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Applied Big Data Analytics in Operations Management
Operations management is a tool by which companies can effectively meet customers’ needs using the least amount of resources necessary. With the emergence of sensors and smart metering, big data is becoming an intrinsic part of modern operations management.Applied Big Data Analytics in Operations Management enumerates the challenges and creative solutions and tools to apply when using big data in operations management. Outlining revolutionary concepts and applications that help businesses predict customer behavior along with applications of artificial neural networks, predictive analytics, and opinion mining on business management, this comprehensive publication is ideal for IT professionals, software engineers, business professionals, managers, and students of management.
R 6,209.00
In-Memory Analytics with Apache Arrow
Process tabular data and build high-performance query engines on modern CPUs and GPUs using Apache Arrow, a standardized language-independent memory format, for optimal performanceKey FeaturesLearn about Apache Arrow's data types and interoperability with pandas and ParquetWork with Apache Arrow Flight RPC, Compute, and Dataset APIs to produce and consume tabular dataReviewed, contributed, and supported by Dremio, the co-creator of Apache ArrowBook DescriptionApache Arrow is designed to accelerate analytics and allow the exchange of data across big data systems easily.In-Memory Analytics with Apache Arrow begins with a quick overview of the Apache Arrow format, before moving on to helping you to understand Arrow's versatility and benefits as you walk through a variety of real-world use cases. You'll cover key tasks such as enhancing data science workflows with Arrow, using Arrow and Apache Parquet with Apache Spark and Jupyter for better performance and hassle-free data translation, as well as working with Perspective, an open source interactive graphical and tabular analysis tool for browsers. As you advance, you'll explore the different data interchange and storage formats and become well-versed with the relationships between Arrow, Parquet, Feather, Protobuf, Flatbuffers, JSON, and CSV. In addition to understanding the basic structure of the Arrow Flight and Flight SQL protocols, you'll learn about Dremio's usage of Apache Arrow to enhance SQL analytics and discover how Arrow can be used in web-based browser apps. Finally, you'll get to grips with the upcoming features of Arrow to help you stay ahead of the curve.By the end of this book, you will have all the building blocks to create useful, efficient, and powerful analytical services and utilities with Apache Arrow.What you will learnUse Apache Arrow libraries to access data files both locally and in the cloudUnderstand the zero-copy elements of the Apache Arrow formatImprove read performance by memory-mapping files with Apache ArrowProduce or consume Apache Arrow data efficiently using a C APIUse the Apache Arrow Compute APIs to perform complex operationsCreate Arrow Flight servers and clients for transferring data quicklyBuild the Arrow libraries locally and contribute back to the communityWho this book is forThis book is for developers, data analysts, and data scientists looking to explore the capabilities of Apache Arrow from the ground up. This book will also be useful for any engineers who are working on building utilities for data analytics and query engines, or otherwise working with tabular data, regardless of the programming language. Some familiarity with basic concepts of data analysis will help you to get the most out of this book but isn't required. Code examples are provided in the C++, Go, and Python programming languages.
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Analytics: Data Science, Data Analysis and Predictive Analytics for Business
SO MANY PEOPLE DREAM OF BECOMING THEIR OWN BOSS OR SUCCEEDING IN THEIR CHOSEN PROFESSION, AND WITH THE RESOURCES AVAILABLE TODAY, MORE ENTREPRENEURS AND PROFESSIONALS ARE ACHIEVING GREAT SUCCESS! HOWEVER, SUCCESS SHOULD BE DEFINED FOR THE LONG TERM, AND AS OPPORTUNITIES START TO GROW, SO DOES THE COMPETITION. Getting your business up and running or starting on your career path is one thing, but have a sustainable business or career is completely another. Many people make the mistake of making plans but having no follow-through. This is where analytics comes in. Don't you wish to have the power to know what your target consumers are thinking? Won't you want to have a preview of what future trends to expect in the market you are in? Well, this book is just the one you need. This book will teach you, in simple and easy-to-understand terms, how to take advantage of data from your daily operations and make such data a powerful tool that can influence how well your business does over time.
