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Principles and Practice of Multi-Agent Systems
by Matteo Baldoni Cristina Baroglio Floris Bex Floriana Grasso Nancy Green Mohammad-Reza Namazi-Rad Masayuki Numao Merlin Teodosia SuarezThis book constitutes the thoroughly refereed post-workshop proceedings of the 5th International Workshop on Empathic Computing, IWEC 2014, co-loacted with PRICAI 2014, held in Gold Coast, QLD, Australia, in December 2014, as well as the 6th International Workshop on Empathic Computing, IWEC 2015, and the 15th Workshop on Computational Models of Natural Argument, CMNA XV, both co-located with PRIMA 2015, held in Bertinoro, Italy, in October 2015. The 12 papers presented were carefully reviewed and selected from 32 initial submissions. The workshops are going alongside with the PRIMA 2015 Conference and are intended to facilitate active exchange, interaction and comparison of approaches, methods and various ideas in specific areas related to intelligent agent systems and multiagent systems.
Principles and Practices of CAD/CAM
by Vikrant Sharma Vikram Sharma Om Ji ShuklaCAD/CAM systems are perhaps the most crucial advancement in the field of new technology relating to engineering, design and drawing in all technical domains. CAD/CAM stands for computer-aided design and computer-aided manufacturing. These systems are useful in all facets of contemporary design and architecture. The fundamentals of CAD/CAM systems are covered in detail throughout this book. This book aims to introduce the fundamental aspects, complete with an adequate numberof illustrations and examples, without delving too deeply into the specifics of the subject matter. This book is valuable in the classroom for both teachers and students. Features Each chapter begins with the Learning Outcomes (LOs) section, which highlights the critical points of that chapter. All LOs, solved examples, and questions are mapped to six Bloom Taxonomy levels (BT levels). Offers fundamental concepts of CAD/CAM without becoming too complicated. Solved examples are presented in each section after the theoretical discussion to clarify the concept of that section. Chapter-end summaries reinforce key ideas and help readers recall the concepts discussed. Students and professionals need to have a working knowledge of CAD/CAM since it has many applications and continues to expand. Students at the undergraduate and graduate levels of engineering courses use this book as their primary textbook. It will also be helpful for managers, consultants, and professionals.
Principles and Structures of FPGAs
by Hideharu AmanoThis comprehensive textbook on the field programmable gate array (FPGA) covers its history, fundamental knowledge, architectures, device technologies, computer-aided design technologies, design tools, examples of application, and future trends. Programmable logic devices represented by FPGAs have been rapidly developed in recent years and have become key electronic devices used in most IT products. This book provides both complete introductions suitable for students and beginners, and high-level techniques useful for engineers and researchers in this field. Differently developed from usual integrated circuits, the FPGA has unique structures, design methodologies, and application techniques. Allowing programming by users, the device can dramatically reduce the rising cost of development in advanced semiconductor chips. The FPGA is now driving the most advanced semiconductor processes and is an all-in-one platform combining memory, CPUs, and various peripheral interfaces. This book introduces the FPGA from various aspects for readers of different levels. Novice learners can acquire a fundamental knowledge of the FPGA, including its history, from Chapter 1; the first half of Chapter 2; and Chapter 4. Professionals who are already familiar with the device will gain a deeper understanding of the structures and design methodologies from Chapters 3 and 5. Chapters 6–8 also provide advanced techniques and cutting-edge applications and trends useful for professionals. Although the first parts are mainly suitable for students, the advanced sections of the book will be valuable for professionals in acquiring an in-depth understanding of the FPGA to maximize the performance of the device.
