Search results for "Introduction to Data Analysis Using IBM SPSS"
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Data Analysis in Bi-partial Perspective: Clustering and Beyond
This book presents the bi-partial approach to data analysis, which is both uniquely general and enables the development of techniques for many data analysis problems, including related models and algorithms. It is based on adequate representation of the essential clustering problem: to group together the similar, and to separate the dissimilar. This leads to a general objective function and subsequently to a broad class of concrete implementations. Using this basis, a suboptimising procedure can be developed, together with a variety of implementations.This procedure has a striking affinity with the classical hierarchical merger algorithms, while also incorporating the stopping rule, based on the objective function. The approach resolves the cluster number issue, as the solutions obtained include both the content and the number of clusters. Further, it is demonstrated how the bi-partial principle can be effectively applied to a wide variety of problems in data analysis.The book offers a valuable resource for all data scientists who wish to broaden their perspective on basic approaches and essential problems, and to thus find answers to questions that are often overlooked or have yet to be solved convincingly. It is also intended for graduate students in the computer and data sciences, and will complement their knowledge and skills with fresh insights on problems that are otherwise treated in the standard “academic” manner.
R 3,389.96
Data Analysis in Bi-partial Perspective: Clustering and Beyond
This book presents the bi-partial approach to data analysis, which is both uniquely general and enables the development of techniques for many data analysis problems, including related models and algorithms. It is based on adequate representation of the essential clustering problem: to group together the similar, and to separate the dissimilar. This leads to a general objective function and subsequently to a broad class of concrete implementations. Using this basis, a suboptimising procedure can be developed, together with a variety of implementations.This procedure has a striking affinity with the classical hierarchical merger algorithms, while also incorporating the stopping rule, based on the objective function. The approach resolves the cluster number issue, as the solutions obtained include both the content and the number of clusters. Further, it is demonstrated how the bi-partial principle can be effectively applied to a wide variety of problems in data analysis.The book offers a valuable resource for all data scientists who wish to broaden their perspective on basic approaches and essential problems, and to thus find answers to questions that are often overlooked or have yet to be solved convincingly. It is also intended for graduate students in the computer and data sciences, and will complement their knowledge and skills with fresh insights on problems that are otherwise treated in the standard “academic” manner.
R 3,590.79
Hands-On Time Series Analysis with R
Master modern time series forecasting in R using real datasets, applied models like ARIMA and Prophet, and advanced tools like LSTM for business-ready analysisKey FeaturesBuild a practical skills in time series forecasting using R from scratchLevereage traditional and deep learning models including ARIMA, Prophet, and LSTMAnalyze and visualize real-world datasets to drive decisions and discover insightsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionHands-on Time Series Analysis with R, Second Edition, is a comprehensive, practical guide to understanding and applying time series analysis using R. Designed for professionals and students alike, the book will help you dissect time-ordered data, uncover trends, detect anomalies, and forecast future events. You’ll start with R fundamentals and data structures, progressing through time series visualization and decomposition,before covering classical forecasting methods like exponential smoothing and ARIMA. You’ll also familiarize yourself with cutting-edge forecasting techniques such as Bayesian modeling, TBATS, and multivariate forecasting. Building upon the extraordinary success of the first edition, this new edition explores the powerful Prophet library, neural networks, and LSTM models to handle complex forecasting challenges. Unlike other similar books, it uses real-world case studies from domains like e-commerce and supply chain make the content highly applicable, while self-assessment questions and step-by-step tutorials reinforce key concepts. By the end of this book, you’ll be able to confidently apply a wide array of forecasting methods to solve real-world business and research problems.What you will learnWork with time series data to uncover underlying patterns and trendsImplement forecasting models like ARIMA and Prophet to predict future valuesCreate powerful data visualizations to communicate time series insightsLeverage R for time-based analysis across various domainsBuild and fine-tune models for different data scenariosDetect anomalies and adjust models for improved accuracyUse deep learning techniques like LSTM for complex time series tasksWho this book is forThis book is for junior to mid-level professionals in business intelligence, finance, and supply chain roles, as well as academics and students interested in data science. No prior expertise in time series analysis is required, but a working knowledge of basic R programming is helpful.
