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Applied Linear Regression for Business Analytics with R
Applied Linear Regression for Business Analytics with R introduces regression analysis to business students using the R programming language with a focus on illustrating and solving real-time, topical problems. Specifically, this book presents modern and relevant case studies from the business world, along with clear and concise explanations of the theory, intuition, hands-on examples, and the coding required to employ regression modeling. Each chapter includes the mathematical formulation and details of regression analysis and provides in-depth practical analysis using the R programming language.
R 3,933.06
Data Analytics and Adaptive Learning
Data Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings. In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments. This set of rich, illuminating, international studies spans quantitative, qualitative, and mixed-methods research in higher education, K-12, and adult/continuing education contexts. By exploring the issues of definition and pedagogical practice that permeate teaching and learning and concluding with recommendations for the future research and practice necessary to support educators at all levels, this book will prepare researchers, developers, and graduate students of instructional technology to produce evidence for the benefits and challenges of data-driven learning.
R 9,329.97
Time Series with PyTorch
Time series is far more than fit-predict forecasting. Real mastery comes from intuition and is built through experimentation. Walk the full range with two practitioners: forecasting, conformal prediction, transfer learning, and beyond. Key FeaturesGrasp core concepts through clear explanations that build genuine understanding rather than surface familiarityWork with realistic datasets and develop the judgement to choose the right approach for your problemProgress from neural network fundamentals to advanced techniques across a full range of time series challenges.Book DescriptionNeural networks are powerful tools for time-series forecasting, but applying them effectively requires both practical experience and a clear understanding of architectures, training strategies, and evaluation methods. This book brings these ideas together in a structured and practical way.Starting with PyTorch fundamentals, you will build neural networks from scratch and progress through recurrent networks, attention mechanisms, and transformers before exploring forecasting architectures such as N-BEATS, N-HiTS, and the Temporal Fusion Transformer. Along the way, you will learn robust hyperparameter tuning, conformal prediction for uncertainty estimation, and reliable evaluation practices.Unlike most forecasting books, this text also explores topics often overlooked or treated separately, including transfer learning across collections of series, synthetic data generation with diffusion models, and self-supervised representation learning. Beyond forecasting, later chapters cover classification, clustering, anomaly detection, and embeddings for large-scale time-series modeling.Throughout, the focus is pragmatic: theory is reinforced through experimentation and implementation so you can apply these methods confidently to real-world time-series problems.What you will learnBuild, train, and evaluate neural networks for time series using PyTorch and PyTorch Lightning. Tune models with Bayesian optimisation and validate them with suitable metrics and strategies.Progress from feedforward and recurrent networks to transformers and models such as N-BEATS, N-HiTS, and TFT.Learn how global models use cross- and transfer learning across many series.Generate synthetic series and representations with diffusion and self-supervised methods.Apply modern approaches to classification, clustering, and anomaly detection.Who this book is forThis book is for data analysts, scientists, and students who want to know how to apply deep learning methods to time-series forecasting problems with PyTorch for real-world business problems.While the book assumes some understanding of statistics and modeling, you won’t need in-depth knowledge of time series to follow along. Some familiarity with Python is important, but we do not assume any prior knowledge of PyTorch.The main goal of this book is to be accessible to those with little or no experience with deep learning methods in time series.
R 1,791.00
Python for Linguists
Specifically designed for linguists, this book provides an introduction to programming using Python for those with little to no experience of coding. Python is one of the most popular and widely-used programming languages as it's also available for free and runs on any operating system. All examples in the text involve language data and can be adapted or used directly for language research. The text focuses on key language-related issues: searching, text manipulation, text encoding and internet data, providing an excellent resource for language research. More experienced users of Python will also benefit from the advanced chapters on graphical user interfaces and functional programming.
R 1,837.39
Python and Matplotlib Essentials for Scientists and Engineers
This book provides an introduction to the core features of the Python programming language and Matplotlib plotting routings for scientists and engineers (or students of either discipline) who want to use Python™ to analyse data, simulate physical processes, and render publication-quality plots. No previous programming experience is needed before reading the first page.Readers will learn the core features of the Python programming language in under a day. They will be able to immediately use Python to implement codes that solve their own problems and make beautiful plots and animations. Python code is extremely fast to prototype, allowing users to achieve results quickly and accurately. Python and Matplotlib Essentials for Scientists and Engineers is accessible for motivated high-school students, but will likely be most useful for undergraduate and graduate students as well as working professionals who have some background with the basic mathematical concepts. This book is intended for technical people who want to get things done.
