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Ai & Quantum Computing For Finance & Insurance: Fortunes And Challenges For China And America
This book offers a framework and analysis for the current technological landscape between the United States and China across the financial and insurance sectors as well as emerging technologies such as AI, Blockchain, Cloud and Data Analytics and Quantum Computing (ABCDQ). Based on original lecture slides used by the authors, the book presents contemporary and critical views of emergent technologies for a wide spectrum of readers from CEOs to university lecturers to students. The narrative aims to help readers upgrade their technology literacy and to overcome the fear of AI posed by our lizard brain.
R 3,572.08
Data Analytics Made Easy
Learn how to gain insights from your data as well as machine learning and become a presentation pro who can create interactive dashboardsKey FeaturesEnhance your presentation skills by implementing engaging data storytelling and visualization techniquesLearn the basics of machine learning and easily apply machine learning models to your dataImprove productivity by automating your data processesBook DescriptionData Analytics Made Easy is an accessible beginner’s guide for anyone working with data. The book interweaves four key elements:Data visualizations and storytelling – Tired of people not listening to you and ignoring your results? Don’t worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience.Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You’ll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components. Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You’ll not only be able to understand data scientists’ machine learning models; you’ll be able to challenge them and build your own.Creating interactive dashboards – Follow the book’s simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results.What you will learnUnderstand the potential of data and its impact on your businessImport, clean, transform, combine data feeds, and automate your processesInfluence business decisions by learning to create engaging presentationsBuild real-world models to improve profitability, create customer segmentation, automate and improve data reporting, and moreCreate professional-looking and business-centric visuals and dashboardsOpen the lid on the black box of AI and learn about and implement supervised and unsupervised machine learning modelsWho this book is forThis book is for beginners who work with data and those who need to know how to interpret their business/customer data. The book also covers the high-level concepts of data workflows, machine learning, data storytelling, and visualizations, which are useful for managers.No previous math, statistics, or computer science knowledge is required.
R 1,140.00
Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry
This book covers aspects of data science and predictive analytics used in oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital well, value chain integration, crude basket forecasting and so forth. It further discusses theoretical, methodological, well-established, and validated empirical work dealing with various related topics. Special focus has been given to experimental topics with various case studies.Features:Provides understanding of basics of IT technologies applied in Oil and Gas sectorIncludes deep comparison between different artificial intelligence techniquesAnalyses different simulators in Oil and Gas sector as well as discussion of AI applicationsFocuses on in-depth experimental and applied topicsDetails different case studies for upstream and downstreamThis book is aimed at professionals and graduate students in petroleum engineering, upstream industry, data analytics and digital transformation process in oil and gas.
R 6,654.18
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.
R 9,325.83
R 1,049.93
AI FINANCE: FINANCIAL MODELING - AI DRIVER® SERIES
This book focuses on bridging the gap between the widespread use of artificial intelligence tools and the absence of systematic training in finance education. It introduces the DRIVER® methodology, a structured six stage framework — Define&Discover, Represent, Implement, Validate, Evolve, and Reflect — designed to guide readers in professional AI collaboration while maintaining rigorous foundations in financial theory.Validated in classroom settings at Purdue University, where students described it as their most useful course, the DRIVER® approach equips learners with both conceptual depth and practical skills. Readers progressively build a portfolio of AI powered financial models, ranging from portfolio optimization to derivatives pricing, while cultivating the systematic thinking that distinguishes professionals from casual experimenters.The text is designed for a broad audience: finance students seeking to strengthen their analytical toolkit, professionals aiming to advance their modeling expertise, and institutions implementing AI transformation strategies. Importantly, no prior programming experience is required. The curriculum introduces Python fundamentals before advancing to sophisticated financial applications, ensuring accessibility while delivering immediate real world value.By combining financial theory with structured AI methodology, this book provides a transformative resource for modern finance education and practice. It reshapes how readers approach modeling, risk analysis, and decision making in an AI driven era, offering a comprehensive foundation for the next generation of financial professionals.
