Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools Illustrations of how to use the outlined concepts in real-world situations Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.

Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics. This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management.

Applied Statistics for Environmental Science with R presents the theory and application of statistical techniques in environmental science and aids researchers in choosing the appropriate statistical technique for analyzing their data. Focusing on the use of univariate and multivariate statistical methods, this book acts as a step-by-step resource to facilitate understanding in the use of R statistical software for interpreting data in the field of environmental science. Researchers utilizing statistical analysis in environmental science and engineering will find this book to be essential in solving their day-to-day research problems.

Neutrosophic Set in Medical Image Analysis gives an understanding of the concepts of NS, along with knowledge on how to gather, interpret, analyze and handle medical images using NS methods. It presents the latest cutting-edge research that gives insight into neutrosophic set's novel techniques, strategies and challenges, showing how it can be used in biomedical diagnoses systems. The neutrosophic set (NS), which is a generalization of fuzzy set, offers the prospect of overcoming the restrictions of fuzzy-based approaches to medical image analysis.

Get the SAP Fiori launchpad up and running! Configure the SAP Fiori launchpad for key on-premise systems: SAP S/4HANA, SAP Business Suite, and SAP Enterprise Portal. Use the SAP Launchpad service in the cloud by setting up SAP Business Technology Platform (SAP BTP) and leveraging tools like SAP Business Application Studio. Follow a real-world landscape example and make the most of SAP Fiori's modern UX!In this book, you'll learn about:a. User Experience Discover the SAP Fiori user experience: design principles, launchpad components, personas, and more. Get a big-picture view of SAP's UX architecture and set the stage with a sample landscape. b. Configuration Follow practical instructions to configure the SAP Fiori launchpad for on-premise SAP backends. Then implement the SAP Fiori launchpad in the cloud by configuring the SAP Launchpad service on SAP BTP and connecting to on-premise systems using the cloud connector. c. Cloud Services See how to create plugins for the SAP Launchpad service using SAP Business Application Studio. Integrate and use cloud services like SAP Web Analytics and SAP Conversational AI to optimize the SAP Fiori launchpad user experience. Highlights include:1) User experience architecture2) Integration 3) SAP S/4HANA4) SAP Business Suite5) SAP Enterprise Portal6) SAP Business Technology Platform (SAP BTP)7) SAP Launchpad service8) SAP Business Application Studio9) SAP Web Analytics10) SAP Conversational AICloud connectorSAP Mobile Start app

When it comes to data migration, choosing the right approach for your organization can be overwhelming. This comprehensive guide not only leads you through project planning, but also gives you step-by-step instructions for executing your migration with LSMW, SAP Data Services, the batch input technique, and more. Whether you're moving to the cloud, migrating to SAP S/4HANA, or replacing a legacy system, let us smooth the way. Planning and Selection Criteria: Get the tools you need to plan your data migration, from project management to terminology and procedure, to criteria for selecting the best migration tool for the job. Migration Methods: Understand the tools and methods for data migration without programming: batch input, IDocs, ALE, the LSMW, SAP Data Services, and SAP Activate. Cloud Migration and SAP S/4HANA: Explore options for migration to the cloud with SAP Business ByDesign and SAP Cloud for Customer. Then, discover pathways for data migration to SAP S/4HANA. Highlights: Legacy data Migration planning Batch input IDocs and ALE Legacy System Migration Workbench (LSMW) SAP Data Services Rapid Data Migration Cloud migration SAP S/4HANA SAP Activate

Intelligent Speech Signal Processing investigates the utilization of speech analytics across several systems and real-world activities, including sharing data analytics, creating collaboration networks between several participants, and implementing video-conferencing in different application areas. Chapters focus on the latest applications of speech data analysis and management tools across different recording systems. The book emphasizes the multidisciplinary nature of the field, presenting different applications and challenges with extensive studies on the design, development and management of intelligent systems, neural networks and related machine learning techniques for speech signal processing.