R 1,465.00
Complex Data Analytics with Formal Concept Analysis
FCA is an important formalism that is associated with a variety of research areas such as lattice theory, knowledge representation, data mining, machine learning, and semantic Web. It is successfully exploited in an increasing number of application domains such as software engineering, information retrieval, social network analysis, and bioinformatics. Its mathematical power comes from its concept lattice formalization in which each element in the lattice captures a formal concept while the whole structure represents a conceptual hierarchy that offers browsing, clustering and association rule mining.Complex data analytics refers to advanced methods and tools for mining and analyzing data with complex structures such as XML/Json data, text and image data, multidimensional data, graphs, sequences and streaming data. It also covers visualization mechanisms used to highlight the discovered knowledge.This edited book examines a set of important and relevant research directions in complex data management, and updates the contribution of the FCA community in analyzing complex and large data such as knowledge graphs and interlinked contexts. For example, Formal Concept Analysis and some of its extensions are exploited, revisited and coupled with recent processing parallel and distributed paradigms to maximize the benefits in analyzing large data.
R 7,551.70
Big Data: Conceptual Analysis and Applications
The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used. Application of hybrid neuro-fuzzy networks for analyzing stock markets was presented. The analysis of big historical, economic and physical data revealed the hidden Fibonacci pattern about the course of systemic world conflicts and their connection with the Kondratieff big economic cycles and the Schwabe–Wolf solar activity cycles. The book is useful for system analysts and practitioners working with complex systems in various spheres of human activity.
R 5,028.92
Data-Centric Business and Applications
Building on the authors’ previous work, this book addresses key processes and procedures used in information/data processing and management. Modern methods of business information processing, which draw on artificial intelligence, big data, and cloud-based storage and processing, are opening exciting new opportunities for doing business on the basis of information technologies. Thus, in this third book, the authors continue to explore various aspects – technological as well as business and social – of the information industries. Further, they analyze the challenges and opportunities entailed by these kinds of business.
R 2,327.78
New Paradigms in Big Data Technology and Business Analytics
"New Paradigms in Big Data Technology and Business Analytics" emphasize the integration of Big Data Technology and Business Analytics enables organizations to transform vast volumes of data into strategic insights, driving smarter decisions, operational efficiency, and innovative growth. This volume presents a comprehensive exploration of the evolving landscape of Big Data Technology and Business Analytics, showcasing transformative approaches that are reshaping industries and research alike. It highlights the integration of advanced tools such as artificial intelligence, machine learning, data mining, and cloud infrastructure to drive intelligent decision-making and operational efficiency. Focusing on both theoretical frameworks and real-world case studies, this book explores into five essential domains: Big Data Infrastructure and Technologies, Data Mining and Machine Learning, Big Data Applications and Case Studies, Business Intelligence and Decision Support, and Data Governance and Ethics. By merging theoretical knowledge with practical applications, this book equips educators, researchers, practitioners, and students with actionable insights into how data-driven strategies can unlock sustainable growth and innovation across sectors. With a forward-thinking vision, it addresses key issues like data privacy, algorithmic fairness, and strategic deployment of analytics in diverse environments. By blending emerging technologies with practical applications, this book serves as a roadmap for anyone aiming to harness the full potential of big data to reshape modern business and society.
R 9,158.00
World of Business with Data and Analytics
This book covers research work spanning the breadth of ventures, a variety of challenges and the finest of techniques used to address data and analytics, by subject matter experts from the business world. The content of this book highlights the real-life business problems that are relevant to any industry and technology environment. This book helps us become a contributor to and accelerator of artificial intelligence, data science and analytics, deploy a structured life-cycle approach to data related issues, apply appropriate analytical tools & techniques to analyze data and deliver solutions with a difference. It also brings out the story-telling element in a compelling fashion using data and analytics. This prepares the readers to drive quantitative and qualitative outcomes and apply this mindset to various business actions in different domains such as energy, manufacturing, health care, BFSI, security, etc.
