Search results for "Data Analytics for Finance Using Python"
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Financial Times Guide to Finance for Non-Financial Managers, The
Gives you the confidence to ask the right business questions, make the correct finance decisions and competently speak the language of commerce to your colleagues, managers, customers and stakeholders. The Financial Times Guide to Finance for Non-Financial Managers will show you how to transform seemingly complex financial information and statistics into data that makes sense. And into data that you’ll feel confident talking about. You’ll learn the language of finance, which will help you better formulate decisions on a day-to-day basis. The book will also help you identify the warning signals and understand key performance indications and ratios. You’ll learn how to make better financial decisions, identify ways to increase profits and have increased confidence in approaching capital projects and making sound business decisions.
R 762.00
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Predictive Analytics For Dummies
Use Big Data and technology to uncover real-world insights You don't need a time machine to predict the future. All it takes is a little knowledge and know-how, and Predictive Analytics For Dummies gets you there fast. With the help of this friendly guide, you'll discover the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data. In no time, you'll learn how to incorporate algorithms through data models, identify similarities and relationships in your data, and predict the future through data classification. Along the way, you'll develop a roadmap by preparing your data, creating goals, processing your data, and building a predictive model that will get you stakeholder buy-in. Big Data has taken the marketplace by storm, and companies are seeking qualified talent to quickly fill positions to analyze the massive amount of data that are being collected each day. If you want to get in on the action and either learn or deepen your understanding of how to use predictive analytics to find real relationships between what you know and what you want to know, everything you need is a page away! Offers common use cases to help you get startedCovers details on modeling, k-means clustering, and moreIncludes information on structuring your dataProvides tips on outlining business goals and approaches The future starts today with the help of Predictive Analytics For Dummies.
R 684.00
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
Data Science and Big Data Analytics
This book presents conjectural advances in big data analysis, machine learning and computational intelligence, as well as their potential applications in scientific computing. It discusses major issues pertaining to big data analysis using computational intelligence techniques, and the conjectural elements are supported by simulation and modelling applications to help address real-world problems. An extensive bibliography is provided at the end of each chapter. Further, the main content is supplemented by a wealth of figures, graphs, and tables, offering a valuable guide for researchers in the field of big data analytics and computational intelligence.
R 6,081.90
R 1,563.00
Panel Data Analysis using EViews
A comprehensive and accessible guide to panel data analysis using EViews software This book explores the use of EViews software in creating panel data analysis using appropriate empirical models and real datasets. Guidance is given on developing alternative descriptive statistical summaries for evaluation and providing policy analysis based on pool panel data. Various alternative models based on panel data are explored, including univariate general linear models, fixed effect models and causal models, and guidance on the advantages and disadvantages of each one is given. Panel Data Analysis using EViews: Provides step-by-step guidance on how to apply EViews software to panel data analysis using appropriate empirical models and real datasets. Examines a variety of panel data models along with the author’s own empirical findings, demonstrating the advantages and limitations of each model.Presents growth models, time-related effects models, and polynomial models, in addition to the models which are commonly applied for panel data.Includes more than 250 examples divided into three groups of models (stacked, unstacked, and structured panel data), together with notes and comments.Provides guidance on which models not to use in a given scenario, along with advice on viable alternatives.Explores recent new developments in panel data analysis An essential tool for advanced undergraduate or graduate students and applied researchers in finance, econometrics and population studies. Statisticians and data analysts involved with data collected over long time periods will also find this book a useful resource.
R 5,306.00
Stochastic Filtering With Applications In Finance
This book provides a comprehensive account of stochastic filtering as a modeling tool in finance and economics. It aims to present this very important tool with a view to making it more popular among researchers in the disciplines of finance and economics. It is not intended to give a complete mathematical treatment of different stochastic filtering approaches, but rather to describe them in simple terms and illustrate their application with real historical data for problems normally encountered in these disciplines. Beyond laying out the steps to be implemented, the steps are demonstrated in the context of different market segments. Although no prior knowledge in this area is required, the reader is expected to have knowledge of probability theory as well as a general mathematical aptitude.Its simple presentation of complex algorithms required to solve modeling problems in increasingly sophisticated financial markets makes this book particularly valuable as a reference for graduate students and researchers interested in the field. Furthermore, it analyses the model estimation results in the context of the market and contrasts these with contemporary research publications. It is also suitable for use as a text for graduate level courses on stochastic modeling.