Excel has become an important and nearly ubiquitous classroom and office resource for students and practitioners who are faced with solving statistical problems on an everyday basis. Despite this, there has yet to emerge a truly practical, "how-do-I-do-it" manual that teaches the various applications and processes/formulas for Excel in educational and psychological Statistics. Quirk's Excel 2010 for Educational and Psychological Statistics will fill this void, as it is designed to be a step-by-step, exercise-driven guide for education and psychology students who need to master Excel to create formulas and solve statistical problems. Each chapter first explains briefly the formulas that are included in the chapter, and then directs the student on how to use Excel commands and formulas to solve a specific business problem. Three practice problems are provided at the end of each chapter, along with their solutions in an Appendix. At the end of the Excel Guide, an additional Practice Exam allows the reader to test his or her understanding of each chapter by attempting to solve a specific educational or psychometrical issue or problem using Excel (the solution to this problem is also given in an Appendix). From the beginning of the book, readers/students are taught how to write their own formulas and then how to utilize Excel drop-down formula menus as well for such exercises involving one-way ANOVA, simple linear regression, and multiple correlation.

Essentials of Programming in Mathematica (R) provides an introduction suitable for readers with little or no background in the language as well as for those with some experience using programs such as C, Java, or Perl. The author, an established authority on Mathematica (R) programming, has written an example-driven text that covers the language from first principles, as well as including material from natural language processing, bioinformatics, graphs and networks, signal analysis, geometry, computer science, and many other applied areas. The book is appropriate for self-study or as a text for a course in programming in computational science. Readers will benefit from the author's tips, which provide insight and suggestions on small and large points. He also provides more than 350 exercises from novice through to advanced level with all of the solutions available online.

This textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector. An expansion of the quantitative economics frameworks presented in policy and business schools, this book emphasizes the process of asking relevant questions to inform public policy. Its techniques and approaches emphasize data-driven practices, beginning with the basic programming paradigms that occupy the majority of an analyst's time and advancing to the practical applications of statistical learning and machine learning. The text considers two divergent, competing perspectives to support its applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live code, written in R and presented in notebook form, which readers can use and modify to practice working with data.

Get the most out of your Materials Management implementation with this updated, comprehensive guide to configuration and functionality. You'll learn the ins and outs of Materials Management in SAP, from goods receipt and invoice verification to early warning systems and special procurement types. Dive into master data and other configuration tasks to ensure your MM system is optimized for your logistics needs! Materials Management Functionality: Learn to use MM for essential tasks like requesting quotations, purchase orders, and inventory management and for more advanced tasks like forecasting, using classification systems, and handling special procurement types. Business Scenarios and Examples: Configure settings for critical processes, and explore master data for materials and vendors. Cloud Migration and SAP S/4HANA: Expand your understanding of MM processes with expert advice, real-world examples, and sample business scenarios. Highlights: Materials master data Vendor master data Purchasing information data Batch management External Services Management Material requirements planning (MRP) Inventory management Goods issue/goods receipt (GI/GR) Material Ledger Balance sheet valuation Invoice verification Document management

Growing Your Career with Social Media presents social media tools, current trends and professional development strategies to help busy librarians remain up-to-date. This title offers advice from librarians on how to use social media for career development and continuing education. Advice is based on accumulated experience from professionals who have incorporated social media into their professional lives. The book includes interviews and suggests ways librarians can use social media as a tool for self-promotion. It includes tables of social media tools and their potential uses, and also provides resources, lists, organizations and information on librarians currently active in social media.

Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition updates and expands on the first edition, bringing a set of techniques and algorithms that are tailored to Big Data projects. The book stresses the point that most data analyses conducted on large, complex data sets can be achieved without the use of specialized suites of software (e.g., Hadoop), and without expensive hardware (e.g., supercomputers). The core of every algorithm described in the book can be implemented in a few lines of code using just about any popular programming language (Python snippets are provided). Through the use of new multiple examples, this edition demonstrates that if we understand our data, and if we know how to ask the right questions, we can learn a great deal from large and complex data collections. The book will assist students and professionals from all scientific backgrounds who are interested in stepping outside the traditional boundaries of their chosen academic disciplines.

