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Data Analytics, Computational Statistics, and Operations Research for Engineers
With the rapidly advancing fields of Data Analytics and Computational Statistics, it’s important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information. Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and machine learning.
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Practical Web Scraping for Data Science
This book provides a complete and modern guide to web scraping, using Python as the programming language, without glossing over important details or best practices. Written with a data science audience in mind, the book explores both scraping and the larger context of web technologies in which it operates, to ensure full understanding. The authors recommend web scraping as a powerful tool for any data scientist’s arsenal, as many data science projects start by obtaining an appropriate data set.Starting with a brief overview on scraping and real-life use cases, the authors explore the core concepts of HTTP, HTML, and CSS to provide a solid foundation. Along with a quick Python primer, they cover Selenium for JavaScript-heavy sites, and web crawling in detail. The book finishes with a recap of best practices and a collection of examples that bring together everything you've learned and illustrate various data science use cases. What You'll LearnLeverage well-established best practices and commonly-used Python packagesHandle today's web, including JavaScript, cookies, and common web scraping mitigation techniquesUnderstand the managerial and legal concerns regarding web scrapingWho This Book is ForA data science oriented audience that is probably already familiar with Python or another programming language or analytical toolkit (R, SAS, SPSS, etc). Students or instructors in university courses may also benefit. Readers unfamiliar with Python will appreciate a quick Python primer in chapter 1 to catch up with the basics and provide pointers to other guides as well.
R 1,986.30
Big Data Analytics with Spark
Big Data Analytics with Spark is a step-by-step guide for learning Spark, which is an open-source fast and general-purpose cluster computing framework for large-scale data analysis. You will learn how to use Spark for different types of big data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. In addition, this book will help you become a much sought-after Spark expert.Spark is one of the hottest Big Data technologies. The amount of data generated today by devices, applications and users is exploding. Therefore, there is a critical need for tools that can analyze large-scale data and unlock value from it. Spark is a powerful technology that meets that need. You can, for example, use Spark to perform low latency computations through the use of efficient caching and iterative algorithms; leverage the features of its shell for easy and interactive Data analysis; employ its fast batch processing and low latency features to process your real time data streams and so on. As a result, adoption of Spark is rapidly growing and is replacing Hadoop MapReduce as the technology of choice for big data analytics. This book provides an introduction to Spark and related big-data technologies. It covers Spark core and its add-on libraries, including Spark SQL, Spark Streaming, GraphX, and MLlib. Big Data Analytics with Spark is therefore written for busy professionals who prefer learning a new technology from a consolidated source instead of spending countless hours on the Internet trying to pick bits and pieces from different sources. The book also provides a chapter on Scala, the hottest functional programming language, and the program that underlies Spark. You’ll learn the basics of functional programming in Scala, so that you can write Spark applications in it.What's more, Big Data Analytics with Spark provides an introduction to other big data technologies that are commonly used along with Spark, like Hive, Avro, Kafka and so on. So the book is self-sufficient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to know is programming in any language.There is a critical shortage of people with big data expertise, so companies are willing to pay top dollar for people with skills in areas like Spark and Scala. So reading this book and absorbing its principles will provide a boost—possibly a big boost—to your career.
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Scientific Programming and Data Visualization in Mathematical Modeling
This book is a practical guide introducing the MATLAB and Python programming languages and their capabilities. It provides real-world examples and exercises to demonstrate Python's practical applications in contrast to MATLAB's.The book follows a sequential learning approach, starting with the coverage of mathematical concepts using MATLAB, and then transitioning to Python. This approach aims to bridge the gap in developing numerical programs in both languages, ensuring a smooth learning experience.Additionally, the book covers Symbolic computation techniques required in various analytic mathematical models using both MATLAB and Python.This book is ideal for mathematics students, researchers who need to incorporate mathematical programming in allied disciplines, data science specialists interested in MATLAB and Python numerical techniques.The main highlights of the proposed work are:• How to solve ordinary differential equations via MATLAB and Python• Dealing with a variety of partial differential equations through MATLAB and Python• Symbolic computation via MATLAB and Python• Data visualization through MATLAB and Python in a comparative mode.
