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Data Science for Water Utilities Data as a Source of Value

Data Science for Water Utilities Data as a Source of Value

This addition to the Data Science Series introduces the principles of data science and the R language to the singular needs of water professionals. The book provides unique data and examples relevant to managing water utility and is sourced from the author’s extensive experience. Data Science for Water Utilities: Data as a Source of Value is an applied practical guide that shows water professionals how to use data science to solve urban water management problems. Content develops through four case studies. The first looks at analysing water quality to ensure public health. The second considers customer feedback. The third case study introduces smart meter data. The guide flows easily from basic principles through code that with each case study increases in complexity. The last case study analyses data using basic machine learning. Readers will be familiar with analysing data but do not need coding experience to use this book. The title will be essential reading for anyone seeking a practical introduction to data science and creating value with R. | Data Science for Water Utilities Data as a Source of Value

GBP 44.99
1

Time Series Clustering and Classification

Time Series Clustering and Classification

The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts statisticians mathematicians econometricians computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and classifying different types of large datasets including time series data. Time Series Clustering and Classification includes relevant developments on observation-based feature-based and model-based traditional and fuzzy clustering methods feature-based and model-based classification methods and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students. FeaturesProvides an overview of the methods and applications of pattern recognition of time seriesCovers a wide range of techniques including unsupervised and supervised approachesIncludes a range of real examples from medicine finance environmental science and moreR and MATLAB code and relevant data sets are available on a supplementary website

GBP 44.99
1

The Cloud Computing Book The Future of Computing Explained

The Cloud Computing Book The Future of Computing Explained

This latest textbook from bestselling author Douglas E. Comer is a class-tested book providing a comprehensive introduction to cloud computing. Focusing on concepts and principles rather than commercial offerings by cloud providers and vendors The Cloud Computing Book: The Future of Computing Explained gives readers a complete picture of the advantages and growth of cloud computing cloud infrastructure virtualization automation and orchestration and cloud-native software design. The book explains real and virtual data center facilities including computation (e. g. servers hypervisors Virtual Machines and containers) networks (e. g. leaf-spine architecture VLANs and VxLAN) and storage mechanisms (e. g. SAN NAS and object storage). Chapters on automation and orchestration cover the conceptual organization of systems that automate software deployment and scaling. Chapters on cloud-native software cover parallelism microservices MapReduce controller-based designs and serverless computing. Although it focuses on concepts and principles the book uses popular technologies in examples including Docker containers and Kubernetes. Final chapters explain security in a cloud environment and the use of models to help control the complexity involved in designing software for the cloud. The text is suitable for a one-semester course for software engineers who want to understand cloud and for IT managers moving an organization’s computing to the cloud. | The Cloud Computing Book The Future of Computing Explained

GBP 44.99
1

Hands-On Machine Learning with R

Hands-On Machine Learning with R

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R which includes using various R packages such as glmnet h2o ranger xgboost keras and others to effectively model and gain insight from their data. The book favors a hands-on approach providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book the reader will be exposed to the entire machine learning process including feature engineering resampling hyperparameter tuning model evaluation and interpretation. The reader will be exposed to powerful algorithms such as regularized regression random forests gradient boosting machines deep learning generalized low rank models and more! By favoring a hands-on approach and using real word data the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages understand when and how to tune the various hyperparameters and be able to interpret model results. By the end of this book the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering resampling deep learning and more. · Uses a hands-on approach and real world data.

GBP 82.99
1

Handbook of Infectious Disease Data Analysis

Handbook of Infectious Disease Data Analysis

Recent years have seen an explosion in new kinds of data on infectious diseases including data on social contacts whole genome sequences of pathogens biomarkers for susceptibility to infection serological panel data and surveillance data. The Handbook of Infectious Disease Data Analysis provides an overview of many key statistical methods that have been developed in response to such new data streams and the associated ability to address key scientific and epidemiological questions. A unique feature of the Handbook is the wide range of topics covered. Key featuresContributors include many leading researchers in the fieldDivided into four main sections: Basic concepts Analysis of Outbreak Data Analysis of Seroprevalence Data Analysis of Surveillance DataNumerous case studies and examples throughoutProvides both introductory material and key reference material