Transportation Policy and Economic Regulation: Essays in Honor of Theodore Keeler addresses a number of today's important transportation policy issues, exploring a variety of transportation modes, and examining the policy implications of a number of alternatives. Theodore Keeler had a distinguished career in transportation economics, helping to shape regulatory policies concerning the transportation industries and assessing the appropriateness of various policies. A distinguishing feature of his work is that it always had policy implications. As a tribute to Theodore Keeler, this book examines transportation policy issues across a variety of transportation industries, including aviation, railroads, highways, motor carrier transport, automobiles, urban transit, and ocean shipping. The book evaluates the economic impact and effectiveness of various policies, employing empirical analyses and new estimation techniques, such as Bayesian analysis. The book is designed for transportation professionals and researchers, as well as transportation economics students, providing an in-depth analysis of some of today's important transportation policy issues. Policy changes established in the last 35-40 years have introduced profound changes in the business environment of the transportation industry. Past policy changes promoted the free market's role in setting prices and determining service availability. While 21st century policy has focused on a variety of other issues, such as safety, road and air congestion, productivity growth, labor relations and exhaust emission, many still promote the role of competition. In addition to examining various transportation policy issues in the U.S., the book explores some approaches to dealing with transportation issues in different parts of the world. Contemporary transportation policy debates have broadened from their initial focus of primarily examining the merits of reforming economic regulations at national levels, to now examining a variety of issues such as alternative methods of social regulation (such as safety regulation and emission controls), new approaches to changing economic regulations, the potential for reforming international regulations, and the appropriate role for government in transportation.

Scientific computing is the study of how to use computers effectively to solve problems that arise from the mathematical modeling of phenomena in science and engineering. It is based on mathematics, numerical and symbolic/algebraic computations and visualization. This book serves as an introduction to both the theory and practice of scientific computing, with each chapter presenting the basic algorithms that serve as the workhorses of many scientific codes; we explain both the theory behind these algorithms and how they must be implemented in order to work reliably in finite-precision arithmetic. The book includes many programs written in Matlab and Maple - Maple is often used to derive numerical algorithms, whereas Matlab is used to implement them. The theory is developed in such a way that students can learn by themselves as they work through the text. Each chapter contains numerous examples and problems to help readers understand the material "hands-on".

Explore the architecture, product offerings, and the various stages of implementation processes in Azure DevOps. The book starts with the basic concepts of DevOps and moves on to discuss project management in Azure DevOps. Next, you will learn requirement management and version control in DevOps. Along the way, you will go through test management followed by continuous integration and build automation with more details on code quality and security implementations. Moving forward, you will learn release pipeline and infrastructure as code implementation including ARM-based environment provisioning and execution. Finally, you'll cover DevOps architecture blueprints used for deploying your web applications to different platforms . After reading this book, you will be able to understand each stage of Azure DevOps and master its implementation. What You Will Learn Understand the various concepts of Azure DevOps Apply DevOps concepts in a variety of application contexts including web applications, containers, and database Understand the implementation of end-to-end DevOps in Azure Work with the different DevOps design patterns and architectures in Azure Who Is This Book For: Developers and architects working with Azure.

Now with a new companion website! Using IBM (R) SPSS (R) Statistics: An Interactive Hands-On Approach, Third Edition gives readers an accessible and comprehensive guide to walking through SPSS (R), providing them with step-by-step knowledge for effectively analyzing their data. From entering data to working with existing databases, and working with the help menu through performing factor analysis, Using IBM (R) SPSS (R) Statistics covers every aspect of SPSS (R) from introductory through intermediate statistics. The book is divided into parts that focus on mastering SPSS (R) basics, dealing with univariate statistics and graphing, inferential statistics, relational statistics, and more. Written using IBM (R) SPSS (R) version 25 and 24, and compatible with the earlier releases, this book is one of the most comprehensive SPSS (R) guides available. Bundle Using IBM (R) SPSS (R) Statistics: An Interactive Hands-On Approach with SAGE IBM (R) SPSS (R) Statistics v24.0 Student Version and SAVE! - Bundle ISBN: 978-1-5443-5071-4