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Python for ArcGIS Pro
Extend your ArcGIS expertise by unlocking the world of Python programming. A fully hands-on guide that takes you through exercise after exercise using real data and real problems. NOTE: This book is compatible with ArcGIS Pro 2.9.Key FeaturesLearn the core components of the two Python modules for ArcGIS: ArcPy and ArcGIS API for PythonUse ArcPy, pandas, NumPy, and ArcGIS in ArcGIS Pro Notebooks to manage and analyze geospatial data at scaleIntegrate with ArcGIS Online using Python to publish and manage dataBook DescriptionIntegrating Python into your day-to-day ArcGIS work is highly recommended when dealing with large amounts of geospatial data. Python for ArcGIS Pro aims to help you get your work done faster, with greater repeatability and higher confidence in your results. Starting from programming basics and building in complexity, two experienced ArcGIS professionals-turned-Python programmers teach you how to incorporate scripting at each step: automating the production of maps for print, managing data between ArcGIS Pro and ArcGIS Online, creating custom script tools for sharing, and then running data analysis and visualization on top of the ArcGIS geospatial library, all using Python. You'll use ArcGIS Pro Notebooks to explore and analyze geospatial data, and write data engineering scripts to manage ongoing data processing and data transfers. This exercise-based book also includes three rich real-world case studies, giving you an opportunity to apply and extend the concepts you studied earlier. Irrespective of your expertise level with Esri software or the Python language, you'll benefit from this book's hands-on approach, which takes you through the major uses of Python for ArcGIS Pro to boost your ArcGIS productivity.What you will learnAutomate map production to make and edit maps at scale, cutting down on repetitive tasksPublish map layer data to ArcGIS OnlineAutomate data updates using the ArcPy Data Access module and cursorsTurn your scripts into script tools for ArcGIS ProLearn how to manage data on ArcGIS OnlineQuery, edit, and append to feature layers and create symbology with renderers and colorizersApply pandas and NumPy to raster and vector analysisLearn new tricks to manage data for entire cities or large companiesWho this book is forThis book is ideal for anyone looking to add Python to their ArcGIS Pro workflows, even if you have no prior experience with programming. This includes ArcGIS professionals, intermediate ArcGIS Pro users, ArcGIS Pro power users, students, and people who want to move from being a GIS Technician to GIS Analyst; GIS Analyst to GIS Programmer; or GIS Developer/Programmer to a GIS Architect. Basic familiarity with geospatial/GIS syntax, ArcGIS, and data science (pandas) is helpful, though not necessary.
R 2,247.00
Data and Analytics Strategy for Business
Struggling to convert your data, analytics and AI strategy into business value? Data and Analytics Strategy for Business by Simon Asplen-Taylor is an essential guide for CDOs, CAIOs, CIOs and CTOs seeking to deliver measurable results from data investments. Focused on aligning data, analytics and AI with strategic goals, this book helps leaders move from initial implementation to transformation. Fully updated with the latest AI developments, it provides practical tools and real-world insights to accelerate implementation, drive adoption and optimize performance across the organization. You'll learn how to: - Align data, analytics and AI to strategic business outcomes - Build trust through data quality, governance and transparency - Integrate AI and ML Secure executive and organizational buy-in for long-term success - Learn from real-world examples including Tesco and Facebook With guidance grounded in real results, Data and Analytics Strategy for Business helps senior leaders lead smarter, execute faster and unlock enterprise-wide impact. Themes include: data strategy, AI adoption, analytics leadership, digital transformation, data governance, performance optimization
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Python for Scientists
The third edition of this practical introduction to Python has been thoroughly updated, with all code migrated to Jupyter notebooks. The notebooks are available online with executable versions of all of the book's content (and more). The text starts with a detailed introduction to the basics of the Python language, without assuming any prior knowledge. Building upon each other, the most important Python packages for numerical math (NumPy), symbolic math (SymPy), and plotting (Matplotlib) are introduced, with brand new chapters covering numerical methods (SciPy) and data handling (Pandas). Further new material includes guidelines for writing efficient Python code and publishing code for other users. Simple and concise code examples, revised for compatibility with Python 3, guide the reader and support the learning process throughout the book. Readers from all of the quantitative sciences, whatever their background, will be able to quickly acquire the skills needed for using Python effectively.