R 2,125.00
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 2,308.00
Twitter Data Analytics
This brief provides methods for harnessing Twitter data to discover solutions to complex inquiries. The brief introduces the process of collecting data through Twitter’s APIs and offers strategies for curating large datasets. The text gives examples of Twitter data with real-world examples, the present challenges and complexities of building visual analytic tools, and the best strategies to address these issues. Examples demonstrate how powerful measures can be computed using various Twitter data sources. Due to its openness in sharing data, Twitter is a prime example of social media in which researchers can verify their hypotheses, and practitioners can mine interesting patterns and build their own applications. This brief is designed to provide researchers, practitioners, project managers, as well as graduate students with an entry point to jump start their Twitter endeavors. It also serves as a convenient reference for readers seasoned in Twitter data analysis.
R 2,127.85
Data Science and Big Data Analytics in Smart Environments
Most applications generate large datasets, like social networking and social influence programs, smart cities applications, smart house environments, Cloud applications, public web sites, scientific experiments and simulations, data warehouse, monitoring platforms, and e-government services. Data grows rapidly, since applications produce continuously increasing volumes of both unstructured and structured data. Large-scale interconnected systems aim to aggregate and efficiently exploit the power of widely distributed resources. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance and to create a smart environment. The impact on data processing, transfer and storage is the need to re-evaluate the approaches and solutions to better answer the user needs. A variety of solutions for specific applications and platforms exist so a thorough and systematic analysis of existing solutions for data science, data analytics, methods and algorithms used in Big Data processing and storage environments is significant in designing and implementing a smart environment.Fundamental issues pertaining to smart environments (smart cities, ambient assisted leaving, smart houses, green houses, cyber physical systems, etc.) are reviewed. Most of the current efforts still do not adequately address the heterogeneity of different distributed systems, the interoperability between them, and the systems resilience. This book will primarily encompass practical approaches that promote research in all aspects of data processing, data analytics, data processing in different type of systems: Cluster Computing, Grid Computing, Peer-to-Peer, Cloud/Edge/Fog Computing, all involving elements of heterogeneity, having a large variety of tools and software to manage them. The main role of resource management techniques in this domain is to create the suitable frameworks for development of applications and deployment in smart environments, with respect to high performance. The book focuses on topics covering algorithms, architectures, management models, high performance computing techniques and large-scale distributed systems.
R 10,601.02
Application of Big Data and Business Analytics
Application of Big Data and Business Analytics uses advanced analytic tools to explore the solutions to problems in society, environment and industry. The chapters within bring together researchers, engineers and practitioners, encompassing a wide and diverse set of topics in almost every field. With the increase in the availability of data, analytics has now become a major element in both the top line and the bottom line of any organization. With this in mind, Application of Big Data and Business Analytics brings together researchers, engineers and practitioners, encompassing a wide and diverse set of topics in almost every field. The primary target audience of this book includes researchers, academicians and data scientists from a variety of disciplines interested in analyzing and application of big data analytics. However, this work will also be of general interest to postgraduates and undergraduates pursuing advanced study in big data.
R 4,584.83
R 248.09
Data Analytics: A Theoretical and Practical View from the EDISON Project
Building upon the knowledge introduced in The Data Science Framework, this book provides a comprehensive and detailed examination of each aspect of Data Analytics, both from a theoretical and practical standpoint. The book explains representative algorithms associated with different techniques, from their theoretical foundations to their implementation and use with software tools.Designed as a textbook for a Data Analytics Fundamentals course, it is divided into seven chapters to correspond with 16 weeks of lessons, including both theoretical and practical exercises. Each chapter is dedicated to a lesson, allowing readers to dive deep into each topic with detailed explanations and examples. Readers will learn the theoretical concepts and then immediately apply them to practical exercises to reinforce their knowledge. And in the lab sessions, readers will learn the ins and outs of the R environment and data science methodology to solve exercises with the R language. With detailed solutions provided for all examples and exercises, readers can use this book to study and master data analytics on their own. Whether you're a student, professional, or simply curious about data analytics, this book is a must-have for anyone looking to expand their knowledge in this exciting field.
R 8,780.31
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.