R 4,471.83
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
Hands-On Data Structures and Algorithms with Python
Understand how implementing different data structures and algorithms intelligently can make your Python code and applications more maintainable and efficientKey FeaturesExplore functional and reactive implementations of traditional and advanced data structuresApply a diverse range of algorithms in your Python codeImplement the skills you have learned to maximize the performance of your applicationsBook DescriptionChoosing the right data structure is pivotal to optimizing the performance and scalability of applications. This new edition of Hands-On Data Structures and Algorithms with Python will expand your understanding of key structures, including stacks, queues, and lists, and also show you how to apply priority queues and heaps in applications. You'll learn how to analyze and compare Python algorithms, and understand which algorithms should be used for a problem based on running time and computational complexity. You will also become confident organizing your code in a manageable, consistent, and scalable way, which will boost your productivity as a Python developer.By the end of this Python book, you'll be able to manipulate the most important data structures and algorithms to more efficiently store, organize, and access data in your applications.What you will learnUnderstand common data structures and algorithms using examples, diagrams, and exercisesExplore how more complex structures, such as priority queues and heaps, can benefit your codeImplement searching, sorting, and selection algorithms on number and string sequencesBecome confident with key string-matching algorithmsUnderstand algorithmic paradigms and apply dynamic programming techniquesUse asymptotic notation to analyze algorithm performance with regard to time and space complexitiesWrite powerful, robust code using the latest features of PythonWho this book is forThis book is for developers and programmers who are interested in learning about data structures and algorithms in Python to write complex, flexible programs. Basic Python programming knowledge is expected.
R 1,628.00
Advances in Statistical Modeling and Financial Machine Learning
The interplay between mathematics, finance, and technology is becoming increasingly intricate and vital in today’s rapidly evolving world. This book explores this complex relationship, offering a thorough examination of advanced theories, models, and applications that are shaping the future across these interconnected fields. The chapters have been curated to reflect the diversity of current research and its practical implications, providing readers with a deeper understanding of both foundational concepts and innovative developments.Divided into three sections, Advances in Statistical Modeling and Financial Machine Learning offers studies in foundations and developments in mathematical and statistical models, mathematical models in finance and emerging technological impacts, and control systems, fuzzy logic, and decision-making in complex environments.The book begins with an exploration of queuing models, a fundamental area of operations research, which, while theoretical, has real-world applications in various industries, including telecommunications, healthcare, and retail, where managing customer flow and service time is essential for operational success. The book looks into the realm of statistical inference, focusing on stress-strength reliability measures, important in the fields of engineering, manufacturing, and quality control, providing valuable insights into risk assessment. A comparative study of exponentiated distributions is presented, highlighting the importance of selecting the appropriate statistical distribution to model data accurately, a decision that can significantly impact the outcomes of research and analysis.The volume also looks at the transformative effects of the Fintech revolution, which is redefining how banking and finance operate, creating new opportunities for innovation while also presenting significant challenges. Chapters explore the impact of Fintech on traditional banking models, mathematical models for option pricing, the growing influence of machine learning in education, using data-driven strategies for stock price prediction.Several novel modeling systems are discussed, such as using exponential weighted moving average control charts, smart decision-making with Pythagorean fuzzy sets in granular uncertainty, generalized uncertainty principles for Wigner–Ville distribution associated with the quaternion linear canonical transform, and fixed points in ordered metric spaces.Offering a holistic view of the advancements in mathematics, finance, and technology, this volume will inspire new ideas and foster a deeper understanding of the intricate relationships that define these fields. By integrating advanced theoretical concepts with their practical applications, it offers a well-rounded understanding of the challenges and opportunities that characterize these fields today.
R 6,594.00
Pricing Analytics
This updated and expanded second edition has the same theme as the first: the price, the number, someone puts on a product to help consumers decide to buy that product, comes from statistically modeling data.Five new chapters provide a wider perspective on pricing analytics to more effectively develop elasticities and pricing strategies. This book gives readers the statistical modeling tools needed to get the number to put on a product, based on economic and statistical principles and theory. It covers elasticities, methodologies for analyzing customer choices including conjoint analysis, pricing segmentation, big data and econometric models—now with improved explanations and developments. The second edition adds discussion of three important and advanced topics: simulations for testing strategies under different conditions such as scenario analysis, AI applications for elasticity estimation and dynamic pricing, and the impacts of tariffs.A comprehensive and essential resource for analysts—economists, statisticians, or market researchers—who must estimate the optimal price for a product or service, this book is also a helpful guide for upper-level students in analytics disciplines.