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Geospatial Data Analytics on AWS
Build an end-to-end geospatial data lake in AWS using popular AWS services such as RDS, Redshift, DynamoDB, and Athena to manage geodata Purchase of the print or Kindle book includes a free PDF eBook.Key FeaturesExplore the architecture and different use cases to build and manage geospatial data lakes in AWSDiscover how to leverage AWS purpose-built databases to store and analyze geospatial dataLearn how to recognize which anti-patterns to avoid when managing geospatial data in the cloudBook DescriptionManaging geospatial data and building location-based applications in the cloud can be a daunting task. This comprehensive guide helps you overcome this challenge by presenting the concept of working with geospatial data in the cloud in an easy-to-understand way, along with teaching you how to design and build data lake architecture in AWS for geospatial data.You’ll begin by exploring the use of AWS databases like Redshift and Aurora PostgreSQL for storing and analyzing geospatial data. Next, you’ll leverage services such as DynamoDB and Athena, which offer powerful built-in geospatial functions for indexing and querying geospatial data. The book is filled with practical examples to illustrate the benefits of managing geospatial data in the cloud. As you advance, you’ll discover how to analyze and visualize data using Python and R, and utilize QuickSight to share derived insights. The concluding chapters explore the integration of commonly used platforms like Open Data on AWS, OpenStreetMap, and ArcGIS with AWS to enable you to optimize efficiency and provide a supportive community for continuous learning.By the end of this book, you’ll have the necessary tools and expertise to build and manage your own geospatial data lake on AWS, along with the knowledge needed to tackle geospatial data management challenges and make the most of AWS services.What you will learnDiscover how to optimize the cloud to store your geospatial dataExplore management strategies for your data repository using AWS Single Sign-On and IAMCreate effective SQL queries against your geospatial data using AthenaValidate postal addresses using Amazon Location servicesProcess structured and unstructured geospatial data efficiently using RUse Amazon SageMaker to enable machine learning features in your applicationExplore the free and subscription satellite imagery data available for use in your GISWho this book is forIf you understand the importance of accurate coordinates, but not necessarily the cloud, then this book is for you. This book is best suited for GIS developers, GIS analysts, data analysts, and data scientists looking to enhance their solutions with geospatial data for cloud-centric applications. A basic understanding of geographic concepts is suggested, but no experience with the cloud is necessary for understanding the concepts in this book.
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Business Analytics
This innovative new textbook, co-authored by an established academic and a leading practitioner, is the first to bring together issues of cloud computing, business intelligence and big data analytics in order to explore how organisations use cloud technology to analyse data and make decisions. In addition to offering an up-to-date exploration of key issues relating to data privacy and ethics, information governance, and the future of analytics, the text describes the options available in deploying analytic solutions to the cloud and draws on real-world, international examples from companies such as Rolls Royce, Lego, Volkswagen and Samsung.Combining academic and practitioner perspectives that are crucial to the understanding of this growing field, Business Analytics acts an ideal core text for undergraduate, postgraduate and MBA modules on Big Data, Business and Data Analytics, and Business Intelligence, as well as functioning as a supplementary text for modules in Marketing Analytics. The book is also an invaluable resource for practitioners and will quickly enable the next generation of 'Information Builders' within organisations to understand innovative cloud based-analytic solutions.