Principles in Noisy Optimization: Applied to Multi-agent Coordination (Cognitive Intelligence and Robotics)
by Amit Konar Pratyusha RakshitNoisy optimization is a topic of growing interest for researchers working on mainstream optimization problems. Although several techniques for dealing with stochastic noise in optimization problems are covered in journals and conference proceedings, today there are virtually no books that approach noisy optimization from a layman’s perspective; this book remedies that gap. Beginning with the foundations of evolutionary optimization, the book subsequently explores the principles of noisy optimization in single and multi-objective settings, and presents detailed illustrations of the principles developed for application in real-world multi-agent coordination problems. Special emphasis is given to the design of intelligent algorithms for noisy optimization in real-time applications. The book is unique in terms of its content, writing style and above all its simplicity, which will appeal to readers with a broad range of backgrounds.The book is divided into 7 chapters, the first of which provides an introduction to Swarm and Evolutionary Optimization algorithms. Chapter 2 includes a thorough review of agent architectures for multi-agent coordination. In turn, Chapter 3 provides an extensive review of noisy optimization, while Chapter 4 addresses issues of noise handling in the context of single-objective optimization problems. An illustrative case study on multi-robot path-planning in the presence of measurement noise is also highlighted in this chapter. Chapter 5 deals with noisy multi-objective optimization and includes a case study on noisy multi-robot box-pushing. In Chapter 6, the authors examine the scope of various algorithms in noisy optimization problems. Lastly, Chapter 7 summarizes the main results obtained in the previous chapters and elaborates on the book’s potential with regard to real-world noisy optimization problems.
Principles of AI Governance and Model Risk Management: Master the Techniques for Ethical and Transparent AI Systems
by James SaylesNavigate the complex landscape of Artificial Intelligence (AI) governance and model risk management using a holistic approach encompassing people, processes, and technology. This book provides practical guidance, oversight structure and centers of excellence, and actionable insights for organizations seeking to harness the power of AI responsibly, ethically, and transparently. By addressing the technical, ethical, and societal dimensions of AI governance, organizations will be empowered to build trustworthy AI systems that benefit both their bottom line and the broader community. Featuring successful mitigating controls based on proven use cases, the book underscores the importance of aligning AI strategy with AI governance, striking a balance between AI innovation, risk mitigation as well as broader business goals. You’ll receive pointers for designing a well-governed AI development lifecycle, emphasizing transparency, accountability, and continuous monitoring throughout the AI development lifecycle. This book highlights the importance of collaboration between stakeholders, i.e., boards of directors, CxOs, corporate counsel, compliance officers, audit executives, data scientists, developers, validators, etc. You’ll gain practical advice on addressing the challenges related to the ownership of AI-generated content and models, stressing the need for legal frameworks and international collaboration. You’ll also learn the importance of auditing AI systems, developing protocols for rapid response in case of AI-related crises, and building capacity for AI actors through education. Principles of AI Governance and Model Risk Management demonstrates its value-added uniqueness by detailing a strategy to ensure a cohesive approach to managing AI-related risks, global compliance, policy, privacy, and AI-human collaboration and oversight. What You Will Learn Different approaches to AI adoption, from building in-house AI capabilities to partnering with external providers Key factors to consider when choosing an AI solution and how to ensure its successful integration into existing workflows AI technologies, their business impact, and ethical considerations to make informed decisions and foster responsible AI The environmental impacts of AI systems and the need for sustainable practices in AI development and deployment. Who This Book is For Business executives and process owners/representatives, risk officers, cybersecurity professionals, legal counsel and ethics officers, human resource professionals, data scientists, AI developers, and CTOs.
Principles of Abstract Interpretation
by Patrick CousotIntroduction to abstract interpretation, with examples of applications to the semantics, specification, verification, and static analysis of computer programs.Formal methods are mathematically rigorous techniques for the specification, development, manipulation, and verification of safe, robust, and secure software and hardware systems. Abstract interpretation is a unifying theory of formal methods that proposes a general methodology for proving the correctness of computing systems, based on their semantics. The concepts of abstract interpretation underlie such software tools as compilers, type systems, and security protocol analyzers. This book provides an introduction to the theory and practice of abstract interpretation, offering examples of applications to semantics, specification, verification, and static analysis of programming languages with emphasis on calculational design. The book covers all necessary computer science and mathematical concepts--including most of the logic, order, linear, fixpoint, and discrete mathematics frequently used in computer science--in separate chapters before they are used in the text. Each chapter offers exercises and selected solutions. Chapter topics include syntax, parsing, trace semantics, properties and their abstraction, fixpoints and their abstractions, reachability semantics, abstract domain and abstract interpreter, specification and verification, effective fixpoint approximation, relational static analysis, and symbolic static analysis. The main applications covered include program semantics, program specification and verification, program dynamic and static analysis of numerical properties and of such symbolic properties as dataflow analysis, software model checking, pointer analysis, dependency, and typing (both for forward and backward analysis), and their combinations. Principles of Abstract Interpretation is suitable for classroom use at the graduate level and as a reference for researchers and practitioners.
