensions of blockchain, emphasizing its evolving role in reshaping industries and economic systems.
R 11,532.40
Statistical Methods for Financial Engineering
While many financial engineering books are available, the statistical aspects behind the implementation of stochastic models used in the field are often overlooked or restricted to a few well-known cases. Statistical Methods for Financial Engineering guides current and future practitioners on implementing the most useful stochastic models used in financial engineering. After introducing properties of univariate and multivariate models for asset dynamics as well as estimation techniques, the book discusses limits of the Black-Scholes model, statistical tests to verify some of its assumptions, and the challenges of dynamic hedging in discrete time. It then covers the estimation of risk and performance measures, the foundations of spot interest rate modeling, Lévy processes and their financial applications, the properties and parameter estimation of GARCH models, and the importance of dependence models in hedge fund replication and other applications. It concludes with the topic of filtering and its financial applications.This self-contained book offers a basic presentation of stochastic models and addresses issues related to their implementation in the financial industry. Each chapter introduces powerful and practical statistical tools necessary to implement the models. The author not only shows how to estimate parameters efficiently, but he also demonstrates, whenever possible, how to test the validity of the proposed models. Throughout the text, examples using MATLAB® illustrate the application of the techniques to solve real-world financial problems. MATLAB and R programs are available on the author’s website.
R 2,528.00
Causal Analytics for Applied Risk Analysis
Causal analytics methods can revolutionize the use of data to make effective decisions by revealing how different choices affect probabilities of various outcomes. This book presents and illustrates models, algorithms, principles, and software for deriving causal models from data and for using them to optimize decisions with uncertain outcomes. It discusses how to describe and summarize situations; detect changes; evaluate effects of policies or interventions; learn what works best under different conditions; predict values of as-yet unobserved quantities from available data; and identify the most likely explanations for observed outcomes, including surprises and anomalies. The book resents practical techniques for causal modeling and analytics that practitioners can apply to improve understanding of how choices affect probabilities of consequences and, based on this understanding, to recommend choices that are more likely to accomplish their intended objectives.The book begins with a survey of modern analytics methods, focusing mainly on techniques useful for decision, risk, and policy analysis. Chapter 2 introduces free in-browser software, including the Causal Analytics Toolkit (CAT) software, to enable readers to perform the analyses described and to apply modern analytics methods easily to their own data sets. Chapters 3 through 11 show how to apply causal analytics and risk analytics to practical risk analysis challenges, mainly related to public and occupational health risks from pathogens in food or from pollutants in air. Chapters 12 through 15 turn to broader questions of how to improve risk management decision-making by individuals, groups, organizations, institutions, and multi-generation societies with different cultures and norms for cooperation. These chapters examine organizational learning, community resilience, societal risk management, and intergenerational collaboration and justice in managing risks.
R 10,311.20