GBP 56.99
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Commodities Fundamental Theory of Futures Forwards and Derivatives Pricing

Commodities Fundamental Theory of Futures Forwards and Derivatives Pricing

Since a major source of income for many countries comes from exporting commodities price discovery and information transmission between commodity futures markets are key issues for continued economic development. Commodities: Fundamental Theory of Futures Forwards and Derivatives Pricing Second Edition covers the fundamental theory of and derivatives pricing for major commodity markets as well as the interaction between commodity prices the real economy and other financial markets. After a thoroughly updated and extensive theoretical and practical introduction this new edition of the book is divided into five parts – the fifth of which is entirely new material covering cutting-edge developments. Oil Products considers the structural changes in the demand and supply for hedging services that are increasingly determining the price of oil Other Commodities examines markets related to agricultural commodities including natural gas wine soybeans corn gold silver copper and other metals Commodity Prices and Financial Markets investigates the contemporary aspects of the financialization of commodities including stocks bonds futures currency markets index products and exchange traded funds Electricity Markets supplies an overview of the current and future modelling of electricity markets Contemporary Topics discuss rough volatility order book trading cryptocurrencies text mining for price dynamics and flash crashes | Commodities Fundamental Theory of Futures Forwards and Derivatives Pricing

GBP 150.00
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MATLAB Handbook with Applications to Mathematics Science Engineering and Finance

MATLAB Handbook with Applications to Mathematics Science Engineering and Finance

The purpose of this handbook is to allow users to learn and master the mathematics software package MATLAB® as well as to serve as a quick reference to some of the most used instructions in the package. A unique feature of this handbook is that it can be used by the novice and by experienced users alike. For experienced users it has four chapters with examples and applications in engineering finance physics and optimization. Exercises are included along with solutions available for the interested reader on the book’s web page. These exercises are a complement for the interested reader who wishes to get a deeper understanding of MATLAB. FeaturesCovers both MATLAB and introduction to SimulinkCovers the use of GUIs in MATLAB and SimulinkOffers downloadable examples and programs from the handbook’s websiteProvides an introduction to object oriented programming using MATLABIncludes applications from many areasIncludes the realization of executable files for MATLAB programs and Simulink models | MATLAB Handbook with Applications to Mathematics Science Engineering and Finance

GBP 44.99
1

Abstract Algebra

Abstract Algebra

Designed for an advanced undergraduate- or graduate-level course Abstract Algebra provides an example-oriented less heavily symbolic approach to abstract algebra. The text emphasizes specifics such as basic number theory polynomials finite fields as well as linear and multilinear algebra. This classroom-tested how-to manual takes a more narrative approach than the stiff formalism of many other textbooks presenting coherent storylines to convey crucial ideas in a student-friendly accessible manner. An unusual feature of the text is the systematic characterization of objects by universal mapping properties rather than by constructions whose technical details are irrelevant. Addresses Common Curricular Weaknesses In addition to standard introductory material on the subject such as Lagrange's and Sylow's theorems in group theory the text provides important specific illustrations of general theory discussing in detail finite fields cyclotomic polynomials and cyclotomic fields. The book also focuses on broader background including brief but representative discussions of naive set theory and equivalents of the axiom of choice quadratic reciprocity Dirichlet's theorem on primes in arithmetic progressions and some basic complex analysis. Numerous worked examples and exercises throughout facilitate a thorough understanding of the material.

GBP 59.99
1

Bioinformatics and Computational Biology Technological Advancements Applications and Opportunities

Bioinformatics and Computational Biology Technological Advancements Applications and Opportunities