R 1,464.00
R 275.00
R 1,221.00
Big Data Analytics Using Splunk
Big Data Analytics Using Splunk is a hands-on book showing how to process and derive business value from big data in real time. Examples in the book draw from social media sources such as Twitter (tweets) and Foursquare (check-ins). You also learn to draw from machine data, enabling you to analyze, say, web server log files and patterns of user access in real time, as the access is occurring. Gone are the days when you need be caught out by shifting public opinion or sudden changes in customer behavior. Splunk’s easy to use engine helps you recognize and react in real time, as events are occurring. Splunk is a powerful, yet simple analytical tool fast gaining traction in the fields of big data and operational intelligence. Using Splunk, you can monitor data in real time, or mine your data after the fact. Splunk’s stunning visualizations aid in locating the needle of value in a haystack of a data. Geolocation support spreads your data across a map, allowing you to drill down to geographic areas of interest. Alerts can run in the background and trigger to warn you of shifts or events as they are taking place.With Splunk you can immediately recognize and react to changing trends and shifting public opinion as expressed through social media, and to new patterns of eCommerce and customer behavior. The ability to immediately recognize and react to changing trends provides a tremendous advantage in today’s fast-paced world of Internet business. Big Data Analytics Using Splunk opens the door to an exciting world of real-time operational intelligence.Built around hands-on projects Shows how to mine social media Opens the door to real-time operational intelligence
R 1,698.63
Industrial Data Analytics for Diagnosis and Prognosis
Discover data analytics methodologies for the diagnosis and prognosis of industrial systems under a unified random effects model In Industrial Data Analytics for Diagnosis and Prognosis - A Random Effects Modelling Approach, distinguished engineers Shiyu Zhou and Yong Chen deliver a rigorous and practical introduction to the random effects modeling approach for industrial system diagnosis and prognosis. In the book’s two parts, general statistical concepts and useful theory are described and explained, as are industrial diagnosis and prognosis methods. The accomplished authors describe and model fixed effects, random effects, and variation in univariate and multivariate datasets and cover the application of the random effects approach to diagnosis of variation sources in industrial processes. They offer a detailed performance comparison of different diagnosis methods before moving on to the application of the random effects approach to failure prognosis in industrial processes and systems. In addition to presenting the joint prognosis model, which integrates the survival regression model with the mixed effects regression model, the book also offers readers: A thorough introduction to describing variation of industrial data, including univariate and multivariate random variables and probability distributions Rigorous treatments of the diagnosis of variation sources using PCA pattern matching and the random effects modelAn exploration of extended mixed effects model, including mixture prior and Kalman filtering approach, for real time prognosisA detailed presentation of Gaussian process model as a flexible approach for the prediction of temporal degradation signals Ideal for senior year undergraduate students and postgraduate students in industrial, manufacturing, mechanical, and electrical engineering, Industrial Data Analytics for Diagnosis and Prognosis is also an indispensable guide for researchers and engineers interested in data analytics methods for system diagnosis and prognosis.
R 4,753.00
R 2,869.00
Data Science Solutions with Python
Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras. The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Principal Components Analysis and Linear Discriminant Analysis are explored. And automated machine learning is unpacked.This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key big data frameworks and ML and DL frameworks. You will need prior knowledge of the basics of statistics, Python programming, probability theories, and predictive analytics. What You Will LearnUnderstand widespread supervised and unsupervised learning, including key dimension reduction techniquesKnow the big data analytics layers such as data visualization, advanced statistics, predictive analytics, machine learning, and deep learningIntegrate big data frameworks with a hybrid of machine learning frameworks and deep learning frameworksDesign, build, test, and validate skilled machine models and deep learning modelsOptimize model performance using data transformation, regularization, outlier remedying, hyperparameter optimization, and data split ratio alteration Who This Book Is ForData scientists and machine learning engineers with basic knowledge and understanding of Python programming, probability theories, and predictive analytics
R 866.00
Python for Chemists
This accessible and self-contained guide provides a comprehensive introduction to the popular programming language Python, with a focus on applications in chemistry and chemical physics. Ideally suited to students and researchers of chemistry learning to employ Python for problem-solving in their research, this fast-paced primer first builds a solid foundation in the programming language before progressing to advanced concepts and applications in chemistry. The required syntax and data structures are established, and then applied to solve problems computationally. Popular numerical packages are described in detail, including NumPy, SciPy, Matplotlib, SymPy, and pandas. End of chapter problems are included throughout, with worked solutions available within the book. Additional resources, datasets, and Jupyter Notebooks are provided on a companion website, allowing readers to reinforce their understanding and gain confidence applying their knowledge through a hands-on approach.