There have been many improvements to QuickBooks over the years, but the program's documentation is not one of them. Luckily, QuickBooks 2008: The Missing Manual picks up where QuickBook's help resources leave off. With this book, you don't just learn how to use the software, you learn why and when to use specific features. And you get basic accounting advice so that it all makes sense to you along the way. With its Simple Start, Basic, Pro, Premier, and industry-specific Enterprise editions, QuickBooks can handle many of the financial tasks companies face, but the price you pay is an overabundance of features. With this book, you get advice on which features you need to use to get your work done efficiently, along with step-by-step instructions on how to use them. QuickBooks 2008: The Missing Manual helps you: *Get more out of QuickBooks whether you're a beginner or an old pro. *Learn how QuickBooks can help you boost sales, control spending, and save on taxes. *Set up and manage your files to fit your company's specific needs. *Use QuickBooks reports to evaluate every aspect of your enterprise. *Follow the money all the way from customer invoices to year-end tasks. *Discover new tips and tricks on the best timesaving options for your business. *Build budgets and plan for the future to make your business more successful. And a lot more. This book is designed to accommodate readers at every technical level. If you're a first-time QuickBooks user, special boxes with the title "Up To Speed" provide the introductory information you need to understand the topic at hand. For advanced users, there are similar boxes called "Power Users' Clinic" that offer more technical tips, tricks, and shortcuts for the experienced QuickBooks fan. For a topic as complicated as accounting software, why trust anything else?

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas-from science and engineering, to medicine, academia and commerce.

This book deals with selected problems of machine perception, using various 2D and 3D imaging sensors. It proposes several new original methods, and also provides a detailed state-of-the-art overview of existing techniques for automated, multi-level interpretation of the observed static or dynamic environment. To ensure a sound theoretical basis of the new models, the surveys and algorithmic developments are performed in well-established Bayesian frameworks. Low level scene understanding functions are formulated as various image segmentation problems, where the advantages of probabilistic inference techniques such as Markov Random Fields (MRF) or Mixed Markov Models are considered. For the object level scene analysis, the book mainly relies on the literature of Marked Point Process (MPP) approaches, which consider strong geometric and prior interaction constraints in object population modeling. In particular, key developments are introduced in the spatial hierarchical decomposition of the observed scenarios, and in the temporal extension of complex MRF and MPP models. Apart from utilizing conventional optical sensors, case studies are provided on passive radar (ISAR) and Lidar-based Bayesian environment perception tasks. It is shown, via several experiments, that the proposed contributions embedded into a strict mathematical toolkit can significantly improve the results in real world 2D/3D test images and videos, for applications in video surveillance, smart city monitoring, autonomous driving, remote sensing, and optical industrial inspection.

Using an approach that author Alan Laub calls "matrix analysis for grown-ups", this textbook introduces fundamental concepts of numerical linear algebra and their application to solving certain numerical problems arising in state-space control and systems theory. It is written for advanced undergraduate and beginning graduate students and can be used as a follow-up to Matrix Analysis for Scientists and Engineers (SIAM, 2005), a compact single-semester introduction to matrix analysis for engineers and computational scientists by the same author. Computational Matrix Analysis provides readers with:* A one-semester introduction to numerical linear algebra.* An introduction to statistical condition estimation in book form for the first time.* An overview of certain computational problems in control and systems theory. The book features a number of elements designed to help students learn to use numerical linear algebra in day-to-day computing or research, including:* A brief review of matrix analysis, including notation, and an introduction to finite (IEEE) arithmetic.* Discussion and examples of conditioning, stability, and rounding analysis.* An introduction to mathematical software topics related to numerical linear algebra.* A thorough introduction to Gaussian elimination, along with condition estimation techniques.* Coverage of linear least squares, with orthogonal reduction and QR factorization.* Variants of the QR algorithm.* Applications of the discussed algorithms.

Application Performance Management (APM) in the Digital Enterprise enables IT professionals to be more successful in managing their company's applications. It explores the fundamentals of application management, examines how the latest technological trends impact application management, and provides best practices for responding to these changes. The recent surge in the use of containers as a way to simplify management and deploy applications has created new challenges, and the convergence of containerization, cloud, mobile, virtualization, analytics, and automation is reshaping the requirements for application management. This book serves as a guide for understanding these dramatic changes and how they impact the management of applications, showing how to create a management strategy, define the underlying processes and standards, and how to select the appropriate tools to enable management processes.