R 2,143.00
Practical Machine Learning for Streaming Data with Python
Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights. You'll start with an introduction to streaming data, the various challenges associated with it, some of its real-world business applications, and various windowing techniques. You'll then examine incremental and online learning algorithms, and the concept of model evaluation with streaming data and get introduced to the Scikit-Multiflow framework in Python. This is followed by a review of the various change detection/concept drift detection algorithms and the implementation of various datasets using Scikit-Multiflow.Introduction to the various supervised and unsupervised algorithms for streaming data, and their implementation on various datasets using Python are also covered. The book concludes by briefly covering other open-source tools available for streaming data such as Spark, MOA (Massive Online Analysis), Kafka, and more.What You'll LearnUnderstand machine learning with streaming data conceptsReview incremental and online learningDevelop models for detecting concept driftExplore techniques for classification, regression, and ensemble learning in streaming data contextsApply best practices for debugging and validating machine learning models in streaming data contextGet introduced to other open-source frameworks for handling streaming data.Who This Book Is ForMachine learning engineers and data science professionals
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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.
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Statistics and Data Visualization in Climate Science with R and Python
A comprehensive overview of essential statistical concepts, useful statistical methods, data visualization, and modern computing tools for the climate sciences and many others such as geography and environmental engineering. It is an invaluable reference for students and researchers in climatology and its connected fields who wish to learn data science, statistics, R and Python programming. The examples and exercises in the book empower readers to work on real climate data from station observations, remote sensing and simulated results. For example, students can use R or Python code to read and plot the global warming data and the global precipitation data in netCDF, csv, txt, or JSON; and compute and interpret empirical orthogonal functions. The book's computer code and real-world data allow readers to fully utilize the modern computing technology and updated datasets. Online supplementary resources include R code and Python code, data files, figure files, tutorials, slides and sample syllabi.
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R 458.00
Risk Analytics
The World Economic Forum in 2022 surveyed 1000 experts and leaders who indicated their risk perception that the earth conditions for humans are a main concern in the next 10 years. This means environmental risks are a priority to study in a formal way. At the same time, innovation risks are present in the mind of leaders, new knowledge brings new risk, and the adaptation and adoption of risk knowledge is required to understand more what causes and effects can have technological risks. These opportunities require not only adopting new ways for managing and controlling emerging processes for society and business, but also to adapt organizations to changes and manage new risks.Risk Analytics: Data-Driven Decisions under Uncertainty introduces a way to analyze and design a risk analytics system (RAS) that integrates multiple approaches to risk analytics to deal with a diverse type of data and problems. A risk analytics system is a hybrid system where human and Artificial Intelligence interact with a data gathering and selection process that uses multiple sources to the delivery of guidelines to make decisions that include humans and machines. The RAS system is an integration of components such as data architecture with diverse data, and a risk analytics process and modeling process to obtain knowledge and then to determine actions through the new knowledge that was obtained. The use of data analytics is not only connected to risk modeling and its implementation but also to the development of the actionable knowledge that can be represented by text in documents to define and share explicit knowledge and guidelines in the organization for strategy implementation.The book moves from a review of data to the concepts of a RAS. It reviews RAS system components required to support the creation of competitive advantage in organizations through risk analytics. Written for executives, analytics professionals, risk management professionals, strategy professionals, and postgraduate students, the book shows a way to implement the analytics process to develop a risk management practice that creates adaptive competitive advantage under uncertainty.