Bioinformatics and Computational Biology: Technological Advancements Applications and Opportunities is an invaluable resource for general and applied researchers who analyze biological data that is generated at an unprecedented rate at the global level. After careful evaluation of the requirements for current trends in bioinformatics and computational biology it is anticipated that the book will provide an insightful resource to the academic and scientific community. Through a myriad of computational resources algorithms and methods it equips readers with the confidence to both analyze biological data and estimate predictions. The book offers comprehensive coverage of the most essential and emerging topics: Cloud-based monitoring of bioinformatics multivariate data with cloud platforms Machine learning and deep learning in bioinformatics Quantum machine learning for biological applications Integrating machine learning strategies with multiomics to augment prognosis in chronic diseases Biomedical engineering Next generation sequencing techniques and applications Computational systems biology and molecular evolution While other books may touch on some of the same issues and nuances of biological data analysis they neglect to feature bioinformatics and computational biology exclusively and as exhaustively. This book's abundance of several subtopics related to almost all of the regulatory activities of biomolecules from where real data is being generated brings an added dimension. | Bioinformatics and Computational Biology Technological Advancements Applications and Opportunities

GBP 120.00
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Introduction to Financial Mathematics With Computer Applications

Introduction to Financial Mathematics With Computer Applications

This book’s primary objective is to educate aspiring finance professionals about mathematics and computation in the context of financial derivatives. The authors offer a balance of traditional coverage and technology to fill the void between highly mathematical books and broad finance books. The focus of this book is twofold: To partner mathematics with corresponding intuition rather than diving so deeply into the mathematics that the material is inaccessible to many readers. To build reader intuition understanding and confidence through three types of computer applications that help the reader understand the mathematics of the models. Unlike many books on financial derivatives requiring stochastic calculus this book presents the fundamental theories based on only undergraduate probability knowledge. A key feature of this book is its focus on applying models in three programming languages –R Mathematica and EXCEL. Each of the three approaches offers unique advantages. The computer applications are carefully introduced and require little prior programming background. The financial derivative models that are included in this book are virtually identical to those covered in the top financial professional certificate programs in finance. The overlap of financial models between these programs and this book is broad and deep. | Introduction to Financial Mathematics With Computer Applications

GBP 89.99
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Automata and Computability A Programmer's Perspective

Automata and Computability A Programmer's Perspective

Automata and Computability is a class-tested textbook which provides a comprehensive and accessible introduction to the theory of automata and computation. The author uses illustrations engaging examples and historical remarks to make the material interesting and relevant for students. It incorporates modern/handy ideas such as derivative-based parsing and a Lambda reducer showing the universality of Lambda calculus. The book also shows how to sculpt automata by making the regular language conversion pipeline available through a simple command interface. A Jupyter notebook will accompany the book to feature code YouTube videos and other supplements to assist instructors and studentsFeatures Uses illustrations engaging examples and historical remarks to make the material accessible Incorporates modern/handy ideas such as derivative-based parsing and a Lambda reducer showing the universality of Lambda calculus Shows how to sculpt automata by making the regular language conversion pipeline available through simple command interface Uses a mini functional programming (FP) notation consisting of lambdas maps filters and set comprehension (supported in Python) to convey math through PL constructs that are succinct and resemble math Provides all concepts are encoded in a compact Functional Programming code that will tesselate with Latex markup and Jupyter widgets in a document that will accompany the books. Students can run code effortlessly. All the code can be accessed here. | Automata and Computability A Programmer's Perspective

GBP 39.99
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Applications of Regression for Categorical Outcomes Using R

Applications of Regression for Categorical Outcomes Using R

This book covers the main models within the GLM (i. e. logistic Poisson negative binomial ordinal and multinomial). For each model estimations interpretations model fit diagnostics and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata SPSS and SAS to using R and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge and for Quantitative social scientists due to it’s ability to act as a practitioners guide. Key Features: Applied- in the sense that we will provide code that others can easily adapt Flexible- R is basically just a fancy calculator. Our programs will enable users to derive quantities that they can use in their work Timely- many in the social sciences are currently transitioning to R or are learning it now. Our book will be a useful resource Versatile- we will write functions into an R package that can be applied to all of the regression models we will cover in the book Aesthetically pleasing- one advantage of R relative to other software packages is that graphs are fully customizable. We will leverage this feature to yield high-end graphical displays of results Affordability- R is free. R packages are free. There is no need to purchase site licenses or updates.