R 1,800.00
Audit Analytics in the Financial Industry
In Audit Analytics in the Financial Industry, editors Jun Dai, Miklos A. Vasarhelyi and Ann F. Medinets bring together a cast of expert contributors to explore ways to integrate Audit Analytics techniques into existing audit programs for the financial industry. Separated into six parts, the contributors take a variety of approaches to this exploration. In Part One, the contributors present two articles illustrating the process of applying Audit Analytics to solving audit problems. Part Two contains four studies that use various Audit Analytics techniques to discover fraud risks and potential frauds in the credit card sector. In Part Three, the chapter focus on the insurance sector and show the application of clustering techniques in auditing. Part Four includes two chapters on how to employ Audit Analytics in the transitory system for fraud/anomaly detection. Finally, Parts Five and Six illustrate the use of Audit Analytics to assess risk in the lawsuit and payment processes. For students, researchers, and professionals in the accounting sector, this is an unmissable read exploring the latest research in Audit Analytics.
R 5,237.84
R 611.00
Introductory Econometrics for Finance
A complete resource for finance students, this textbook presents the most common empirical approaches in finance in a comprehensive and well-illustrated manner that shows how econometrics is used in practice, and includes detailed case studies to explain how the techniques are used in relevant financial contexts. Maintaining the accessible prose and clear examples of previous editions, the new edition of this best-selling textbook provides support for the main industry-standard software packages, expands the coverage of introductory mathematical and statistical techniques into two chapters for students without prior econometrics knowledge, and includes a new chapter on advanced methods. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Online resources include extensive teacher and student support materials, including EViews, Stata, R, and Python software guides.
R 2,901.00
Data Analytics Applied to the Mining Industry
Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book: Explains how to implement advanced data analytics through case studies and examples in mining engineering Provides approaches and methods to improve data-driven decision making Explains a concise overview of the state of the art for Mining Executives and Managers Highlights and describes critical opportunity areas for mining optimization Brings experience and learning in digital transformation from adjacent sectors
R 11,133.27
R 384.72
Computational Finance
The book covers a wide range of topics, yet essential, in Computational Finance (CF), understood as a mix of Finance, Computational Statistics, and Mathematics of Finance. In that regard it is unique in its kind, for it touches upon the basic principles of all three main components of CF, with hands-on examples for programming models in R. Thus, the first chapter gives an introduction to the Principles of Corporate Finance: the markets of stock and options, valuation and economic theory, framed within Computation and Information Theory (e.g. the famous Efficient Market Hypothesis is stated in terms of computational complexity, a new perspective). Chapters 2 and 3 give the necessary tools of Statistics for analyzing financial time series, it also goes in depth into the concepts of correlation, causality and clustering. Chapters 4 and 5 review the most important discrete and continuous models for financial time series. Each model is provided with an example program in R. Chapter 6 covers the essentials of Technical Analysis (TA) and Fundamental Analysis. This chapter is suitable for people outside academics and into the world of financial investments, as a primer in the methods of charting and analysis of value for stocks, as it is done in the financial industry. Moreover, a mathematical foundation to the seemly ad-hoc methods of TA is given, and this is new in a presentation of TA. Chapter 7 reviews the most important heuristics for optimization: simulated annealing, genetic programming, and ant colonies (swarm intelligence) which is material to feed the computer savvy readers. Chapter 8 gives the basic principles of portfolio management, through the mean-variance model, and optimization under different constraints which is a topic of current research in computation, due to its complexity. One important aspect of this chapter is that it teaches how to use the powerful tools for portfolio analysis from the RMetrics R-package. Chapter 9 is a natural continuation of chapter 8 into the new area of research of online portfolio selection. The basic model of the universal portfolio of Cover and approximate methods to compute are also described.