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Implementing Microsoft Dynamics 365 for Finance and Operations
Harness the power of Dynamics 365 Operations and discover all you need to implement itAbout This Book• Master all the necessary tools and resources to evaluate Dynamics 365 for Operations, implement it, and proactively maintain it.• Troubleshoot your problems effectively with your Dynamics 365 partner• Learn about architecture, deployment choices, integration, configuration and data migration, development, testing, reporting and BI, support, upgrading, and more. Who This Book Is ForThis book is for technology leaders, project managers solution architects, and consultants who are planning to implement, are in the process of implementing, or are currently upgrading to Dynamics 365 for Operations. This book will help you effectively learn and implement Dynamics 365 for Operations.What You Will Learn• Learn about Microsoft Dynamics 365, it's offerings, plans and details of Finance and Operations, Enterprise edition• Understand the methodology and the tool, architecture, and deployment options• Effectively plan and manage configurations and data migration, functional design, and technical design• Understand integration frameworks, development concepts, best practices, and recommendations while developing new solutions• Learn how to leverage intelligence and analytics through Power BI, machine learning, IOT, and Cortana intelligence• Master testing, training, going live, upgrading, and how to get support during and after the implementationIn DetailMicrosoft Dynamics 365 for Finance and Operations, Enterprise edition, is a modern, cloud-first, mobile-first, ERP solution suitable for medium and large enterprise customers.This book will guide you through the entire life cycle of a implementation, helping you avoid common pitfalls while increasing your efficiency and effectiveness at every stage of the project.Starting with the foundations, the book introduces the Microsoft Dynamics 365 offerings, plans, and products. You will be taken through the various methodologies, architectures, and deployments so you can select, implement, and maintain Microsoft Dynamics 365 for Finance and Operations, Enterprise edition. You will delve in-depth into the various phases of implementation: project management, analysis, configuration, data migration, design, development, using Power BI, machine learning, Cortana analytics for intelligence, testing, training, and finally deployment, support cycles, and upgrading. This book focuses on providing you with information about the product and the various concepts and tools, along with real-life examples from the field and guidance that will empower you to execute and implement Dynamics 365 for Finance and Operations, Enterprise edition.Style and approachThis book is a step-by-step guide focusing on implementing Dynamics 365 Operations solutions for your organization.
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Respatialising Finance
RESPATIALISING FINANCE ‘In Respatialising Finance Sarah Hall uses the internationalisation of the Chinese Renminbi (RMB) to work through a sympathetic conceptual and empirical critique of prevailing analyses of International Financial Centres (IFCs). Her conceptual (re)framing stresses the politics, institutions and economics of IFCs and will be essential reading for all social scientists interested in the dynamism of contemporary finance and financial centres.’Professor Jane Pollard, Centre for Urban and Regional Development Studies (CURDS), Newcastle University, UK‘Through detailed study of Chinese RMB internationalisation and combining analytical insights from economic geography, sociology, and international political economy, Sarah Hall shows why offshore networks anchored in territories such as the City of London are both core to global monetary and financial landscapes, and provide a key terrain for state power and politics.’Professor Paul Langley, Department of Geography, Durham University, UKRespatialising Finance is one of the first detailed empirical studies of how and why London became the leading western financial centre within the wider Chinese economic and political project of internationalising its currency, the renminbi (RMB). This in-depth volume examines how political authorities in both London and Beijing identified the potential value of London’s international financial centre in facilitating and legitimising RMB internationalisation, and how they sought to operationalise this potential through a range of market-making activities.The text features original data from on-the-ground research in London and Beijing conducted with financial and legal professionals working in RMB markets and offers an original theoretical approach that brings economic geography into closer dialogue with international political economy. Recent work on territory illustrates how financial centres are not simply containers and facilitators of global financial flows – rather they serve as territorial fixes within the dynamic and crisis-prone nature of global finance.