GBP 59.99
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Data Mining with R Learning with Case Studies Second Edition

Data Mining with R Learning with Case Studies Second Edition

Data Mining with R: Learning with Case Studies Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition this new edition is divided into two parts. The first part will feature introductory material including a new chapter that provides an introduction to data mining to complement the already existing introduction to R. The second part includes case studies and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies and they facilitate the do-it-yourself approach followed in the book. Designed for users of data analysis tools as well as researchers and developers the book should be useful for anyone interested in entering the world of R and data mining. About the AuthorLuís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA. | Data Mining with R Learning with Case Studies Second Edition

GBP 44.99
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Contemporary Abstract Algebra

Contemporary Abstract Algebra

Contemporary Abstract Algebra Tenth Edition For more than three decades this classic text has been widely appreciated by instructors and students alike. The book offers an enjoyable read and conveys and develops enthusiasm for the beauty of the topics presented. It is comprehensive lively and engaging. The author presents the concepts and methodologies of contemporary abstract algebra as used by working mathematicians computer scientists physicists and chemists. Students will learn how to do computations and to write proofs. A unique feature of the book are exercises that build the skill of generalizing a skill that students should develop but rarely do. Applications are included to illustrate the utility of the abstract concepts. Examples and exercises are the heart of the book. Examples elucidate the definitions theorems and proof techniques; exercises facilitate understanding provide insight and develop the ability to do proofs. The exercises often foreshadow definitions concepts and theorems to come. Changes for the tenth edition include new exercises new examples new quotes and a freshening of the discussion portions. The hallmark features of previous editions of the book are enhanced in this edition. These include: A good mixture of approximately 1900 computational and theoretical exercises including computer exercises that synthesize concepts from multiple chapters Approximately 300 worked-out examples from routine computations to the challenging Many applications from scientific and computing fields and everyday life Historical notes and biographies that spotlight people and events Motivational and humorous quotations Numerous connections to number theory and geometry While many partial solutions and sketches for the odd-numbered exercises appear in the book an Instructor’s Solutions Manual written by the author has comprehensive solutions for all exercises and some alternative solutions to develop a critical thought and deeper understanding. It is available from CRC Press only. The Student Solution Manual has comprehensive solutions for all odd-numbered exercises and many even-numbered exercises.

GBP 82.99
1

Rough Multiple Objective Decision Making

Rough Multiple Objective Decision Making

Under intense scrutiny for the last few decades Multiple Objective Decision Making (MODM) has been useful for dealing with the multiple-criteria decisions and planning problems associated with many important applications in fields including management science engineering design and transportation. Rough set theory has also proved to be an effective mathematical tool to counter the vague description of objects in fields such as artificial intelligence expert systems civil engineering medical data analysis data mining pattern recognition and decision theory. Rough Multiple Objective Decision Making is perhaps the first book to combine state-of-the-art application of rough set theory rough approximation techniques and MODM. It illustrates traditional techniques—and some that employ simulation-based intelligent algorithms—to solve a wide range of realistic problems. Application of rough theory can remedy two types of uncertainty (randomness and fuzziness) which present significant drawbacks to existing decision-making methods so the authors illustrate the use of rough sets to approximate the feasible set and they explore use of rough intervals to demonstrate relative coefficients and parameters involved in bi-level MODM. The book reviews relevant literature and introduces models for both random and fuzzy rough MODM applying proposed models and algorithms to problem solutions. Given the broad range of uses for decision making the authors offer background and guidance for rough approximation to real-world problems with case studies that focus on engineering applications including construction site layout planning water resource allocation and resource-constrained project scheduling. The text presents a general framework of rough MODM including basic theory models and algorithms as well as a proposed methodological system and discussion of future research.