R 2,891.96
Soccer Analytics
Sports analytics is on the rise, with top soccer clubs, bookmakers, and broadcasters all employing statisticians and data scientists to gain an edge over their competitors.Many popular books have been written exploring the mathematics of soccer. However, few supply details on how soccer data can be analysed in real-life. The book addresses this issue via a practical route one approach designed to show readers how to successfully tackle a range of soccer related problems using the easy-to-learn computer language R. Through a series of easy-to-follow examples, the book explains how R can be used to:Download and edit soccer dataProduce graphics and statisticsPredict match outcomes and final league positionsFormulate betting strategiesRank teamsConstruct passing networksAssess match playSoccer Analytics: An Introduction with R is a comprehensive introduction to soccer analytics aimed at all those interested in analysing soccer data, be they fans, gamblers, coaches, sports scientists, or data scientists and statisticians wishing to pursue a career in professional soccer. It aims to equip the reader with the knowledge and skills required to confidently analyse soccer data using R, all in a few easy lessons.
R 2,421.74
Big Data Management And Analytics
With the proliferation of information, big data management and analysis have become an indispensable part of any system to handle such amounts of data. The amount of data generated by the multitude of interconnected devices increases exponentially, making the storage and processing of these data a real challenge.Big data management and analytics have gained momentum in almost every industry, ranging from finance or healthcare. Big data can reveal key insights if handled and analyzed properly; it has great application potential to improve the working of any industry. This book covers the spectrum aspects of big data; from the preliminary level to specific case studies. It will help readers gain knowledge of the big data landscape.Highlights of the topics covered include description of the Big Data ecosystem; real-world instances of big data issues; how the Vs of Big Data (volume, velocity, variety, veracity, valence, and value) affect data collection, monitoring, storage, analysis, and reporting; structural process to get value out of Big Data and recognize the differences between a standard database management system and a big data management system.Readers will gain insights into choice of data models, data extraction, data integration to solve large data problems, data modelling using machine learning techniques, Spark's scalable machine learning techniques, modeling a big data problem into a graph database and performing scalable analytical operations over the graph and different tools and techniques for processing big data and its applications including in healthcare and finance.
R 3,843.43
R 1,373.00
Data Analytics for Business Intelligence
This book studies data, analytics, and intelligence using Boolean structure. Chapters dive into the theories, foundations, technologies, and methods of data, analytics and intelligence.The primary aim of this book is to convey the theories and technologies of data, analytics, and intelligence with applications to readers based on systematic generalization and specialization. Sun uses the Boolean structure to deconstruct all books and papers related to data, analytics, intelligence and reorganize them to reshape the world of big data, data analytics, analytics intelligence, data science, and artificial intelligence. Multi-industry applications in business, management and decision making are provided. Cutting-edge theories, technologies, and applications of data, analytics, and intelligence and their integration are also explored. Overall, this book provides original insights on sharing computing, insight computing, platform computing, a calculus of intelligent analytics and intelligent business analytics, meta computing, data analyticizing, DDPP (Descriptive, diagnostic, predictive, and prescriptive) computing and analytics. A useful resource with multi-industry applications for scientists, engineers, data analysts, educators and university students.
R 7,608.19
Deep Data Analytics for New Product Development
This book presents and develops the deep data analytics for providing the information needed for successful new product development.Deep Data Analytics for New Product Development has a simple theme: information about what customers need and want must be extracted from data to effectively guide new product decisions regarding concept development, design, pricing, and marketing. The benefits of reading this book are twofold. The first is an understanding of the stages of a new product development process from ideation through launching and tracking, each supported by information about customers. The second benefit is an understanding of the deep data analytics for extracting that information from data. These analytics, drawn from the statistics, econometrics, market research, and machine learning spaces, are developed in detail and illustrated at each stage of the process with simulated data. The stages of new product development and the supporting deep data analytics at each stage are not presented in isolation of each other, but are presented as a synergistic whole. This book is recommended reading for analysts involved in new product development. Readers with an analytical bent or who want to develop analytical expertise would also greatly benefit from reading this book, as well as students in business programs.
R 2,483.00
Time Series for Economics and Finance
Focusing on methods for data that are ordered in time, this textbook provides a comprehensive guide to analyzing time series data using modern techniques from data science. It is specifically tailored to economics and finance applications, aiming to provide students with rigorous training. Chapters cover Bayesian approaches, nonparametric smoothing methods, machine learning, and continuous time econometrics. Theoretical and empirical exercises, concise summaries, bolded key terms, and illustrative examples are included throughout to reinforce key concepts and bolster understanding. Ancillary materials include an instructor's manual with solutions and additional exercises, PowerPoint lecture slides, and datasets. With its clear and accessible style, this textbook is an essential tool for advanced undergraduate and graduate students in economics, finance, and statistics.