Principles of Applied Engineering
by Elizabeth A. Stephan Kenneth Reid David ReepingNIMAC-sourced textbook
Principles of Automated Drafting (Mechanical Engineering Ser. #28)
by Daniel L. RyanThis book introduces the reader to each phase of the subject, step-by-step to enable one to use the various automated drafting devices, instruments and technique of application. It shows the way to produce acceptable drafting in the framework of high productivity.
Principles of Blended Learning: Shared Metacognition and Communities of Inquiry (Issues in Distance Education)
by D. Randy Garrison Norman D. Vaughan Martha Cleveland-Innes Deborah DellThe rapid migration to remote instruction during the Covid-19 pandemic has expedited the need for more research, expertise, and practical guidelines for online and blended learning. A theoretical grounding of approaches and practices is imperative to support blended learning and sustain change across multiple levels in education organizations, from leadership to classroom. The Community of Inquiry is a valuable framework that regards higher education as both a collaborative and individually constructivist learning experience. The framework considers the interdependent elements of social, cognitive, and teaching presence to create a meaningful learning experience. In this volume, the authors further explore and refine the blended learning principles presented in their first book, Teaching in Blended Learning Environments: Creating and Sustaining Communities of Inquiry, with an added focus on designing, facilitating, and directing collaborative blended learning environments by emphasizing the concept of shared metacognition.
Principles of Computational Genomics (Chapman & Hall/CRC Computational Biology Series)
by Paolo ProveroThe advent of high-throughput experimental assays, and in particular of next-generation sequencing, has revolutionized life sciences by enabling the generation of data at the scale of the whole genome. Extracting biologically useful or clinically actionable information from this data requires analytical methods quite different from the ones used to analyze low-throughput experimental results. The development of these methods is the goal of computational biology.Understanding the principles on which these methods are based is thus necessary for all students and researchers in life sciences. This book provides the conceptual framework needed to understand computational genomics enough to be able to follow the arguments in recent papers, or to collaborate with computational scientists in research projects. In particular, it introduces the mathematical and statistical basis of the methods in some depth. The main focus is on the analysis of next-generation-sequencing assays, both for the interpretation of the DNA sequence per se (sequence alignment, phylogenetic tree reconstruction, genetic variants) and for the study of gene regulation and epigenomics (gene expression, transcription factor binding, chromatin states, 3D structure of the genome). The final chapter discusses the associations of genetic variants with phenotypes and diseases, and their biological interpretation.Principles of Computational Genomics provides a solid foundation for understanding the many parts of computational genomics, including those not treated directly in the book. It will be of great benefit to students and researchers across the life sciences.
Principles of Computer Architecture
by Miles J. Murdocca Vincent P. HeuringDesigned especially for undergraduates in computer-related disciplines, with emphasis to support the compiler, operating system, and networking courses. For a first course in computer architecture or computer organization. This text covers computer architecture at the instruction set architecture (ISA) and system design levels. Starting with foundation material on data representation and computer arithmetic, the book moves through the basic components of a computer architecture, covering topics at increasing levels of complexity up through CISC, network architecture, and parallel architecture. the authors have adopted the use of a SPARC-subset for an instructional ISA called "ARC" (A RISC Computer), which is carried through the mainstream of the book, and is complemented with platform-independent software tools that simulate the ARC ISA as well as the MIPS and x86 (Pentium) ISAs.
Principles of Computer Security: Comp TIA Security and Beyond
by Dwayne Williams Chuck Cothren Roger Davis Gregory White Wm. ConklinLearn IT security fundamentals while getting complete coverage of the objectives for the latest release of CompTIA Security+ certification exam SY0-501. This thoroughly revised, full-color textbook discusses communication, infrastructure, operational security, attack prevention, disaster recovery, computer forensics, and much more. Written by a pair of highly respected security educators, Principles of Computer Security: CompTIA Security+® and Beyond, Fifth Edition (Exam SY0-501) will help you pass the exam and become a CompTIA certified computer security expert.
Principles of Computer Systems and Network Management
by Dinesh Chandra VermaSystems Management is emerging as the predominant area for computer science in the enterprise, with studies showing that the bulk (up to 80%) of an enterprise IT budget is spent on management/operational issues and is the largest piece of the expenditure. This textbook provides an overview of the field of computer systems and network management. Systems management courses are being taught in different graduate and undergraduate computer science programs, but there are no good books with a comprehensive overview of the subject. This text book will provide content appropriate for either an undergraduate course (junior or senior year) or a graduate course in systems management.