R 2,308.00
Physics and Finance
This book introduces physics students to concepts and methods of finance. Despite being perceived as quite distant from physics, finance shares a number of common methods and ideas, usually related to noise and uncertainties. Juxtaposing the key methods to applications in both physics and finance articulates both differences and common features, this gives students a deeper understanding of the underlying ideas. Moreover, they acquire a number of useful mathematical and computational tools, such as stochastic differential equations, path integrals, Monte-Carlo methods, and basic cryptology. Each chapter ends with a set of carefully designed exercises enabling readers to test their comprehension.
R 2,015.00
R 1,140.00
Embedded Analytics
Embedded Analytics is one of the hottest trends in business intelligence right now. It's being used in multiple ways to improve decision making, provide faster insights, gain competitive advantages and grow revenue.Over the last 10 years, data analytics and data visualization have become essential components of an enterprise information strategy. Nevertheless, despite this recognition, the adoption of data analytics has remained remarkably static - perhaps reaching no more than thirty percent of potential users. This book explores the most important techniques for taking that adoption further: embedding analytics into the workflow of our everyday operations.
R 1,203.00
AI-Powered Business Analytics
This book offers an integrated framework that actively leverages AI-driven analytics, empowering businesses to transform raw data into strategic insights, actionable intelligence, and informed decision-making. It expertly covers core AI techniques and presents real-world case studies from diverse industries. Furthermore, the book provides step-by-step methodologies for constructing effective AI analytics solutions, guiding readers through the practical application of these powerful tools.The author empowers the reader to adopt strategies that actively increase predictive accuracy, automate complex analyses, and improve business processes, ultimately leading to better outcomes. The clear and structured chapters facilitate understanding, starting with the basics and progressing to advanced machine learning algorithms, equipping readers with hands-on skills for future practical applications. Key topics covered include neural networks, deep learning fundamentals, and convolutional and recurrent networks. The book illustrates this essential knowledge with real-world applications for solving complex business problems. Furthermore, it delves into Natural Language Processing (NLP) methods, sentiment analysis, text classification, and document analytics within the context of business cases like customer feedback analysis and market intelligence. By actively engaging with the material, the reader will learn and understand the tools for both predictive and prescriptive analytics, gaining valuable competitive insights and enhanced strategic decision-making capabilities. This comprehensive approach ensures readers can confidently apply AI to drive tangible business improvements.This book is targeted at upper-level undergraduates and graduates in Business Analytics, Data Science, AI in Business, MBA in Analytics, and Digital Transformation. Industry professionals like business analysts, data analysts, data scientists, AI specialists, and decision-makers seeking strategic AI analytics implementation will also benefit.
R 2,674.00
Statistical Tools for Finance and Insurance
Statistical Tools for Finance and Insurance presents ready-to-use solutions, theoretical developments and method construction for many practical problems in quantitative finance and insurance. Written by practitioners and leading academics in the field, this book offers a unique combination of topics from which every market analyst and risk manager will benefit. Features of the significantly enlarged and revised second edition:Offers insight into new methods and the applicability of the stochastic technologyProvides the tools, instruments and (online) algorithms for recent techniques in quantitative finance and modern treatments in insurance calculationsCovers topics such as - expected shortfall for heavy tailed and mixture distributions*- pricing of variance swaps*- volatility smile calibration in FX markets- pricing of catastrophe bonds and temperature derivatives*- building loss models and ruin probability approximation- insurance pricing with GLM*- equity linked retirement plans*(new topics in the second edition marked with*)Presents extensive examples
R 4,445.01
Delivering Data Analytics
The importance of data analytics is well known, but how can you get end users to engage with analytics and business intelligence (BI) when adoption of new technology can be frustratingly slow or may not happen at all? Avoid wasting time on dashboards and reports that no one uses with this practical guide to increasing analytics adoption by focusing on people and process, not technology. Pulling together agile, UX and change management principles, Delivering Data Analytics outlines a step-by-step, technology agnostic process designed to shift the organizational data culture and gain buy-in from users and stakeholders at every stage of the project. This book outlines how to succeed and build trust with stakeholders amid the politics, ambiguity and lack of engagement in business. With case studies, templates, checklists and scripts based on the author's considerable experience in analytics and data visualisation, this book covers the full cycle from requirements gathering and data assessment to training and launch. Ensure lasting adoption, trust and, most importantly, actionable business value with this roadmap to creating user-centric analytics projects.