R 6,974.26
Business Analytics
This exciting new textbook offers an accessible, business-focused overview of the key theoretical concepts underpinning modern data analytics. It provides engaging and practical advice on using the key software tools, including SAS Visual Analytics, R and DataRobot, that are used in organisations to help make effective data-driven decisions. Combining theory with hands-on practical examples, this essential text includes cutting edge coverage of new areas of interest including social media analytics, design thinking and the ethical implications of using big data. A wealth of learning features including exercises, cases, online resources and data sets help students to develop analytic problem-solving skills.With its management perspective on analytics and its coverage of a range of popular software tools, this is an ideal essential text for upper-level undergraduate, postgraduate and MBA students. It is also ideal for practitioners wanting to understand the broader organisational context of big data analysis and to engage critically with the tools and techniques of business analytics.Accompanying online resources for this title can be found at bloomsburyonlineresources.com/business-analytics. These resources are designed to support teaching and learning when using this textbook and are available at no extra cost.
R 2,647.00
Data Analytics for Business
Interest in applying analytics, machine learning, and artificial intelligence to sales and marketing has grown dramatically, with no signs of slowing down. This book provides essential guidance to apply advanced analytics and data mining techniques to real-world business applications. The foundation of this text is the author’s 20-plus years of developing and delivering big data and artificial intelligence solutions across multiple industries: financial services, pharmaceuticals, consumer packaged goods, media, and retail. He provides guidelines and summarized cases for those studying or working in the fields of data science, data engineering, and business analytics. The book also offers a distinctive style: a series of essays, each of which summarizes a critical lesson or provides a step-by-step business process, with specific examples of successes and failures.Sales and marketing executives, project managers, business and engineering professionals, and graduate students will find this clear and comprehensive book the ideal companion when navigating the complex world of big data analytics.
R 7,561.47
Data-Driven Marketing Analytics
Data-Driven Marketing Analytics: Research Methods and Analytical Tools bridges the gap between traditional marketing research and modern big data analytics, offering a step-by-step guide to transforming data into actionable insights.Readers will gain practical skills to design and manage research projects, predict customer behaviour, and uncover hidden patterns in purchasing data. The book delivers these benefits through hands-on exercises, shareable datasets, and visual workflows, ensuring accessibility for users without coding experience. Key features include real-world case studies, interactive examples, and tools for segmentation, forecasting, and strategic decision-making.This book is tailored for postgraduate students, researchers, and marketing professionals seeking to master data-driven decision-making. It is ideal for advanced courses in Marketing Analytics, Management Information Systems, and Business Statistics.The book is supported by a comprehensive online repository containing over 100 ready-to-use files, including datasets, Excel solutions, SPSS files, and Orange workflows.
R 8,572.00
Development Methodologies for Big Data Analytics Systems
This book presents research in big data analytics (BDA) for business of all sizes. The authors analyze problems presented in the application of BDA in some businesses through the study of development methodologies based on the three approaches - 1) plan-driven, 2) agile and 3) hybrid lightweight. The authors first describe BDA systems and how they emerged with the convergence of Statistics, Computer Science, and Business Intelligent Analytics with the practical aim to provide concepts, models, methods and tools required for exploiting the wide variety, volume, and velocity of available business internal and external data - i.e. Big Data - and provide decision-making value to decision-makers. The book presents high-quality conceptual and empirical research-oriented chapters on plan-driven, agile, and hybrid lightweight development methodologies and relevant supporting topics for BDA systems suitable to be used for large-, medium-, and small-sized business organizations.
R 6,594.00
Data Analytics for Business Intelligence
This book studies data, analytics, and intelligence using Boolean structure. Chapters dive into the theories, foundations, technologies, and methods of data, analytics and intelligence.The primary aim of this book is to convey the theories and technologies of data, analytics, and intelligence with applications to readers based on systematic generalization and specialization. Sun uses the Boolean structure to deconstruct all books and papers related to data, analytics, intelligence and reorganize them to reshape the world of big data, data analytics, analytics intelligence, data science, and artificial intelligence. Multi-industry applications in business, management and decision making are provided. Cutting-edge theories, technologies, and applications of data, analytics, and intelligence and their integration are also explored. Overall, this book provides original insights on sharing computing, insight computing, platform computing, a calculus of intelligent analytics and intelligent business analytics, meta computing, data analyticizing, DDPP (Descriptive, diagnostic, predictive, and prescriptive) computing and analytics. A useful resource with multi-industry applications for scientists, engineers, data analysts, educators and university students.