Principles of Cyber-Physical Systems
by Rajeev AlurA foundational text that offers a rigorous introduction to the principles of design, specification, modeling, and analysis of cyber-physical systems.A cyber-physical system consists of a collection of computing devices communicating with one another and interacting with the physical world via sensors and actuators in a feedback loop. Increasingly, such systems are everywhere, from smart buildings to medical devices to automobiles. This textbook offers a rigorous and comprehensive introduction to the principles of design, specification, modeling, and analysis of cyber-physical systems. The book draws on a diverse set of subdisciplines, including model-based design, concurrency theory, distributed algorithms, formal methods of specification and verification, control theory, real-time systems, and hybrid systems, explaining the core ideas from each that are relevant to system design and analysis.The book explains how formal models provide mathematical abstractions to manage the complexity of a system design. It covers both synchronous and asynchronous models for concurrent computation, continuous-time models for dynamical systems, and hybrid systems for integrating discrete and continuous evolution. The role of correctness requirements in the design of reliable systems is illustrated with a range of specification formalisms and the associated techniques for formal verification. The topics include safety and liveness requirements, temporal logic, model checking, deductive verification, stability analysis of linear systems, and real-time scheduling algorithms. Principles of modeling, specification, and analysis are illustrated by constructing solutions to representative design problems from distributed algorithms, network protocols, control design, and robotics.This book provides the rapidly expanding field of cyber-physical systems with a long-needed foundational text by an established authority. It is suitable for classroom use or as a reference for professionals.
Principles of Cybercrime
by Jonathan CloughWe live in a digital age. The proliferation of digital technology, and the convergence of computing and communication devices, has transformed the way in which we socialise and do business. While overwhelmingly positive, there has also been a dark side to these developments. Proving the maxim that crime follows opportunity, virtually every advance has been accompanied by a corresponding niche to be exploited for criminal purposes; so-called 'cybercrimes'. Whether it be fraud, child pornography, stalking, criminal copyright infringement or attacks on computers themselves, criminals will find ways to exploit new technology. The challenge for all countries is to ensure their criminal laws keep pace. The challenge is a global one, and much can be learned from the experience of other jurisdictions. Focusing on Australia, Canada, the UK and the US, this book provides a comprehensive analysis of the legal principles that apply to the prosecution of cybercrimes.
Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently
by Sonia MezzettaApply Data Fabric solutions to automate Data Integration, Data Sharing, and Data Protection across disparate data sources using different data management styles.Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn to design Data Fabric architecture effectively with your choice of toolBuild and use a Data Fabric solution using DataOps and Data Mesh frameworksFind out how to build Data Integration, Data Governance, and Self-Service analytics architectureBook DescriptionData can be found everywhere, from cloud environments and relational and non-relational databases to data lakes, data warehouses, and data lakehouses. Data management practices can be standardized across the cloud, on-premises, and edge devices with Data Fabric, a powerful architecture that creates a unified view of data. This book will enable you to design a Data Fabric solution by addressing all the key aspects that need to be considered.The book begins by introducing you to Data Fabric architecture, why you need them, and how they relate to other strategic data management frameworks. You'll then quickly progress to grasping the principles of DataOps, an operational model for Data Fabric architecture. The next set of chapters will show you how to combine Data Fabric with DataOps and Data Mesh and how they work together by making the most out of it. After that, you'll discover how to design Data Integration, Data Governance, and Self-Service analytics architecture. The book ends with technical architecture to implement distributed data management and regulatory compliance, followed by industry best practices and principles.By the end of this data book, you will have a clear understanding of what Data Fabric is and what the architecture looks like, along with the level of effort that goes into designing a Data Fabric solution.What you will learnUnderstand the core components of Data Fabric solutionsCombine Data Fabric with Data Mesh and DataOps frameworksImplement distributed data management and regulatory compliance using Data FabricManage and enforce Data Governance with active metadata using Data FabricExplore industry best practices for effectively implementing a Data Fabric solutionWho this book is forIf you are a data engineer, data architect, or business analyst who wants to learn all about implementing Data Fabric architecture, then this is the book for you. This book will also benefit senior data professionals such as chief data officers looking to integrate Data Fabric architecture into the broader ecosystem.