R 1,715.68
Tools and Techniques for Financial Stability Analysis
Combatting financial stability risks is a highly challenging task which can by no means be concentrated into a ‘one-size fits all’ approach. It is important to select the appropriate tools and techniques in order to monitor, analyse, and maintain financial stability through proactive policy measures. Tools and Techniques for Financial Stability Analysis explores all key aspects of analytical tools and challenges for sound financial stability assessments. Comprehensive coverage is given to value at risk, stress testing, graphical tools for financial stability, the financial system stress index, as well as ratios and metrics of financial stability assessment. Finally, a concluding chapter is devoted to understand the key challenges involved in maintaining financial stability. This book will prove valuable to central bankers, economists, and policy-makers who are involved in the field of financial stability, as well as researchers studying the field.
R 4,817.88
R 166.00
Radical Solutions and Learning Analytics
Learning Analytics become the key for Personalised Learning and Teaching thanks to the storage, categorisation and smart retrieval of Big Data. Thousands of user data can be tracked online via Learning Management Systems, instant messaging channels, social networks and other ways of communication. Always with the explicit authorisation from the end user, being a student, a teacher, a manager or a persona in a different role, an instructional designer can design a way to produce a practical dashboard that helps him improve that very user’s performance, interaction, motivation or just grading. This book provides a thorough approach on how education, as such, from teaching to learning through management, is improved by a smart analysis of available data, making visible and useful behaviours, predictions and patterns that are hinder to the regular eye without the process of massive data.
R 3,823.14
Computational Frameworks for Political and Social Research with Python
This book is intended to serve as the basis for a first course in Python programming for graduate students in political science and related fields. The book introduces core concepts of software development and computer science such as basic data structures (e.g. arrays, lists, dictionaries, trees, graphs), algorithms (e.g. sorting), and analysis of computational efficiency. It then demonstrates how to apply these concepts to the field of political science by working with structured and unstructured data, querying databases, and interacting with application programming interfaces (APIs). Students will learn how to collect, manipulate, and exploit large volumes of available data and apply them to political and social research questions. They will also learn best practices from the field of software development such as version control and object-oriented programming. Instructors will be supplied with in-class example code, suggested homework assignments (with solutions), and material for practical lab sessions.
R 4,260.30
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
Data Analytics and Digital Transformation
Understanding the significance of data analytics is paramount for digital transformation but in many organisations they are separate units without fully aligned goals. As organisations are applying digital transformations to be adaptive and agile in a competitive environment, data analytics can play a critical role for their success. This book explores the crossroads between them and how to leverage their connection for improved business outcomes. The need to collaborate and share data is becoming an integral part of digital transformation. This creates new opportunities but also requires well considered and continuously assessed decision making as competitiveness is at stake. This book details approaches, concepts and frameworks, as well as actionable insights and good practices, including combined data management and agile concepts. Critical issues are discussed such as data quality and data governance, as well as compliance, privacy, and ethics. It also offers insights into how both private and public organisations can innovate and keep up with growing data volumes and increasing technological developments in the short, mid and long term. This book will be of direct appeal to global researchers and students across a range of business disciplines, including technology and innovation management, organisational studies and strategic management. It is also relevant for policy makers, regulators and executives of private and public organisations looking to implement successful transformation policies.
R 9,312.77
R 183.00
Data-Centric Business and Applications
This book presents real-world applications across sectors, focusing on economic security, energy transitions, digital agriculture, ESG reporting, and investment strategies--all informed by the Ukrainian experience of transformation and uncertainty. Comprising 20 comprehensive chapters, this book provides methodologies for risk assessment, performance evaluation, and decision-making in the fields of finance, enterprise management, and innovation ecosystems. It introduces tools such as logistic regression for assessing the health of banks, microgrid strategies for renewable energy, and transistor-based microsensors for biomedical monitoring. This book is ideal for researchers, analysts, and policy advisors, providing a framework for applying data-centric approaches to modern challenges in business, sustainability, and regional development.