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Forensic Analytics
Become the forensic analytics expert in your organization using effective and efficient data analysis tests to find anomalies, biases, and potential fraud—the updated new edition Forensic Analytics reviews the methods and techniques that forensic accountants can use to detect intentional and unintentional errors, fraud, and biases. This updated second edition shows accountants and auditors how analyzing their corporate or public sector data can highlight transactions, balances, or subsets of transactions or balances in need of attention. These tests are made up of a set of initial high-level overview tests followed by a series of more focused tests. These focused tests use a variety of quantitative methods including Benford’s Law, outlier detection, the detection of duplicates, a comparison to benchmarks, time-series methods, risk-scoring, and sometimes simply statistical logic. The tests in the new edition include the newly developed vector variation score that quantifies the change in an array of data from one period to the next. The goals of the tests are to either produce a small sample of suspicious transactions, a small set of transaction groups, or a risk score related to individual transactions or a group of items. The new edition includes over two hundred figures. Each chapter, where applicable, includes one or more cases showing how the tests under discussion could have detected the fraud or anomalies. The new edition also includes two chapters each describing multi-million-dollar fraud schemes and the insights that can be learned from those examples. These interesting real-world examples help to make the text accessible and understandable for accounting professionals and accounting students without rigorous backgrounds in mathematics and statistics. Emphasizing practical applications, the new edition shows how to use either Excel or Access to run these analytics tests. The book also has some coverage on using Minitab, IDEA, R, and Tableau to run forensic-focused tests. The use of SAS and Power BI rounds out the software coverage. The software screenshots use the latest versions of the software available at the time of writing. This authoritative book: Describes the use of statistically-based techniques including Benford’s Law, descriptive statistics, and the vector variation score to detect errors and anomaliesShows how to run most of the tests in Access and Excel, and other data analysis software packages for a small sample of the testsApplies the tests under review in each chapter to the same purchasing card data from a government entityIncludes interesting cases studies throughout that are linked to the tests being reviewed.Includes two comprehensive case studies where data analytics could have detected the frauds before they reached multi-million-dollar levelsIncludes a continually-updated companion website with the data sets used in the chapters, the queries used in the chapters, extra coverage of some topics or cases, end of chap
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Programming with Python for Social Scientists
As data become ′big′, fast and complex, the software and computing tools needed to manage and analyse them are rapidly developing. Social scientists need new tools to meet these challenges, tackle big datasets, while also developing a more nuanced understanding of - and control over - how these computing tools and algorithms are implemented. Programming with Python for Social Scientists offers a vital foundation to one of the most popular programming tools in computer science, specifically for social science researchers, assuming no prior coding knowledge. It guides you through the full research process, from question to publication, including:the fundamentals of why and how to do your own programming in social scientific research,questions of ethics and research design,a clear, easy to follow ′how-to′ guide to using Python, with a wide array of applications such as data visualisation, social media data research, social network analysis, and more.Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis.
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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.
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Data Science Projects with Python
Gain hands-on experience of Python programming with industry-standard machine learning techniques using pandas, scikit-learn, and XGBoostKey FeaturesThink critically about data and use it to form and test a hypothesisChoose an appropriate machine learning model and train it on your dataCommunicate data-driven insights with confidence and clarityBook DescriptionIf data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable.In this book, you’ll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you’ll experience in real-world data science projects.You’ll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest.Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world.By the end of this data science book, you’ll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data.What you will learnLoad, explore, and process data using the pandas Python packageUse Matplotlib to create compelling data visualizationsImplement predictive machine learning models with scikit-learnUse lasso and ridge regression to reduce model overfittingEvaluate random forest and logistic regression model performanceDeliver business insights by presenting clear, convincing conclusionsWho this book is forData Science Projects with Python – Second Edition is for anyone who wants to get started with data science and machine learning. If you’re keen to advance your career by using data analysis and predictive modeling to generate business insights, then this book is the perfect place to begin. To quickly grasp the concepts covered, it is recommended that you have basic experience of programming with Python or another similar language, and a general interest in statistics.