Principles of Data Mining
by David J. Hand Heikki Mannila Padhraic SmythThe growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
Principles of Data Mining
by Max BramerData Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail. This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data. Principles of Data Mining aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Suitable as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science.
Principles of Data Mining
by Max BramerThis book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.
Principles of Data Mining (Undergraduate Topics in Computer Science)
by Max BramerThis book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self-study, it aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. Principles of Data Mining includes descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift. The expanded fourth edition gives a detailed description of a feed-forward neural network with backpropagation and shows how it can be used for classification.
Principles of Data Mining (Undergraduate Topics in Computer Science)
by Max BramerData Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas.Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail.This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data.Principles of Data Mining aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field.Suitable as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science.
Principles of Data Science
by Sinan OzdemirLearn the techniques and math you need to start making sense of your data About This Book • Enhance your knowledge of coding with data science theory for practical insight into data science and analysis • More than just a math class, learn how to perform real-world data science tasks with R and Python • Create actionable insights and transform raw data into tangible value Who This Book Is For You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you. What You Will Learn • Get to know the five most important steps of data science • Use your data intelligently and learn how to handle it with care • Bridge the gap between mathematics and programming • Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results • Build and evaluate baseline machine learning models • Explore the most effective metrics to determine the success of your machine learning models • Create data visualizations that communicate actionable insights • Read and apply machine learning concepts to your problems and make actual predictions In Detail Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means. Style and approach This is an easy-to-understand and accessible tutorial. It is a step-by-step guide with use cases, examples, and illustrations to get you well-versed with the concepts of data science. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world.
Principles of Data Science (Transactions on Computational Science and Computational Intelligence)
by Hamid R. Arabnia Robert Stahlbock Kevin Daimi Cristina Soviany Leonard Heilig Kai BrüssauThis book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists’ preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science.Introduces various techniques, methods, and algorithms adopted by Data Science expertsProvides a detailed explanation of data science perceptions, reinforced by practical examplesPresents a road map of future trends suitable for innovative data science research and practice
Principles of Data Science: A beginner's guide to essential math and coding skills for data fluency and machine learning
by Sinan OzdemirTransform your data into insights with must-know techniques and mathematical concepts to unravel the secrets hidden within your dataKey FeaturesLearn practical data science combined with data theory to gain maximum insights from dataDiscover methods for deploying actionable machine learning pipelines while mitigating biases in data and modelsExplore actionable case studies to put your new skills to use immediatelyPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionPrinciples of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights. Starting with cleaning and preparation, you’ll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you’ll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data. With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You’ll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you’ll explore medium-level data governance, including data provenance, privacy, and deletion request handling. By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.What you will learnMaster the fundamentals steps of data science through practical examplesBridge the gap between math and programming using advanced statistics and MLHarness probability, calculus, and models for effective data controlExplore transformative modern ML with large language modelsEvaluate ML success with impactful metrics and MLOpsCreate compelling visuals that convey actionable insightsQuantify and mitigate biases in data and ML modelsWho this book is forIf you are an aspiring novice data scientist eager to expand your knowledge, this book is for you. Whether you have basic math skills and want to apply them in the field of data science, or you excel in programming but lack the necessary mathematical foundations, you’ll find this book useful. Familiarity with Python programming will further enhance your learning experience.
Principles of Data Wrangling: Practical Techniques for Data Preparation
by Joseph M. Hellerstein Connor Carreras Jeffrey Heer Sean Kandel Tye RattenburyA key task that any aspiring data-driven organization needs to learn is data wrangling, the process of converting raw data into something truly useful. This practical guide provides business analysts with an overview of various data wrangling techniques and tools, and puts the practice of data wrangling into context by asking, "What are you trying to do and why?"Wrangling data consumes roughly 50-80% of an analyst’s time before any kind of analysis is possible. Written by key executives at Trifacta, this book walks you through the wrangling process by exploring several factors—time, granularity, scope, and structure—that you need to consider as you begin to work with data. You’ll learn a shared language and a comprehensive understanding of data wrangling, with an emphasis on recent agile analytic processes used by many of today’s data-driven organizations.Appreciate the importance—and the satisfaction—of wrangling data the right way.Understand what kind of data is availableChoose which data to use and at what level of detailMeaningfully combine multiple sources of dataDecide how to distill the results to a size and shape that can drive downstream analysis