GBP 74.99
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Fundamentals of Causal Inference With R

Fundamentals of Causal Inference With R

Overall this textbook is a perfect guide for interested researchers and students who wish to understand the rationale and methods of causal inference. Each chapter provides an R implementation of the introduced causal concepts and models and concludes with appropriate exercises. An-Shun Tai & Sheng-Hsuan Lin in BiometricsOne of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is of utmost importance. Fundamentals of Causal Inference explains and relates different methods of confounding adjustment in terms of potential outcomes and graphical models including standardization difference-in-differences estimation the front-door method instrumental variables estimation and propensity score methods. It also covers effect-measure modification precision variables mediation analyses and time-dependent confounding. Several real data examples simulation studies and analyses using R motivate the methods throughout. The book assumes familiarity with basic statistics and probability regression and R and is suitable for seniors or graduate students in statistics biostatistics and data science as well as PhD students in a wide variety of other disciplines including epidemiology pharmacy the health sciences education and the social economic and behavioral sciences. Beginning with a brief history and a review of essential elements of probability and statistics a unique feature of the book is its focus on real and simulated datasets with all binary variables to reduce complex methods down to their fundamentals. Calculus is not required but a willingness to tackle mathematical notation difficult concepts and intricate logical arguments is essential. While many real data examples are included the book also features the Double What-If Study based on simulated data with known causal mechanisms in the belief that the methods are best understood in circumstances where they are known to either succeed or fail. Datasets R code and solutions to odd-numbered exercises are available on the book's website at www. routledge. com/9780367705053. Instructors can also find slides based on the book and a full solutions manual under 'Instructor Resources'. | Fundamentals of Causal Inference With R

GBP 56.99
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Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies

Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies

Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies was inspired by recent developments in the fields of reinforcement learning (RL) and cyber-physical systems (CPSs). Rooted in behavioral psychology RL is one of the primary strands of machine learning. Different from other machine learning algorithms such as supervised learning and unsupervised learning the key feature of RL is its unique learning paradigm i. e. trial-and-error. Combined with the deep neural networks deep RL become so powerful that many complicated systems can be automatically managed by AI agents at a superhuman level. On the other hand CPSs are envisioned to revolutionize our society in the near future. Such examples include the emerging smart buildings intelligent transportation and electric grids. However the conventional hand-programming controller in CPSs could neither handle the increasing complexity of the system nor automatically adapt itself to new situations that it has never encountered before. The problem of how to apply the existing deep RL algorithms or develop new RL algorithms to enable the real-time adaptive CPSs remains open. This book aims to establish a linkage between the two domains by systematically introducing RL foundations and algorithms each supported by one or a few state-of-the-art CPS examples to help readers understand the intuition and usefulness of RL techniques. FeaturesIntroduces reinforcement learning including advanced topics in RL Applies reinforcement learning to cyber-physical systems and cybersecurity Contains state-of-the-art examples and exercises in each chapterProvides two cybersecurity case studies Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies is an ideal text for graduate students or junior/senior undergraduates in the fields of science engineering computer science or applied mathematics. It would also prove useful to researchers and engineers interested in cybersecurity RL and CPS. The only background knowledge required to appreciate the book is a basic knowledge of calculus and probability theory.

GBP 44.99
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Cloud Security Attacks Techniques Tools and Challenges

Cloud Security Attacks Techniques Tools and Challenges

Cloud computing has gained paramount attention and most of the companies are adopting this new paradigm and gaining significant benefits. As number of applications and business operations are being facilitated by the cloud computing paradigm it has become the potential target to attackers. The importance of well-organized architecture and security roles have become greater with the growing popularity. Cloud Security: Attacks Techniques Tools and Challenges provides an in-depth technical description about various key essential aspects of cloud security. We have endeavored to provide a technical foundation that will be practically useful not just for students and independent researchers but also for professional cloud security analysts for conducting security procedures and all those who are curious in the field of cloud security The book offers comprehensive coverage of the most essential topics including: Basic fundamentals of Cloud Computing Cloud security concepts vulnerabilities security standards and reference models Cloud security goals key issues and privacy requirements Threat model detailed taxonomy of cloud attacks Attack feature analysis – case study A detailed taxonomy of IDS techniques and Cloud Intrusion Detection Systems (IDS) Attack and security tools LibVMI – case study Advanced approaches: Virtual Machine Introspection (VMI) and Hypervisor Introspection (HVI) Container security: threat model attacks and defense systems This book is intended for both academic and professional audience. It could also be used as a textbook for a semester course at undergraduate and post graduate level in Computer Science Information Technology Information Security and Information Science & Management. The book serves as basic reference volume for researchers in cloud security. It will be useful to practitioners cloud security team and the cloud security auditor as well. To get the most out of this book the reader should have a working knowledge of various operating system environments hypervisors cloud computing fundamentals programming languages like Python and a working knowledge of security tools. | Cloud Security Attacks Techniques Tools and Challenges