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Data and Analytics Strategy for Business
Struggling to convert your data, analytics and AI strategy into business value? Data and Analytics Strategy for Business by Simon Asplen-Taylor is an essential guide for CDOs, CAIOs, CIOs and CTOs seeking to deliver measurable results from data investments. Focused on aligning data, analytics and AI with strategic goals, this book helps leaders move from initial implementation to transformation. Fully updated with the latest AI developments, it provides practical tools and real-world insights to accelerate implementation, drive adoption and optimize performance across the organization. You'll learn how to: - Align data, analytics and AI to strategic business outcomes - Build trust through data quality, governance and transparency - Integrate AI and ML Secure executive and organizational buy-in for long-term success - Learn from real-world examples including Tesco and Facebook With guidance grounded in real results, Data and Analytics Strategy for Business helps senior leaders lead smarter, execute faster and unlock enterprise-wide impact. Themes include: data strategy, AI adoption, analytics leadership, digital transformation, data governance, performance optimization
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Workout in Computational Finance, with Website
A comprehensive introduction to various numerical methods used in computational finance today Quantitative skills are a prerequisite for anyone working in finance or beginning a career in the field, as well as risk managers. A thorough grounding in numerical methods is necessary, as is the ability to assess their quality, advantages, and limitations. This book offers a thorough introduction to each method, revealing the numerical traps that practitioners frequently fall into. Each method is referenced with practical, real-world examples in the areas of valuation, risk analysis, and calibration of specific financial instruments and models. It features a strong emphasis on robust schemes for the numerical treatment of problems within computational finance. Methods covered include PDE/PIDE using finite differences or finite elements, fast and stable solvers for sparse grid systems, stabilization and regularization techniques for inverse problems resulting from the calibration of financial models to market data, Monte Carlo and Quasi Monte Carlo techniques for simulating high dimensional systems, and local and global optimization tools to solve the minimization problem.
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Training Systems Using Python Statistical Modeling
Leverage the power of Python and statistical modeling techniques for building accurate predictive modelsKey FeaturesGet introduced to Python's rich suite of libraries for statistical modelingImplement regression, clustering and train neural networks from scratchIncludes real-world examples on training end-to-end machine learning systems in PythonBook DescriptionPython's ease of use and multi-purpose nature has led it to become the choice of tool for many data scientists and machine learning developers today. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book takes you through an exciting journey, of using these libraries to implement effective statistical models for predictive analytics.You’ll start by diving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will look at supervised learning, where you will explore the principles of machine learning and train different machine learning models from scratch. You will also work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. This book also covers algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. You will also learn how neural networks can be trained and deployed for more accurate predictions, and which Python libraries can be used to implement them.By the end of this book, you will have all the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics.What you will learnUnderstand the importance of statistical modelingLearn about the various Python packages for statistical analysisImplement algorithms such as Naive Bayes, random forests, and moreBuild predictive models from scratch using Python's scikit-learn libraryImplement regression analysis and clusteringLearn how to train a neural network in PythonWho this book is forIf you are a data scientist, a statistician or a machine learning developer looking to train and deploy effective machine learning models using popular statistical techniques, then this book is for you. Knowledge of Python programming is required to get the most out of this book.
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Introduction to Python for Humanists
This book will introduce digital humanists at all levels of education to Python. It provides background and guidance on learning the Python computer programming language, and as it presumes no knowledge on the part of the reader about computers or coding concepts allows the reader to gradually learn the more complex tasks that are currently popular in the field of digital humanities. This book will be aimed at undergraduates, graduates, and faculty who are interested in learning how to use Python as a tool within their workflow. An Introduction to Python for Digital Humanists will act as a primer for students who wish to use Python, allowing them to engage with more advanced textbooks. This book fills a real need, as it is first Python introduction to be aimed squarely at humanities students, as other books currently available do not approach Python from a humanities perspective. It will be designed so that those experienced in Python can teach from it, in addition to allowing those who are interested in being self-taught can use it for that purpose.Key Features: Data analysis Data science Computational humanities Digital humanities Python Natural language processing Social network analysisApp development
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Scientific Data Analysis using Jython Scripting and Java
Scientific Data Analysis using Jython Scripting and Java presents practical approaches for data analysis using Java scripting based on Jython, a Java implementation of the Python language. The chapters essentially cover all aspects of data analysis, from arrays and histograms to clustering analysis, curve fitting, metadata and neural networks. A comprehensive coverage of data visualisation tools implemented in Java is also included.Written by the primary developer of the jHepWork data-analysis framework, the book provides a reliable and complete reference source laying the foundation for data-analysis applications using Java scripting. More than 250 code snippets (of around 10-20 lines each) written in Jython and Java, plus several real-life examples help the reader develop a genuine feeling for data analysis techniques and their programming implementation. This is the first data-analysis and data-mining book which is completely based on the Jython language, and opens doors to scripting using a fully multi-platform and multi-threaded approach. Graduate students and researchers will benefit from the information presented in this book.