R 2,247.00
Handbook of Business Analytics
Business analytics are widely used in healthcare, manufacturing, and other industries that need to monitor or carefully analyze its users. Every day, over 2.5 quintillion data bytes are collected, for which it is essential to use business analytics. Many new software solutions and platforms have been relied upon to ingest and process large data sets.The book provides insights into business analytics as a key analytical technique to evaluate risk, project future commercial trends, and determine when upgradation is needed. It will help analysts and researchers in discovering trends and patterns in the data, which uses past information as a source and a variety of analytical algorithms for forecasting future events with a high level of accuracy, is the fundamental aim of business analytics. The chapters are written by academicians, industry experts, and researchers, each focusing on a different facet of business analytics. The book is meant for managers, executives, and entrepreneurs, who want to use data to improve consumer insights, strategic planning, and operational efficiency. With its theoretical foundations and real-world analytics applications, the book is also a great resource for academicians, scholars, and students studying business administration, data science, and information systems.
R 7,303.32
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.
R 2,601.00
Big Data Analytics
This book constitutes the refereed proceedings of the 6th International Conference on Big Data analytics, BDA 2018, held in Warangal, India, in December 2018. The 29 papers presented in this volume were carefully reviewed and selected from 93 submissions. The papers are organized in topical sections named: big data analytics: vision and perspectives; financial data analytics and data streams; web and social media data; big data systems and frameworks; predictive analytics in healthcare and agricultural domains; and machine learning and pattern mining.
R 2,196.36
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
R 562.00
R 1,160.00
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 for Insurance Companies
This book will be a "must" for people who want good knowledge of big data concepts and their applications in the real world, particularly in the field of insurance. It will be useful to people working in finance and to masters students using big data tools. The authors present the bases of big data: data analysis methods, learning processes, application to insurance and position within the insurance market. Individual chapters a will be written by well-known authors in this field.
R 5,795.00
R 1,435.00
R 1,099.00
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.
R 9,358.62
R 1,038.00
Business Analytics, Volume II
This business analytics (BA) text discusses the models based on fact-based data to measure past business performance to guide an organization in visualizing and predicting future business performance and outcomes.It provides a comprehensive overview of analytics in general with an emphasis on predictive analytics. Given the booming interest in analytics and data science, this book is timely and informative. It brings many terms, tools, and methods of analytics together.The first three chapters provide an introduction to BA, importance of analytics, types of BA—descriptive, predictive, and prescriptive—along with the tools and models. Business intelligence (BI) and a case on descriptive analytics are discussed. Additionally, the book discusses on the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics—machine learning, neural networks, and artificial intelligence. The concluding chapter discusses on the current state, job outlook, and certifications in analytics.
R 958.00
Mastering Text Analytics
This book is a comprehensive guide to mastering Natural Language Processing (NLP), a rapidly growing field in AI-powered text and data analytics. It equips you with tools and techniques to extract valuable insights from both structured and unstructured data, enabling you to uncover insights beyond the reach of traditional data analysis methods and stay competitive in this evolving domain. The book starts with foundational concepts, such as collecting and extracting data for NLP projects, before progressing to advanced topics like applications of transfer learning in NLP and Large Language Models (LLMs). Each chapter emphasizes real-world applications and includes practical case studies to ensure the knowledge is immediately applicable. Throughout the book, readers will find Python code demonstrations, hands-on projects, and detailed explanations of key concepts. Special features include business use cases from industries like healthcare and customer service, practice exercises to reinforce learning, and explorations of emerging NLP technologies. These elements make the book not only informative but also highly engaging and interactive. By the end of the book, the reader will have a solid foundation in Generative AI techniques to apply them to complex challenges. Whether you're a budding data scientist or a seasoned professional, this guide will help you harness the power of AI-driven text and data analytics effectively. What you will learn: Understand NLP with easy-to-follow explanations, examples, and Python implementations. Explore techniques such as transformers, word embeddings, and pragmatic analysis in real-world contexts. Work with real-world datasets and apply pre-processing, tokenization, and text extraction using NLP libraries. How to build complete NLP pipelines from data collection to model implementation, including sentiment analysis and chatbots. Learn state-of-the-art methods like deep learning techniques in NLP, large language models (LLMs), and zero-shot learning in NLP. Who this book is for: This book is tailored for data scientists, machine learning engineers, AI practitioners, and software developers seeking to learn NLP techniques and apply them to solve problems.