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Understanding the Role of Business Analytics
This book encompasses empirical evidences to understand the application of data analytical techniques in emerging contexts. Varied studies relating to manufacturing and services sectors including healthcare, banking, information technology, power, education sector etc. stresses upon the systematic approach followed in applying the data analytical techniques; and also analyses how these techniques are effective in decision-making in different contexts. Especially, the application of regression modeling, financial modelling, multi-group modeling, cluster analysis, and sentiment analysis will help the readers in understanding critical business scenarios in the best possible way, and which later can help them in arriving at best solution for the business related problems. The individual chapters will help the readers in understanding the role of specific data analytic tools and techniques in resolving business operational issues experienced in manufacturing and service organisations in India and in developing countries. The book offers a relevant resource that will help readers in the application and interpretation of data analytical statistical practices relating to emerging issues like customer experience, marketing capability, quality of manufactured products, strategic orientation, high-performance human resource policy, employee resilience, financial resources, etc. This book will be of interest to a professional audience that include practitioners, policy makers, NGOs, managers and employees as well as academicians, researchers and students.
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R 384.72
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.
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Financial Technology
This book constitutes the proceedings of the 7th International Conference on Financial Technology, ICFT 2026, held in Istanbul, Turkiye during August, 19-21, 2026.The 34 full papers presented in this volume were carefully reviewed and selected from 85 submissions. These papers are organized in the topical sections as follows: AI, Machine Learning & Intelligent Financial Analytics; Blockchain, Cryptocurrency, and Digital Assets; AI-Driven Risk Management and Corporate Finance; Digital Finance, Economic Growth, and Inclusion.
R 3,910.55
Data Science and Machine Learning
Praise for the first edition:“In nine succinct but information-packed chapters, the authors provide a logically structured and robust introduction to the mathematical and statistical methods underpinning the still-evolving field of AI and data science.”- Joacim Rocklöv and Albert A. Gayle, International Journal of Epidemiology, Volume 49, Issue 6“This book organizes the algorithms clearly and cleverly. The way the Python code was written follows the algorithm closely—very useful for readers who wish to understand the rationale and flow of the background knowledge.”- Yin-Ju Lai and Chuhsing Kate Hsiao, Biometrics, Volume 77, Issue 4The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.New in the Second EditionThis expanded edition provides updates across key areas of statistical learning: Monte Carlo Methods: A new section introducing regenerative rejection sampling - a simpler alternative to MCMC. Unsupervised Learning: Inclusion of two multidimensional diffusion kernel density estimators, as well as the bandwidth perturbation matching method for the optimal data-driven bandwidth selection. Regression: New automatic bandwidth selection for local linear regression. Feature Selection and Shrinkage: A new chapter introducing the klimax method for model selection in high-dimensions. Reinforcement Learning: A new chapter on contemporary topics such as policy iteration, temporal difference learning, and policy gradient methods, all complete with Python code. Appendices: Expanded treatment of linear algebra, functional analysis, and optimization that includes the coordinate-descent method and the novel Majorization–Minimization method for constrained optimization.Key Features:Focuses on mathematical understanding.Presentation is self-contained, accessible, and comprehensive.Extensive list of exercises and worked-out examples.Many concrete algorithms with Python code.Full color throughout and extensive indexing.A single-counter consecutive numbering of all theorems, definitions, equations, etc., for easier text searches.
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R 1,049.93
Mining Data for Financial Applications
This book constitutes revised selected papers from the 4th Workshop on Mining Data for Financial Applications, MIDAS 2019, held in conjunction with ECML PKDD 2019, in Würzburg, Germany, in September 2019. The 8 full and 3 short papers presented in this volume were carefully reviewed and selected from 16 submissions. They deal with challenges, potentialities, and applications of leveraging data-mining tasks regarding problems in the financial domain.
R 1,531.09
Blockchain Data Analytics For Dummies
Get ahead of the curve—learn about big data on the blockchain Blockchain came to prominence as the disruptive technology that made cryptocurrencies work. Now, data pros are using blockchain technology for faster real-time analysis, better data security, and more accurate predictions. Blockchain Data Analytics For Dummies is your quick-start guide to harnessing the potential of blockchain. Inside this book, technologists, executives, and data managers will find information and inspiration to adopt blockchain as a big data tool. Blockchain expert Michael G. Solomon shares his insight on what the blockchain is and how this new tech is poised to disrupt data. Set your organization on the cutting edge of analytics, before your competitors get there! Learn how blockchain technologies work and how they can integrate with big dataDiscover the power and potential of blockchain analyticsEstablish data models and quickly mine for insights and resultsCreate data visualizations from blockchain analysis Discover how blockchains are disrupting the data world with this exciting title in the trusted For Dummies line!