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Fundamental Aspects of Operational Risk and Insurance Analytics
A one-stop guide for the theories, applications, and statistical methodologies essential to operational risk Providing a complete overview of operational risk modeling and relevant insurance analytics, Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk offers a systematic approach that covers the wide range of topics in this area. Written by a team of leading experts in the field, the handbook presents detailed coverage of the theories, applications, and models inherent in any discussion of the fundamentals of operational risk, with a primary focus on Basel II/III regulation, modeling dependence, estimation of risk models, and modeling the data elements. Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk begins with coverage on the four data elements used in operational risk framework as well as processing risk taxonomy. The book then goes further in-depth into the key topics in operational risk measurement and insurance, for example diverse methods to estimate frequency and severity models. Finally, the book ends with sections on specific topics, such as scenario analysis; multifactor modeling; and dependence modeling. A unique companion with Advances in Heavy Tailed Risk Modeling: A Handbook of Operational Risk, the handbook also features: Discussions on internal loss data and key risk indicators, which are both fundamental for developing a risk-sensitive frameworkGuidelines for how operational risk can be inserted into a firm’s strategic decisionsA model for stress tests of operational risk under the United States Comprehensive Capital Analysis and Review (CCAR) program A valuable reference for financial engineers, quantitative analysts, risk managers, and large-scale consultancy groups advising banks on their internal systems, the handbook is also useful for academics teaching postgraduate courses on the methodology of operational risk.
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Python and R for the Modern Data Scientist
Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. With this book, data scientists from the Python and R communities will learn how to speak the dialects of each language. By recognizing the strengths of working with both, you'll discover new ways to accomplish data science tasks and expand your skill set.Authors Boyan Angelov and Rick Scavetta explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. Not only will you learn how to use Python and R together in real-world settings, but you'll also broaden your knowledge and job opportunities by working as a bilingual data scientist.Learn Python and R from the perspective of your current languageUnderstand the strengths and weaknesses of each languageIdentify use cases where one language is better suited than the otherUnderstand the modern open source ecosystem available for both, including packages, frameworks, and workflowsLearn how to integrate R and Python in a single workflowFollow a real-world case study that demonstrates ways to use these languages together
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R 1,465.00
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.
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Digital Economy, Business Analytics, and Big Data Analytics Applications
This book is about turning data into smart decisions, knowledge into wisdom and business into business intelligence and insight. It explores diverse paradigms, methodologies, models, tools and techniques of the emerging knowledge domain of digitalized business analytics applications.The book covers almost every crucial aspect of applied artificial intelligence in business, smart mobile and digital services in business administration, marketing, accounting, logistics, finance and IT management.This book aids researchers, practitioners and decisions makers to gain enough knowledge and insight on how to effectively leverage data into competitive intelligence.
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Big Data Analytics
The proposed book will discuss various aspects of big data Analytics. It will deliberate upon the tools, technology, applications, use cases and research directions in the field. Chapters would be contributed by researchers, scientist and practitioners from various reputed universities and organizations for the benefit of readers.