R 1,368.00
R 305.00
R 183.00
Advances in Big Data Analytics
Today, big data affects countless aspects of our daily lives. This book provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence.Since each aspect mentioned above concerns a specific domain of application, taken together, the algorithms, procedures, analysis and empirical studies presented here offer a general picture of big data developments. Accordingly, the book can not only serve as a textbook for graduates with a fundamental grasp of training in big data analytics, but can also show practitioners how to use the proposed techniques to deal with real-world big data problems.
R 7,326.00
Statistics For Business And Financial Economics
NewsProfessor Cheng-Few Lee ranks #1 based on his publications in the 26 core finance journals, and #163 based on publications in the 7 leading finance journals (Source: Most Prolific Authors in the Finance Literature: 1959-2008 by Jean L Heck and Philip L Cooley (Saint Joseph's University and Trinity University).This is an extensively revised edition of a popular statistics textbook for business and economics students. The first edition has been adopted by universities and colleges worldwide, including New York University, Carnegie Mellon University and UCLA.Designed for upper-level undergraduates, MBA and other graduate students, this book closely integrates various statistical techniques with concepts from business, economics and finance and clearly demonstrates the power of statistical methods in the real world of business. While maintaining the essence of the first edition, the new edition places more emphasis on finance, economics and accounting concepts with updated sample data. Students will find this book very accessible with its straightforward language, ample cases, examples, illustrations and real-life applications. The book is also useful for financial analysts and portfolio managers.
R 4,396.00
Business Data Analysis using Excel
Taking a thematic approach to the use of Excel spreadsheets in introductory business data analysis, this text has been designed to explain the overall nature of what is to be achieved and also instruction in how it is to be done.The learning approach is highly interactive and enables students to develop an understanding of the power of Excel in allowing both analysis of business data sets and in the flexible preparation of graphs, charts and tables for inclusion in reports and essays.The text is supported by an online resource centre with self marking exercises that can be used by instructors for formative and summative assessment, and a series of PowerPoint files containing all of the illustrated worksheets and figures.
R 4,531.00
Analyzing Analytics
This book aims to achieve the following goals: (1) to provide a high-level survey of key analytics models and algorithms without going into mathematical details; (2) to analyze the usage patterns of these models; and (3) to discuss opportunities for accelerating analytics workloads using software, hardware, and system approaches. The book first describes 14 key analytics models (exemplars) that span data mining, machine learning, and data management domains. For each analytics exemplar, we summarize its computational and runtime patterns and apply the information to evaluate parallelization and acceleration alternatives for that exemplar. Using case studies from important application domains such as deep learning, text analytics, and business intelligence (BI), we demonstrate how various software and hardware acceleration strategies are implemented in practice. This book is intended for both experienced professionals and students who are interested in understanding core algorithms behind analytics workloads. It is designed to serve as a guide for addressing various open problems in accelerating analytics workloads, e.g., new architectural features for supporting analytics workloads, impact on programming models and runtime systems, and designing analytics systems.
R 1,457.96
R 317.00
Energy and Analytics
This book details how to leverage big data style analytics to manage and coordinate the key issues in both energy supply and demand. It presents a detailed explanation of the underlying systems technology that enables big data in buildings and how this technology provides added cost benefit from efficiency, onsite solar, and electricity markets. It is a primer on Building Automation Systems Standards, web services and electricity markets and programs plus a complete tutorial on energy analytics hardware, software, and Internet-enabled offerings that energy managers must understand today.
R 5,862.00
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
Perception-based Data Mining and Decision Making in Economics and Finance
The primary goal of this book is to present to the scientific and management communities a selection of applications using more recent Soft Computing (SC) and Computing with Words and Perceptions (CWP) models and techniques meant to solve the economics and financial problems. The selected examples could also serve as a starting point or as an opening out, in the SC and CWP techniques application to a wider range of problems in economics and finance. Decision making in the present world is becoming more and more sophisticated, time consuming and difficult for human beings who require more and more computational support. This book addresses the significant increase on research and applications of Soft Computing and Computing with Words and Perceptions for decision making in Economics and Finance in recent years. Decision making is heavily based on information and knowledge usually extracted from the analysis of large amounts of data. Data mining techniques enabled with the capability to integrate human experience could be used for a more realistic business decision support. Computing with Words and Perceptions introduced by Lotfi Zadeh, can serve as a basis for such extension of traditional data mining and decision making systems. Fuzzy logic as a main constituent of CWP gives powerful tools for modeling and processing linguistic information defined on numerical domain.