R 798.00
Media Analytics
This textbook takes a case study approach to media and audience analytics. Realizing the best way to understand analytics in the digital age is to practice it, the authors have created a collection of cases using datasets that present real and hypothetical scenarios for students to work through.Media Analytics introduces the key principles of media economics and management. It outlines how to interpret and present results, the principles of data visualization and storytelling, and the basics of research design and sampling. Although shifting technology makes measurement and analytics a dynamic space, this book takes an evergreen, conceptual approach, reminding students to focus on the principles and foundations that will remain constant.Aimed at upper-level students in the fast-growing area of media analytics in a cross-platform world, students using this text will learn how to find the stories in the data and how to present those stories in an engaging way to others.Instructor and Student Resources include an Instructor’s Manual, discussion questions, short exercises, and links to additional resources. They are available online at www.routledge.com/cw/hollifield.
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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.
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Quantitative Finance
A graduate-level, mathematically rigorous introduction to the tools, methods, and approaches used in contemporary quantitative financeThis book offers a theory-oriented introduction to investments, asset pricing, and derivatives. Designed for a quantitative master’s program in finance, it is grounded by what works in the classroom. Presenting its topics in a unified, self-contained framework, the book is specifically appropriate for courses in asset pricing and derivatives pricing but may also be used for courses in investments, asset management, and portfolio management. Students will learn how to make decisions under uncertainty and over time, how to choose an investment portfolio, and how to characterize the prices and returns of financial assets in equity, bond, and derivative markets. The book focuses on a number of classical models and theories in quantitative finance and covers selected advanced and newer topics in its final section. Proofs and in-depth theoretical results within quantitative finance appear throughout the book along with examples and end-of-chapter exercises to facilitate and support the learning process.Part I covers the capital asset pricing model, the Lucas model, the static Arrow-Debreu model, consumption-based asset pricing, and the arbitrage pricing theory, and introduces preliminary theories of decision-making and portfolio choicePart II covers no-arbitrage theory, with applications to derivatives and bond markets, beginning with a static economy and then gradually moving to the continuous-time setting; it includes the advanced mathematical tools needed for continuous-time financePart III covers selected advanced and newer topics, including equilibrium models in continuous time, the variance gamma option pricing model, and the Ross recovery theoremAn appendix presents mathematical concepts and results from set theory, topology, linear algebra, matrix theory, and analysis
R 2,651.00
Applied Linear Regression for Business Analytics with R
Applied Linear Regression for Business Analytics with R introduces regression analysis to business students using the R programming language with a focus on illustrating and solving real-time, topical problems. Specifically, this book presents modern and relevant case studies from the business world, along with clear and concise explanations of the theory, intuition, hands-on examples, and the coding required to employ regression modeling. Each chapter includes the mathematical formulation and details of regression analysis and provides in-depth practical analysis using the R programming language.