R 5,861.00
Segmentation, Revenue Management and Pricing Analytics
The practices of revenue management and pricing analytics have transformed the transportation and hospitality industries, and are increasingly important in industries as diverse as retail, telecommunications, banking, health care and manufacturing. Segmentation, Revenue Management and Pricing Analytics guides students and professionals on how to identify and exploit revenue management and pricing opportunities in different business contexts. Bodea and Ferguson introduce concepts and quantitative methods for improving profit through capacity allocation and pricing. Whereas most marketing textbooks cover more traditional, qualitative methods for determining customer segments and prices, this book uses historical sales data with mathematical optimization to make those decisions. With hands-on practice and a fundamental understanding of some of the most common analytical models, readers will be able to make smarter business decisions and higher profits.This book will be a useful and enlightening read for MBA students in pricing and revenue management, marketing, and service operations.
R 3,553.00
Business Data Analytics
This book constitutes the proceedings of the First International Conference on Business Data Analytics (ICBDA 2022) held in Dehradun, India, in October 7–8, 2022. The purpose of conference is to bring the diverse community of data scientist, machine learning, analytics, and data specialist from all over the world to share their original piece research. The 6 full papers included in this proceedings were selected among 107 submissions in a single-blind review process. The theme of conference includes three sub categories: Predictive Modelling and Data Analytics, Decision Analytics and Support System and Business data Analytics.
R 4,229.87
R 659.52
Global Entrepreneurship Analytics
This innovative book proposes new methodologies for the measurement of entrepreneurship by applying techniques of demography, engineering, mathematics and statistics. Using the data from the Global Entrepreneurship Monitor (GEM), statistical demographic techniques are used for the evaluation of data quality (EDQ), and a new methodology for the estimation of Specific Entrepreneurship Rates (SER) and the Global Entrepreneurship Rate (GER) is proposed. At the same time the authors present artificial intelligence techniques such as Fuzzy Time Series (FTS) to forecast data series of the entrepreneurial population. Finally, they present a case study of the implementation of Big Data in Entrepreneurship using GEM data that shows the latest technological trends for the management of data, in support of making more accurate decisions. Being a methodological book, the techniques presented can be applied to any dataset in different areas. Readers will learn new methodologies of analysis and measurement of entrepreneurship using data from the Global Entrepreneurship Monitor. They will be able to access the experience of the authors through each of the applied cases in which the reader is taken by the hand, both through the scientific method and through the methodology of construction of more accurate metrics in entrepreneurship, with less error.This book will be of value to students at an advanced level, academics and researchers in the fields of Entrepreneurship, Business Analytics and Research Methodology.
R 3,595.89
High-Performance Big-Data Analytics
This book presents a detailed review of high-performance computing infrastructures for next-generation big data and fast data analytics. Features: includes case studies and learning activities throughout the book and self-study exercises in every chapter; presents detailed case studies on social media analytics for intelligent businesses and on big data analytics (BDA) in the healthcare sector; describes the network infrastructure requirements for effective transfer of big data, and the storage infrastructure requirements of applications which generate big data; examines real-time analytics solutions; introduces in-database processing and in-memory analytics techniques for data mining; discusses the use of mainframes for handling real-time big data and the latest types of data management systems for BDA; provides information on the use of cluster, grid and cloud computing systems for BDA; reviews the peer-to-peer techniques and tools and the common information visualization techniques, used in BDA.
R 3,061.41
Fraud Data Analytics Methodology
Uncover hidden fraud and red flags using efficient data analytics Fraud Data Analytics Methodology addresses the need for clear, reliable fraud detection with a solid framework for a robust data analytic plan. By combining fraud risk assessment and fraud data analytics, you'll be able to better identify and respond to the risk of fraud in your audits. Proven techniques help you identify signs of fraud hidden deep within company databases, and strategic guidance demonstrates how to build data interrogation search routines into your fraud risk assessment to locate red flags and fraudulent transactions. These methodologies require no advanced software skills, and are easily implemented and integrated into any existing audit program. Professional standards now require all audits to include data analytics, and this informative guide shows you how to leverage this critical tool for recognizing fraud in today's core business systems. Fraud cannot be detected through audit unless the sample contains a fraudulent transaction. This book explores methodologies that allow you to locate transactions that should undergo audit testing. Locate hidden signs of fraudBuild a holistic fraud data analytic planIdentify red flags that lead to fraudulent transactionsBuild efficient data interrogation into your audit plan Incorporating data analytics into your audit program is not about reinventing the wheel. A good auditor must make use of every tool available, and recent advances in analytics have made it accessible to everyone, at any level of IT proficiency. When the old methods are no longer sufficient, new tools are often the boost that brings exceptional results. Fraud Data Analytics Methodology gets you up to speed, with a brand new tool box for fraud detection.