R 6,576.20
Data Analytics in Project Management
This book aims to help the reader better understand the importance of data analysis in project management. Moreover, it provides guidance by showing tools, methods, techniques and lessons learned on how to better utilize the data gathered from the projects. First and foremost, insight into the bridge between data analytics and project management aids practitioners looking for ways to maximize the practical value of data procured. The book equips organizations with the know-how necessary to adapt to a changing workplace dynamic through key lessons learned from past ventures. The book’s integrated approach to investigating both fields enhances the value of research findings.
R 5,831.92
Intro to Python for Computer Science and Data Science
For introductory-level Python programming and/or data-science courses. A groundbreaking, flexible approach to computer science and data science The Deitels’ Introduction to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud offers a unique approach to teaching introductory Python programming, appropriate for both computer-science and data-science audiences. Providing the most current coverage of topics and applications, the book is paired with extensive traditional supplements as well as Jupyter Notebooks supplements. Real-world datasets and artificial-intelligence technologies allow students to work on projects making a difference in business, industry, government and academia. Hundreds of examples, exercises, projects (EEPs), and implementation case studies give students an engaging, challenging and entertaining introduction to Python programming and hands-on data science. The book's modular architecture enables instructors to conveniently adapt the text to a wide range of computer-science and data-science courses offered to audiences drawn from many majors. Computer-science instructors can integrate as much or as little data-science and artificial-intelligence topics as they'd like, and data-science instructors can integrate as much or as little Python as they'd like. The book aligns with the latest ACM/IEEE CS-and-related computing curriculum initiatives and with the Data Science Undergraduate Curriculum Proposal sponsored by the National Science Foundation.
R 5,413.00
Learning Analytics
Learning analytics is one of the most important research issues in the field of educational technology. By analyzing logs and records in educational databases and systems, it can provide useful information to teachers, learners, and decision makers – information which they can use to improve teaching strategies, learning performances, and educational policies. However, it is a great challenge for most researchers to efficiently analyze educational data in a meaningful way. This book presents various learning analytics approaches and applications, including the process of determining the coding scheme, analyzing the collected data, and interpreting the findings. This book was originally published as a special issue of Interactive Learning Environments.
R 2,235.00
Lectures On The Theory And Application Of Modern Finance With R And Chatgpt
These lecture notes are thought for Master courses in Finance, Fintech and Quantitative Finance programmes. We fully subscribe to the philosophy that post-graduate students should be offered courses that are really at the cutting edge of the technologies and advances that are disrupting the financial industry and delve deep into topics such as A.I., machine learning, and their importance for Asset Management.In these notes, the illustration of the theory of Finance is paired with practical applications to real-life asset allocation problems. A hands-on approach is proposed to construct and manipulate databases to build portfolios, assess their performance and manage their risk. The course begins with a section on the fundamentals on individual choice to market valuation, covering the traditional Markowitz mean-variance approach, market-based asset pricing and Arbitrage-based pricing theory.Empirical modelling in finance is then introduced by illustrating its working and its historical evolution. The translation of financial theory into action on data is driven by building predictive models for asset prices and returns. Basic models are explored, and programming emerges as an essential prerequisite for data manipulation. Readers can acquaint themselves with the statistical software R and exhibit the application of theoretical concepts to financial data, illustrated by sample programs, exercises, and corresponding solutions.
R 3,226.94
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 4,759.25
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.
R 1,301.00
Artificial Intelligence and Big Data for Financial Risk Management
This book presents a collection of high-quality contributions on the state-of-the-art in Artificial Intelligence and Big Data analysis as it relates to financial risk management applications. It brings together, in one place, the latest thinking on an emerging topic and includes principles, reviews, examples, and research directions. The book presents numerous specific use-cases throughout, showing practical applications of the concepts discussed. It looks at technologies such as eye movement analysis, data mining or mobile apps and examines how these technologies are applied by financial institutions, and how this affects both the institutions and the market. This work introduces students and aspiring practitioners to the subject of risk management in a structured manner. It is primarily aimed at researchers and students in finance and intelligent big data applications, such as intelligent information systems, smart economics and finance applications, and the internet of things in a marketing environment.
R 9,311.15