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Advanced Analytics with R and Tableau
Leverage the power of advanced analytics and predictive modeling in Tableau using the statistical powers of RAbout This Book• A comprehensive guide that will bring out the creativity in you to visualize the results of complex calculations using Tableau and R• Combine Tableau analytics and visualization with the power of R using this step-by-step guide• Wondering how R can be used with Tableau? This book is your one-stop solution.Who This Book Is ForThis book will appeal to Tableau users who want to go beyond the Tableau interface and deploy the full potential of Tableau, by using R to perform advanced analytics with Tableau.A basic familiarity with R is useful but not compulsory, as the book will start off with concrete examples of R and will move quickly into more advanced spheres of analytics using online data sources to support hands-on learning. Those R developers who want to integrate R in Tableau will also benefit from this book.What You Will Learn• Integrate Tableau's analytics with the industry-standard, statistical prowess of R.• Make R function calls in Tableau, and visualize R functions with Tableau using RServe.• Use the CRISP-DM methodology to create a roadmap for analytics investigations.• Implement various supervised and unsupervised learning algorithms in R to return values to Tableau.• Make quick, cogent, and data-driven decisions for your business using advanced analytical techniques such as forecasting, predictions, association rules, clustering, classification, and other advanced Tableau/R calculated field functions.In DetailTableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation.Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics.By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.Style and approachTableau (uniquely) offers excellent visualization combined with advanced analytics; R is at the pinnacle of statistical computational languages. When you want to move from one view of data to another, backed up by complex computations, the combination of R and Tableau makes the perfect solution. This example-rich guide will teach you how to combine these two to perform advanced ana
R 1,498.00
Pricing Analytics
The theme of this book is simple. The price – the number someone puts on a product to help consumers decide to buy that product – comes from data. Specifically, itcomes from statistically modeling the data.This book gives the reader the statistical modeling tools needed to get the number to put on a product. But statistical modeling is not done in a vacuum. Economic and statistical principles and theory conjointly provide the background and framework for the models. Therefore, this book emphasizes two interlocking components of modeling: economic theory and statistical principles.The economic theory component is sufficient to provide understanding of the basic principles for pricing, especially about elasticities, which measure the effects of pricing on key business metrics. Elasticity estimation is the goal of statistical modeling, so attention is paid to the concept and implications of elasticities.The statistical modeling component is advanced and detailed covering choice (conjoint, discrete choice, MaxDiff) and sales data modeling. Experimental design principles, model estimation approaches, and analysis methods are discussed and developed for choice models. Regression fundamentals have been developed for sales model specification and estimation and expanded for latent class analysis.
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Digital Analytics for Marketing
This second edition of Digital Analytics for Marketing provides students with a comprehensive overview of the tools needed to measure digital activity and implement best practices when using data to inform marketing strategy. It is the first text of its kind to introduce students to analytics platforms from a practical marketing perspective.Demonstrating how to integrate large amounts of data from web, digital, social, and search platforms, this helpful guide offers actionable insights into data analysis, explaining how to "connect the dots" and "humanize" information to make effective marketing decisions. The authors cover timely topics, such as social media, web analytics, marketing analytics challenges, and dashboards, helping students to make sense of business measurement challenges, extract insights, and take effective actions. The book’s experiential approach, combined with chapter objectives, summaries, and review questions, will engage readers, deepening their learning by helping them to think outside the box.Filled with engaging, interactive exercises and interesting insights from industry experts, this book will appeal to undergraduate and postgraduate students of digital marketing, online marketing, and analytics.Online support materials for this book include an instructor’s manual, test bank, and PowerPoint slides.
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Learning Modern C++ for Finance
A lot of financial modeling has gravitated toward Python, R, and VBA, but many developers hit a wall with these languages when it comes to performance. This practical book demonstrates why C++ is still one of the dominant production-quality languages for financial applications and systems. Many programmers believe that C++ is too difficult to learn. Author Daniel Hanson demonstrates that this is no longer the case.Financial programmers coming from Python or another interpreted language will discover how to leverage C++ abstractions that enable safer and quicker implementation of financial models. You'll also explore how popular open source libraries provide additional weapons for attacking mathematical problems. C++ programmers unfamiliar with financial applications will also benefit from this handy guide.Learn C++ basics: syntax, inheritance, polymorphism, composition, STL containers, and algorithmsDive into newer features and abstractions including functional programming using lambdas, task-based concurrency, and smart pointersEmploy common but nontrivial financial models in modern C++Explore external open source math libraries, particularly Eigen and BoostImplement basic numerical routines in modern C++Understand best practices for writing clean and efficient code