R 3,302.30
R 1,191.00
Essentials of Excel, Excel VBA, SAS and Minitab for Statistical and Financial Analyses
This introductory textbook for business statistics teaches statistical analysis and research methods via business case studies and financial data using Excel, Minitab, and SAS. Every chapter in this textbook engages the reader with data of individual stock, stock indices, options, and futures.One studies and uses statistics to learn how to study, analyze, and understand a data set of particular interest. Some of the more popular statistical programs that have been developed to use statistical and computational methods to analyze data sets are SAS, SPSS, and Minitab. Of those, we look at Minitab and SAS in this textbook. One of the main reasons to use Minitab is that it is the easiest to use among the popular statistical programs. We look at SAS because it is the leading statistical package used in industry. We also utilize the much less costly and ubiquitous Microsoft Excel to do statistical analysis, as the benefits of Excel have become widely recognized in the academic world and its analytical capabilities extend to about 90 percent of statistical analysis done in the business world. We demonstrate much of our statistical analysis using Excel and double check the analysis and outcomes using Minitab and SAS—also helpful in some analytical methods not possible or practical to do in Excel.
R 6,594.00
R 531.00
Deep Learning for Data Analytics
Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis.
R 5,002.00
Applied Insurance Analytics
Insurers: use analytics to drive far more value from your most important asset -- data! Today, many insurers radically underutilize their data, leaving them vulnerable to traditional and non-traditional competitors alike. Now, drawing on 25 years of industry experience, Patricia Saporito shows how to systematically leverage analytics to improve business performance and customer satisfaction throughout any insurance business. Applied Insurance Analytics demonstrates how to use analytics to systematically improve operations ranging from underwriting and risk management to claims. Even more important: it will help you drive more value everywhere by defining a focused enterprise-wide analytics strategy, and overcoming the challenges that stand in your way. Saporito helps you assess your current analytics maturity, choose the new applications that offer the most value, and master best practices from throughout the industry and beyond. Throughout, she helps you gain more value from data assets, technologies and tools you've already invested in. You'll find new case studies, practical tools, and easy templates for improving the "Analytics IQ" of your entire enterprise. For every insurance industry professional and manager concerned with analytics, including users, IT pros, sales/marketing specialists, and data scientists. This book will also be valuable to students in any MBA or other program focused on insurance or risk management, and to many students in IT or analytics-specific programs.
R 2,279.00
Navigating the Future of Finance in the Age of AI
The financial landscape is rapidly evolving, and professionals must keep pace with the complex relationship between traditional financial practices and cutting-edge technologies. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into finance presents a transformative shift that requires a deep understanding and strategic approach. Navigating the Future of Finance in the Age of AI offers a comprehensive exploration of AI's impact on the financial sector, from predictive analytics to algorithmic trading strategies. Each chapter is written by experts in the field, and they provide practical insights and real-world examples to make complex concepts accessible and actionable. The book also delves into regulatory challenges, ethical considerations, and case studies, equipping readers with the tools needed to harness AI's transformative power in finance. Whether you are a finance professional seeking to enhance decision-making, a data scientist aiming to apply ML techniques in finance, or an academic exploring AI's role in financial innovation, this book is an indispensable resource that offers a roadmap to navigate the complexities of AI-driven finance and seize the opportunities it presents.
R 9,853.69
Business Analytics
Business analytics is all about leveraging data analysis and analytical modeling methods to achieve business objectives. This book is suitable for students with special interest in data science, business, or both. Methods are presented in an intuitive fashion, and business applications are backed up by an approachable level of mathematical rigor. The balance makes the material accessible to students with a broad range of backgrounds: those looking for 'So that's how it works!', 'So that's what it's for!', and 'So that's how to do it!' Students will come away well-positioned and well-differentiated for their future industry, consulting, academic, and government roles as data-savvy business practitioners. Accompanied by a robust set of digital materials including code, data sets, primers, and more for both students and instructors, this text is the ideal learning resource for senior and graduate students in business or data science departments.
R 2,552.00
Media Analytics
This textbook takes a case study approach to media and audience analytics. Realizing the best way to understand analytics in the digital age is to practice it, the authors have created a collection of cases using datasets that present real and hypothetical scenarios for students to work through.Media Analytics introduces the key principles of media economics and management. It outlines how to interpret and present results, the principles of data visualization and storytelling, and the basics of research design and sampling. Although shifting technology makes measurement and analytics a dynamic space, this book takes an evergreen, conceptual approach, reminding students to focus on the principles and foundations that will remain constant.Aimed at upper-level students in the fast-growing area of media analytics in a cross-platform world, students using this text will learn how to find the stories in the data and how to present those stories in an engaging way to others.Instructor and Student Resources include an Instructor’s Manual, discussion questions, short exercises, and links to additional resources. They are available online at www.routledge.com/cw/hollifield.