R 3,094.00
Data Analytics and Digital Transformation
Understanding the significance of data analytics is paramount for digital transformation but in many organisations they are separate units without fully aligned goals. As organisations are applying digital transformations to be adaptive and agile in a competitive environment, data analytics can play a critical role for their success. This book explores the crossroads between them and how to leverage their connection for improved business outcomes. The need to collaborate and share data is becoming an integral part of digital transformation. This creates new opportunities but also requires well considered and continuously assessed decision making as competitiveness is at stake. This book details approaches, concepts and frameworks, as well as actionable insights and good practices, including combined data management and agile concepts. Critical issues are discussed such as data quality and data governance, as well as compliance, privacy, and ethics. It also offers insights into how both private and public organisations can innovate and keep up with growing data volumes and increasing technological developments in the short, mid and long term. This book will be of direct appeal to global researchers and students across a range of business disciplines, including technology and innovation management, organisational studies and strategic management. It is also relevant for policy makers, regulators and executives of private and public organisations looking to implement successful transformation policies.
R 2,478.15
People Analytics
This book addresses the topic of HR analytics focusing especially on demystifying analytics for HR managers, from both statistical and computing points of view. It presents the evolution and the importance of people analytics in today’s workforce management and highlights the motivation for studying business analytics, shows the different levels of analytic maturity and discusses real world success stories of business analytics in action through case studies. The volume provides the readers with a step-by-step guide to analyze HR data and interpret the results for strategy formulation. Each chapter of the book includes a vignette, data-driven exercises, and real-world case studies. The authors also introduce the readers to data visualization software Microsoft Power BI as well as elaborates on how to use Tableau Desktop.This book would be useful to the students of human resource management, organizational behaviour, applied psychology, social work and sociology. It would also serve as very good handbook for practicing business executives helping them to implement data analytics for effective decision making in all kinds of business situations around the world.
R 2,496.80
Economics of Data, Analytics, and Digital Transformation
Build a continuously learning and adapting organization that can extract increasing levels of business, customer and operational value from the amalgamation of data and advanced analytics such as AI and Machine LearningKey FeaturesMaster the Big Data Business Model Maturity Index methodology to transition to a value-driven organizational mindsetAcquire implementable knowledge on digital transformation through 8 practical lawsExplore the economics behind digital assets (data and analytics) that appreciate in value when constructed and deployed correctlyBook DescriptionIn today's digital era, every organization has data, but just possessing enormous amounts of data is not a sufficient market discriminator.The Economics of Data, Analytics, and Digital Transformation aims to provide actionable insights into the real market discriminators, including an organization's data-fueled analytics products that inspire innovation, deliver insights, help make practical decisions, generate value, and produce mission success for the enterprise.The book begins by first building your mindset to be value-driven and introducing the Big Data Business Model Maturity Index, its maturity index phases, and how to navigate the index. You will explore value engineering, where you will learn how to identify key business initiatives, stakeholders, advanced analytics, data sources, and instrumentation strategies that are essential to data science success. The book will help you accelerate and optimize your company's operations through AI and machine learning.By the end of the book, you will have the tools and techniques to drive your organization's digital transformation.Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the book:Data analytics should first and foremost be about action and value. Consequently, the great value of this book is that it seeks to be actionable. It offers a dynamic progression of purpose-driven ignition points that you can act upon.What you will learnTrain your organization to transition from being data-driven to being value-drivenNavigate and master the big data business model maturity indexLearn a methodology for determining the economic value of your data and analyticsUnderstand how AI and machine learning can create analytics assets that appreciate in value the more that they are usedBecome aware of digital transformation misconceptions and pitfallsCreate empowered and dynamic teams that fuel your organization's digital transformationWho this book is forThis book is designed to benefit everyone from students who aspire to study the economic fundamentals behind data and digital transformation to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their business careers.