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Practical Business Analytics Using SAS
Practical Business Analytics Using SAS: A Hands-on Guide shows SAS users and businesspeople how to analyze data effectively in real-life business scenarios. The book begins with an introduction to analytics, analytical tools, and SAS programming. The authors—both SAS, statistics, analytics, and big data experts—first show how SAS is used in business, and then how to get started programming in SAS by importing data and learning how to manipulate it. Besides illustrating SAS basic functions, you will see how each function can be used to get the information you need to improve business performance. Each chapter offers hands-on exercises drawn from real business situations. The book then provides an overview of statistics, as well as instruction on exploring data, preparing it for analysis, and testing hypotheses. You will learn how to use SAS to perform analytics and model using both basic and advanced techniques like multiple regression, logistic regression, and time series analysis, among other topics. The book concludes with a chapter on analyzing big data. Illustrations from banking and other industries make the principles and methods come to life.Readers will find just enough theory to understand the practical examples and case studies, which cover all industries. Written for a corporate IT and programming audience that wants to upgrade skills or enter the analytics field, this book includes:More than 200 examples and exercises, including code and datasets for practice. Relevant examples for all industries.Case studies that show how to use SAS analytics to identify opportunities, solve complicated problems, and chart a course.Practical Business Analytics Using SAS: A Hands-on Guide gives you the tools you need to gain insight into the data at your fingertips, predict business conditions for better planning, and make excellent decisions. Whether you are in retail, finance, healthcare, manufacturing, government, or any other industry, this book will help your organization increase revenue, drive down costs, improve marketing, and satisfy customers better than ever before.
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Data Analytics in Football
Data Analytics in Football provides students, researchers and coaches with a firm grounding in the principles of modern performance analysis. It offers an insight into the use of positional data, exploring how it can be collected, modelled, analysed and interpreted. Introducing cutting-edge methods, the book challenges long-held assumptions and encourages a new way of thinking about football analysis. The book seeks to define the role of positional data in football match analysis by exploring topics such as: • What is positional data analysis and how did it emerge from conventional match analysis? • How can positional data be collected, and which technologies can be used? • What Key Performance Indicators based on positional data should be used? • How can traditional match analysis be complemented by using positional data and advanced KPIs? • How can these new methods evolve in the future? Based on data collected from active leagues and fully revised for the new second edition, the text now also examines data collection in relation to other sports, how a data-driven approach in decision-making is achieved by professional teams and how big is the potential of the new methods of data collection and how could they evolve in the future. Accessibly written, packed full of examples from elite football and supplemented with expert interviews, Data Analytics in Football is a thought- provoking, rigorously evidence-based guide to the use of data analytics in football performance analysis. As such, it is a vital resource for any student, researcher or coach interested in performance analysis and skill acquisition, or anyone interested in football more generally.
R 9,364.22
Big Data Analytics for Sustainable Computing
Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore, to obtain the valuable information within the data, one must use a variety of innovative analytical methods, such as web analytics, machine learning, and network analytics. As the study of big data becomes more popular, there is an urgent demand for studies on high-level computational intelligence and computing services for analyzing this significant area of information science. Big Data Analytics for Sustainable Computing is a collection of innovative research that focuses on new computing and system development issues in emerging sustainable applications. Featuring coverage on a wide range of topics such as data filtering, knowledge engineering, and cognitive analytics, this publication is ideally designed for data scientists, IT specialists, computer science practitioners, computer engineers, academicians, professionals, and students seeking current research on emerging analytical techniques and data processing software.
R 7,915.69
Operations Management and Data Analytics Modelling
Operations Management and Data Analytics Modelling: Economic Crises Perspective addresses real operation management problems in thrust areas like the healthcare and energy management sectors and Industry 4.0.It discusses recent advances and trends in developing data-driven operation management-based methodologies, big data analysis, application of computers in industrial engineering, optimization techniques, development of decision support systems for industrial operation, the role of a multiple-criteria decision-making (MCDM) approach in operation management, fuzzy set theory-based operation management modelling and Lean Six Sigma.Features Discusses the importance of data analytics in industrial operations to improve economy Provides step-by-step implementation of operation management models to identify best practices Covers in-depth analysis using data-based operation management tools and techniques Discusses mathematical modelling for novel operation management models to solve industrial problemsThis book is aimed at graduate students and professionals in the field of industrial and production engineering, mechanical engineering and materials science.