R 6,892.27
R for Business Analytics
R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages. With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. The book tries to expose the reader to a breadth of business analytics topics without burying the user in needless depth. The included references and links allow the reader to pursue business analytics topics. This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses. Business Intelligence (BI) is the seamless dissemination of information through the organization, which primarily involves business metrics both past and current for the use of decision support in businesses. Data Mining (DM) is the process of discovering new patterns from large data using algorithms and statistical methods. To differentiate between the three, BI is mostly current reports, BA is models to predict and strategize and DM matches patterns in big data. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy. The book utilizes Albert Einstein’s famous remarks on making things as simple as possible, but no simpler. This book will blow the last remaining doubts in your mind about using R in your business environment. Even non-technical users will enjoy the easy-to-use examples. The interviews with creators and corporate users of R make the book very readable. The author firmly believes Isaac Asimov was a better writer in spreading science than any textbook or journal author.
R 4,762.00
Advanced Digital Technologies in Financial and Business Management
This new book explores emerging new technologies for the development of business and finance sectors, to strengthen economic growth, and to facilitate the efficiency and creation of new and innovative business and finance management applications. The book covers state-of-the-art technologies such as artificial intelligence and big data analytics in fintech and finance, blockchain technology in the insurance industry, the use of AI and data science in tax administration, the growing use of 6G in industrial automation, quantum machine learning in finance, the merging of AI and blockchain in auditing, and more. Chapters explore the collaboration between humans and machines, the application of data science and artificial intelligence in the retail sector, and AI and data science in business services with a focus on enhancing efficiency and driving innovation.
R 8,790.12
Analytical Finance: Volume I
This book provides an introduction to the valuation of financial instruments on equity markets. Written from the perspective of trading, risk management and quantitative research functions and written by a practitioner with many years’ experience in markets and in academia, it provides a valuable learning tool for students and new entrants to these markets.Coverage includes:·Trading and sources of risk, including credit and counterparty risk, market and model risks, settlement and Herstatt risks.·Numerical methods including discrete-time methods, finite different methods, binomial models and Monte Carlo simulations.·Probability theory and stochastic processes from the financial modeling perspective, including probability spaces, sigma algebras, measures and filtrations.·Continuous time models such as Black-Scholes-Merton; Delta-hedging and Delta-Gamma-hedging; general diffusion models and how to solve Partial Differential Equation using the Feynmann-Kac representation.·The trading, structuring and hedging several kinds of exotic options, including: Binary/Digital options; Barrier options; Lookbacks; Asian options; Chooses; Forward options; Ratchets; Compounded options; Basket options; Exchange and Currency-linked options; Pay later options and Quantos.·A detailed explanation of how to construct synthetic instruments and strategies for different market conditions, discussing more than 30 different option strategies. With source code for many of the models featured in the book provided and extensive examples and illustrations throughout, this book provides a comprehensive introduction to this topic and will prove an invaluable learning tool and reference for anyone studying or working in this field.
R 3,354.25
Practical Business Analytics Using SAS
Practical Business Analytics Using SAS: A Hands-on Guide shows SAS users and businesspeople how to analyze data effectively in real-life business scenarios. The book begins with an introduction to analytics, analytical tools, and SAS programming. The authors—both SAS, statistics, analytics, and big data experts—first show how SAS is used in business, and then how to get started programming in SAS by importing data and learning how to manipulate it. Besides illustrating SAS basic functions, you will see how each function can be used to get the information you need to improve business performance. Each chapter offers hands-on exercises drawn from real business situations. The book then provides an overview of statistics, as well as instruction on exploring data, preparing it for analysis, and testing hypotheses. You will learn how to use SAS to perform analytics and model using both basic and advanced techniques like multiple regression, logistic regression, and time series analysis, among other topics. The book concludes with a chapter on analyzing big data. Illustrations from banking and other industries make the principles and methods come to life.Readers will find just enough theory to understand the practical examples and case studies, which cover all industries. Written for a corporate IT and programming audience that wants to upgrade skills or enter the analytics field, this book includes:More than 200 examples and exercises, including code and datasets for practice. Relevant examples for all industries.Case studies that show how to use SAS analytics to identify opportunities, solve complicated problems, and chart a course.Practical Business Analytics Using SAS: A Hands-on Guide gives you the tools you need to gain insight into the data at your fingertips, predict business conditions for better planning, and make excellent decisions. Whether you are in retail, finance, healthcare, manufacturing, government, or any other industry, this book will help your organization increase revenue, drive down costs, improve marketing, and satisfy customers better than ever before.