R 3,490.34
Recent Advancements in Multi-View Data Analytics
This book provides timely studies on multi-view facets of data analytics by covering recent trends in processing and reasoning about data originating from an array of local sources. A multi-view nature of data analytics is encountered when working with a variety of real-world scenarios including clustering, consensus building in decision processes, computer vision, knowledge representation, big data, data streaming, among others.The chapters demonstrate recent pursuits in the methodology, theory, advanced algorithms, and applications of multi-view data analytics and bring new perspectives of data interpretation. The timely book will appeal to a broad readership including both researchers and practitioners interested in gaining exposure to the rapidly growing trend of multi-view data analytics and intelligent systems.
R 8,460.96
Big Data Management and Analysis for Cyber Physical Systems
This book consists of selected and peer-reviewed papers presented at 2022 4th International Conference on Big Data Engineering and Technology (BDET), held during April 22–24, 2022, in Singapore. As IT infrastructure and data management technologies have become critical assets and capabilities for today’s enterprises, this book aims to be part of the effort in contributing to their development. In particular, the BDET conference series aims to provide the much needed forum for researchers and practitioners across the world who are actively engaged in advancing research and raising awareness of the many challenges in the diverse field of big data engineering and technology to share their research outcomes and bounce ideas off their international colleagues. Over the last few years, the conference series has brought together the latest developments of novel theory in big data, algorithm and applications, emerging standards for big data, big data infrastructure, MapReduce and cloud computing, big data visualization, big data curation and management, big data semantics, scientific discovery and intelligence, which collectively form parts of the cyber-physical systems of interest. It is hoped that the book will prove useful to students, researchers, and professionals working in the field of big data engineering and applications in cyber-physical systems.
R 7,245.25
Data-Centric Business and Applications
This book explores various aspects of software creation and development as well as data and information processing. It covers relevant topics such as business analysis, business rules, requirements engineering, software development processes, software defect prediction, information management systems, and knowledge management solutions. Lastly, the book presents lessons learned in information and data management processes and procedures.
R 2,015.00
Algebraic Approach to Data Processing
The book explores a new general approach to selecting—and designing—data processing techniques. Symmetry and invariance ideas behind this algebraic approach have been successful in physics, where many new theories are formulated in symmetry terms.The book explains this approach and expands it to new application areas ranging from engineering, medicine, education to social sciences. In many cases, this approach leads to optimal techniques and optimal solutions. That the same data processing techniques help us better analyze wooden structures, lung dysfunctions, and deep learning algorithms is a good indication that these techniques can be used in many other applications as well. The book is recommended to researchers and practitioners who need to select a data processing technique—or who want to design a new technique when the existing techniques do not work. It is also recommended to students who want to learn the state-of-the-art data processing.
R 3,595.24
International Conference on Deep Learning, Big Data and Blockchain (DBB 2022)
Deep and machine learning is the state-of-the-art at providing models, methods, tools and techniques for developing autonomous and intelligent systems which can revolutionise industrial and commercial applications in various fields such as online commerce, intelligent transportation, healthcare and medicine, etc. The ground-breaking technology of blockchain also enables decentralisation, immutability, and transparency of data and applications.This event aims to enable synergy between these areas and provide a leading forum for researchers, developers, practitioners, and professionals from public sectors and industries to meet and share the latest solutions and ideas in solving cutting-edge problems in the modern information society and the economy. The conference focuses on specific challenges in deep (and machine) learning, big data and blockchain. Some of the key topics of interest include (but are not limited to):Deep/Machine learning based models Statistical models and learning Data analysis, insights and hidden pattern Data visualisation Security threat detection Data classification and clustering Blockchain security and trust Blockchain data management
R 7,019.74