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Python Programming for Linguistics and Text-focussed Digital Humanities
Learn how to use Python for linguistics and digital humanities research, perfect for students working with Python for the first time Python programming is no longer only for computer science students; it is now an essential skill in linguistics, the digital humanities (DH), and social science programs that involve text analytics. Python Programming for Linguistics and Digital Humanities provides a comprehensive introduction to this widely used programming language, offering guidance on using Python to perform various processing and analysis techniques on text. Assuming no prior knowledge of programming, this student-friendly guide covers essential topics and concepts such as installing Python, using the command line, working with strings, writing modular code, designing a simple graphical user interface (GUI), annotating language data in XML and TEI, creating basic visualizations, and more. This invaluable text explains the basic tools students will need to perform their own research projects and tackle various data analysis problems. Throughout the book, hands-on exercises provide students with the opportunity to apply concepts to particular questions or projects in processing textual data and solving language-related issues. Each chapter concludes with a detailed discussion of the code applied, possible alternatives, and potential pitfalls or error messages. Teaches students how to use Python to tackle the types of problems they will encounter in linguistics and the digital humanitiesFeatures numerous practical examples of language analysis, gradually moving from simple concepts and programs to more complex projectsDescribes how to build a variety of data visualizations, such as frequency plots and word cloudsFocuses on the text processing applications of Python, including creating word and frequency lists, recognizing linguistic patterns, and processing words for morphological analysisIncludes access to a companion website with all Python programs produced in the chapter exercises and additional Python programming resourcesPython Programming for Linguistics and Digital Humanities: Applications for Text-Focused Fields is a must-have resource for students pursuing text-based research in the humanities, the social sciences, and all subfields of linguistics, particularly computational linguistics and corpus linguistics.
R 1,235.00
Mathematics of Banking and Finance
Throughout banking, mathematical techniques are used. Some of these are within software products or models; mathematicians use others to analyse data. The current literature on the subject is either very basic or very advanced. The Mathematics of Banking offers an intermediate guide to the various techniques used in the industry, and a consideration of how each one should be approached. Written in a practical style, it will enable readers to quickly appreciate the purpose of the techniques and, through illustrations, see how they can be applied in practice. Coverage is extensive and includes techniques such as VaR analysis, Monte Carlo simulation, extreme value theory, variance and many others. A practical review of mathematical techniques needed in banking which does not expect a high level of mathematical competence from the reader
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Financial Market Analytics
A variety of quantitative concepts and models essential to understanding financial markets are introduced and explained in this broad overview of financial analytical tools designed for financial practitioners, advanced students, and researchers lacking a strong mathematical background. Coverage ranges from matrix mathematics and elementary calculus with their applications to portfolio and fixed income analysis to probability and stochastic processes with their applications to option pricing. The book is sequenced by mathematics topics, most of which are followed by relevant usage to areas such as valuation, risk management, derivatives, back-testing of financial models, and market efficiency.The book begins by motivating the need for understanding quantitative technique with a brief discussion of financial mathematics and financial literature review. Preliminary concepts including geometric expansion, elementary statistics, and basic portfolio techniques are introduced in chapters 2 and 3. Chapters 4 and 5 present matrix mathematics and differential calculus applied to yield curves, APT, state preference theory, binomal option pricing, mean-variance analysis, and other applications. Integral calculus and differential equations follow in chapter 6. The rest of the book covers applications of probability, statistics and stochastic processes as well as a sampling of topics from numerical methods used in financial analysis.
R 3,664.00
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
Statistical Data Analysis for the Physical Sciences
Data analysis lies at the heart of every experimental science. Providing a modern introduction to statistics, this book is ideal for undergraduates in physics. It introduces the necessary tools required to analyse data from experiments across a range of areas, making it a valuable resource for students. In addition to covering the basic topics, the book also takes in advanced and modern subjects, such as neural networks, decision trees, fitting techniques and issues concerning limit or interval setting. Worked examples and case studies illustrate the techniques presented, and end-of-chapter exercises help test the reader's understanding of the material.