R 2,056.32
R 1,069.00
R 409.00
Cloud Analytics with Microsoft Azure
Learn to extract actionable insights from your big data in real time using a range of Microsoft Azure featuresKey FeaturesUpdated with the latest features and new additions to Microsoft AzureMaster the fundamentals of cloud analytics using AzureLearn to use Azure Synapse Analytics (formerly known as Azure SQL Data Warehouse) to derive real-time customer insightsBook DescriptionCloud Analytics with Microsoft Azure serves as a comprehensive guide for big data analysis and processing using a range of Microsoft Azure features. This book covers everything you need to build your own data warehouse and learn numerous techniques to gain useful insights by analyzing big data The book begins by introducing you to the power of data with big data analytics, the Internet of Things (IoT), machine learning, artificial intelligence, and DataOps. You will learn about cloud-scale analytics and the services Microsoft Azure offers to empower businesses to discover insights. You will also be introduced to the new features and functionalities added to the modern data warehouse. Finally, you will look at two real-world business use cases to demonstrate high-level solutions using Microsoft Azure. The aim of these use cases will be to illustrate how real-time data can be analyzed in Azure to derive meaningful insights and make business decisions. You will learn to build an end-to-end analytics pipeline on the cloud with machine learning and deep learning concepts. By the end of this book, you will be proficient in analyzing large amounts of data with Azure and using it effectively to benefit your organization.What you will learnExplore the concepts of modern data warehouses and data pipelinesDiscover unique design considerations while applying a cloud analytics solutionDesign an end-to-end analytics pipeline on the cloudDifferentiate between structured, semi-structured, and unstructured dataChoose a cloud-based service for your data analytics solutionsUse Azure services to ingest, store, and analyze data of any scaleWho this book is forThis book is designed to benefit software engineers, Azure developers, cloud consultants, and anyone who is keen to learn the process of deriving business insights from huge amounts of data using Azure.Though not necessary, a basic understanding of data analytics concepts such as data streaming, data types, the machine learning life cycle, and Docker containers will help you get the most out of the book.
R 1,889.00
Getting Started with Forex Trading Using Python
Discover the inner workings of today's forex market, the essential risks in forex algo trading, and how to mitigate themKey FeaturesBuild trading applications with research and without advanced Python programming skillsDive into professional fx trading while enhancing your trading apps to be more accurateDevelop simple yet efficient backtesting applications to help keep your expectations realisticBook DescriptionAlgorithm-based trading is a popular choice for Python programmers due to its apparent simplicity. However, very few traders get the results they want, partly because they aren't able to capture the complexity of the factors that influence the market. Getting Started with Forex Trading Using Python helps you understand the market and build an application that reaps desirable results. The book is a comprehensive guide to everything that is market-related: data, orders, trading venues, and risk. From the programming side, you'll learn the general architecture of trading applications, systemic risk management, de-facto industry standards such as FIX protocol, and practical examples of using simple Python codes. You'll gain an understanding of how to connect to data sources and brokers, implement trading logic, and perform realistic tests. Throughout the book, you'll be encouraged to further study the intricacies of algo trading with the help of code snippets. By the end of this book, you'll have a deep understanding of the fx market from the perspective of a professional trader. You'll learn to retrieve market data, clean it, filter it, compress it into various formats, apply trading logic, emulate the execution of orders, and test the trading app before trading live.What you will learnExplore the forex market organization and operationsUnderstand the sources of alpha and the concept of algo tradingGet a grasp on typical risks and ways to mitigate themUnderstand fundamental and technical analysisConnect to data sources and check the integrity of market dataUse API and FIX protocol to send ordersTranslate trading ideas into codeRun reliable backtesting emulating real-world market conditionsWho this book is forThis book is for financial traders and python developers who are interested in forex trading. Academic researchers looking to focus on practical applications will find this book useful. This book can also help established fx market professionals who want to take the first steps in algo trading. Familiarity with Python and object-oriented programming within the scope of an online course or self-study is a must. Knowledge of network protocols and interfaces is a plus but not a prerequisite, as is specific knowledge about markets and trading.
R 1,270.00