R 1,531.00
Quantitative Analysis, Derivatives Modeling, And Trading Strategies: In The Presence Of Counterparty Credit Risk For The Fixed-income Market
This book addresses selected practical applications and recent developments in the areas of quantitative financial modeling in derivatives instruments, some of which are from the authors' own research and practice. It is written from the viewpoint of financial engineers or practitioners, and, as such, it puts more emphasis on the practical applications of financial mathematics in the real market than the mathematics itself with precise (and tedious) technical conditions. It attempts to combine economic insights with mathematics and modeling so as to help the reader to develop intuitions.Among the modeling and the numerical techniques presented are the practical applications of the martingale theories, such as martingale model factory and martingale resampling and interpolation. In addition, the book addresses the counterparty credit risk modeling, pricing, and arbitraging strategies from the perspective of a front office functionality and a revenue center (rather than merely a risk management functionality), which are relatively recent developments and are of increasing importance. It also discusses various trading structuring strategies and touches upon some popular credit/IR/FX hybrid products, such as PRDC, TARN, Snowballs, Snowbears, CCDS, and credit extinguishers.While the primary scope of this book is the fixed-income market (with further focus on the interest rate market), many of the methodologies presented also apply to other financial markets, such as the credit, equity, foreign exchange, and commodity markets.
R 2,247.90
Big Data, Big Analytics
Unique prospective on the big data analytics phenomenon for both business and IT professionals The availability of Big Data, low-cost commodity hardware and new information management and analytics software has produced a unique moment in the history of business. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue and profitability. The Age of Big Data is here, and these are truly revolutionary times. This timely book looks at cutting-edge companies supporting an exciting new generation of business analytics. Learn more about the trends in big data and how they are impacting the business world (Risk, Marketing, Healthcare, Financial Services, etc.)Explains this new technology and how companies can use them effectively to gather the data that they need and glean critical insightsExplores relevant topics such as data privacy, data visualization, unstructured data, crowd sourcing data scientists, cloud computing for big data, and much more.
R 1,186.00
Data Mining and Predictive Analytics
Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming languageFeatures over 750 chapter exercises, allowing readers to assess their understanding of the new materialProvides a detailed case study that brings together the lessons learned in the bookIncludes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.
R 4,560.00
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-Enabled Analytics
This book explores the novel uses and potentials of Data Envelopment Analysis (DEA) under big data. These areas are of widespread interest to researchers and practitioners alike. Considering the vast literature on DEA, one could say that DEA has been and continues to be, a widely used technique both in performance and productivity measurement, having covered a plethora of challenges and debates within the modelling framework.
R 4,397.10
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Big Data in Finance
This edited book explores the unique risks, opportunities, challenges, and societal implications associated with big data developments within the field of finance. While the general use of big data has been the subject of frequent discussions, this book will take a more focused look at big data applications in the financial sector. With contributions from researchers, practitioners, and entrepreneurs involved at the forefront of big data in finance, the book discusses technological and business-inspired breakthroughs in the field. The contributions offer technical insights into the different applications presented and highlight how these new developments may impact and contribute to the evolution of the financial sector. Additionally, the book presents several case studies that examine practical applications of big data in finance. In exploring the readiness of financial institutions to adapt to new developments in the big data/artificial intelligence space and assessing different implementation strategies and policy solutions, the book will be of interest to academics, practitioners, and regulators who work in this field.
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Machine Learning and Deep Learning Using Python and TensorFlow
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.Explore the principles and practices of machine learning and deep learning This comprehensive textbook lays out the theories and applications of machine learning and deep learning in a style that is approachable for students and working professionals at all math skill levels. You will discover how to handle data, regression and logistic regression, decision trees, cross-validation techniques and error testing, artificial neural networks (ANN, CNN and RNN), random forests, boosting, and more. Machine Learning and Deep Learning Using Python and TensorFlow includes valuable case studies across professional domains, including banking, insurance, e-commerce, retail, and healthcare. The book presents examples using both Python and TensorFlow programming languages and clearly demonstrates how to utilize this cutting-edge technology in today’s “smart” devices.• Presented in a mathematically non-rigorous way• Includes image classification and computer vision tasks • Written by a pair of business analytics experts
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