R 7,566.17
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
R 2,543.00
R 1,824.00
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
Digitalization in Finance and Accounting
This book explores current digitalization issues in finance and accounting with particular focus on emerging and transitioning markets. It features models, empirical studies and cases studies on topics such as Fintech, blockchain technology, financing renewable energy, and XBRL usage from sectors such health care, pharmacology, transportation, and education. Such a complex view of current economic phenomena makes the volume attractive not only for academia, but also for regulators and policy-makers, when deliberating the potential outcome of competing regulatory mechanisms.
R 10,442.19
World of Business with Data and Analytics
This book covers research work spanning the breadth of ventures, a variety of challenges and the finest of techniques used to address data and analytics, by subject matter experts from the business world. The content of this book highlights the real-life business problems that are relevant to any industry and technology environment. This book helps us become a contributor to and accelerator of artificial intelligence, data science and analytics, deploy a structured life-cycle approach to data related issues, apply appropriate analytical tools & techniques to analyze data and deliver solutions with a difference. It also brings out the story-telling element in a compelling fashion using data and analytics. This prepares the readers to drive quantitative and qualitative outcomes and apply this mindset to various business actions in different domains such as energy, manufacturing, health care, BFSI, security, etc.
R 3,490.34
Financial Statement Analysis Workbook
The helpful workbook to help practice assessing financial statements Financial statement analysis is essential as part of any well-organized financial portfolio. As a companion piece, Financial Statement Analysis Workbook: A Practitioner's Guide allows readers the opportunity to test and hone the skills put forward in Martin Fridson and Fernando Alvarez's Financial Statement Analysis, a resource devoted to providing the analytical framework necessary to make sense of the sometimes misleading numbers put forth by companies. Scrutinizing financial statements allows one to, for example, evaluate a company’s stock price or determine merger or acquisition valuations. The Financial Statement Analysis Workbook, then, provides a pathway to become familiar with these methodologies in order to be prepared to use them in real-world scenarios. With the skills provided within, you can begin to undertake goal-oriented preparation for the practical challenges of contemporary business, and feel confident in your financial decision-making. This is aided by: Question-and-answer sections within this Workbook correspond to each chapter of Financial Statement AnalysisFinancial statement and computational exercises designed to require analysis and synthesis of concepts covered in the core textA full list of answers in the second half of the book that help explain pitfalls within the questions An essential tool for professional analysts, investors, and students, Financial Statement Analysis Workbook offers the perfect opportunity to help turn theory into reality.
R 961.00
R 977.00
Marketing Analytics
All customers differ. All customers change. All competitors react. All resources are limited. Robert W. Palmatier’s dynamic First Principles of Marketing framework provides the structure for this research-based, action-orientated guide to organizing analytics tools, marketing models and methodologies. When should you use a specific technique in data analytics? How does each new analytics technique improve performance? Which techniques are worth time and investment to implement? As organizations prioritize digital growth to better connect with customers, it is vital that you are able to respond confidently to these questions, enabling you to utilize marketing analytics to better understand your business and increase revenue. Marketing Analytics will help you to:· Learn how to contextualize models and statistical analysis within the foundational principles of marketing through the use of a problem-centric framework.· Understand technical analyses by engaging with a pertinent range of vivid examples, and a running case study to contextualize practical, jargon-free descriptions.· Embark on an applied learning pathway with a comprehensive companion website including datasets and walk-through videos on challenging tasks: bloomsbury.pub/marketing-analytics.· Take a software-agnostic approach to learning, enhanced by the provision of examples in free, open-source R and Tableau software.Authored by world-leading experts in marketing strategy, Marketing Analytics is the ideal textbook for advanced undergraduate, postgraduate and MBA students of marketing, and practitioners seeking to direct effective strategy from an analysis-based evidential approach.
R 6,768.00