R 3,091.15
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
Statistics for Data Scient PB
This book is designed to offer a fast-paced yet thorough introduction to essential statistical concepts using Python code samples, and aims to assist data scientists in their daily endeavors. The ability to extract meaningful insights from data requires a deep understanding of statistics. The book ensures that each topic is introduced with clarity, followed by executable Python code samples that can be modified and applied according to individual needs. Topics include working with data and exploratoryanalysis, the basics of probability, descriptive and inferential statistics and their applications, metrics for data analysis, probability distributions, hypothesis testing, and more. Appendices on Python and Pandas have been included. From foundational Python concepts to the intricacies of statistics, this book serves as a comprehensive resource for both beginners and seasoned professionals.FEATURESProvides Python code samples to ensure readers can immediately apply what they learnCovers everything from basic data handling to advanced statistical conceptsFeatures downloadable companion files with code samples and figuresIncludes two appendices, An Introduction to Python and an Introduction to Pandas as refresher material
R 1,345.00
Transition to Advanced Analytics
Amazon knows the products we’re interested in and shows us more to boost the size of our shopping cart. Google Maps knows the best route to get from A to B and recommends it to get us there in the shortest possible time. Netflix knows the media we enjoy most and recommends more to boost streaming time. However, many companies still stop short with their data analytics practice.This book focuses on how to embed advanced analytics directly into daily business operations and complement an enterprise system. This book can guide you in how traditional industries like retailers, banks and insurers can utilise and develop advanced analytics complementing their enterprise systems whilst embedding advanced analytics directly to optimise revenue and customer experience. Detailed in this book is a world-class analytics application used by loyalty points providers, banks, insurers and leading retailers.The title also provides a step-by-step implementation framework for CDAOs to develop their advanced analytics capabilities and in tandem with legacy IT systems.
R 4,438.58
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
Big Data Analytics Methods
Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.
R 2,371.00
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
Luminescence Signal Analysis Using Python
This book compiles and presents a complete package of open-access Python software code for luminescence signal analysis in the areas of radiation dosimetry, luminescence dosimetry, and luminescence dating. Featuring more than 90 detailed worked examples of Python code, fully integrated into the text, 16 chapters summarize the theory and equations behind the subject matter, while presenting the practical Python codes used to analyze experimental data and extract the various parameters that mathematically describe the luminescence signals. Several examples are provided of how researchers can use and modify the available codes for different practical situations. Types of luminescence signals analyzed in the book are thermoluminescence (TL), isothermal luminescence (ITL), optically stimulated luminescence (OSL), infrared stimulated luminescence (IRSL), timeresolved luminescence (TR) and dose response of dosimetric materials. The open-access Python codes are available at GitHub.The book is well suited to the broader scientific audience using the tools of luminescence dosimetry: physicists, geologists, archaeologists, solid-state physicists, medical physicists, and all scientists using luminescence dosimetry in their research. The detailed code provided allows both students and researchers to be trained quickly and efficiently on the practical aspects of their work, while also providing an overview of the theory behind the analytical equations.
R 7,848.20
Reinforcement Learning for Finance
Reinforcement learning (RL) has led to several breakthroughs in AI. The use of the Q-learning (DQL) algorithm alone has helped people develop agents that play arcade games and board games at a superhuman level. More recently, RL, DQL, and similar methods have gained popularity in publications related to financial research.This book is among the first to explore the use of reinforcement learning methods in finance.Author Yves Hilpisch, founder and CEO of The Python Quants, provides the background you need in concise fashion. ML practitioners, financial traders, portfolio managers, strategists, and analysts will focus on the implementation of these algorithms in the form of self-contained Python code and the application to important financial problems.This book covers:Reinforcement learningDeep Q-learningPython implementations of these algorithmsHow to apply the algorithms to financial problems such as algorithmic trading, dynamic hedging, and dynamic asset allocationThis book is the ideal reference on this topic. You'll read it once, change the examples according to your needs or ideas, and refer to it whenever you work with RL for finance.
R 1,596.00
R 562.00
R 1,069.00