GBP 130.00
1

Abstract Algebra A First Course

Abstract Algebra A First Course

When a student of mathematics studies abstract algebra he or she inevitably faces questions in the vein of What is abstract algebra or What makes it abstract? Algebra in its broadest sense describes a way of thinking about classes of sets equipped with binary operations. In high school algebra a student explores properties of operations (+ − × and ÷) on real numbers. Abstract algebra studies properties of operations without specifying what types of number or object we work with. Any theorem established in the abstract context holds not only for real numbers but for every possible algebraic structure that has operations with the stated properties. This textbook intends to serve as a first course in abstract algebra. The selection of topics serves both of the common trends in such a course: a balanced introduction to groups rings and fields; or a course that primarily emphasizes group theory. The writing style is student-centered conscientiously motivating definitions and offering many illustrative examples. Various sections or sometimes just examples or exercises introduce applications to geometry number theory cryptography and many other areas. This book offers a unique feature in the lists of projects at the end of each section. the author does not view projects as just something extra or cute but rather an opportunity for a student to work on and demonstrate their potential for open-ended investigation. The projects ideas come in two flavors: investigative or expository. The investigative projects briefly present a topic and posed open-ended questions that invite the student to explore the topic asking and to trying to answer their own questions. Expository projects invite the student to explore a topic with algebraic content or pertain to a particular mathematician’s work through responsible research. The exercises challenge the student to prove new results using the theorems presented in the text. The student then becomes an active participant in the development of the field. | Abstract Algebra A First Course

GBP 99.99
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Computational Genomics with R

Computational Genomics with R

Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming to machine learning and statistics to the latest genomic data analysis techniques. The text provides accessible information and explanations always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary it requires different starting points for people with different backgrounds. For example a biologist might skip sections on basic genome biology and start with R programming whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics supervised and unsupervised learning techniques that are important in data modeling and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics such as heatmaps meta-gene plots and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets such as RNA-seq ChIP-seq and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology Max Delbrück Center Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

GBP 42.99
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Differential Equations Theory Technique and Practice

Differential Equations Theory Technique and Practice

Differential equations is one of the oldest subjects in modern mathematics. It was not long after Newton and Leibniz invented the calculus that Bernoulli and Euler and others began to consider the heat equation and the wave equation of mathematical physics. Newton himself solved differential equations both in the study of planetary motion and also in his consideration of optics. Today differential equations is the centerpiece of much of engineering of physics of significant parts of the life sciences and in many areas of mathematical modeling. This text describes classical ideas and provides an entree to the newer ones. The author pays careful attention to advanced topics like the Laplace transform Sturm–Liouville theory and boundary value problems (on the traditional side) but also pays due homage to nonlinear theory to modeling and to computing (on the modern side). This book began as a modernization of George Simmons’ classic Differential Equations with Applications and Historical Notes. Prof. Simmons invited the author to update his book. Now in the third edition this text has become the author’s own and a unique blend of the traditional and the modern. The text describes classical ideas and provides an entree to newer ones. Modeling brings the subject to life and makes the ideas real. Differential equations can model real life questions and computer calculations and graphics can then provide real life answers. The symbiosis of the synthetic and the calculational provides a rich experience for students and prepares them for more concrete applied work in future courses. Additional Features Anatomy of an Application sections. Historical notes continue to be a unique feature of this text. Math Nuggets are brief perspectives on mathematical lives or other features of the discipline that will enhance the reading experience. Problems for Review and Discovery give students some open-ended material for exploration and further learning. They are an important means of extending the reach of the text and for anticipating future work. This new edition is re-organized to make it more useful and more accessible. The most frequently taught topics are now up front. And the major applications are isolated in their own chapters. This makes this edition the most useable and flexible of any previous editions. | Differential Equations Theory Technique and Practice

GBP 82.99
1

An Introduction to Analysis

An Introduction to Analysis

The third edition of this widely popular textbook is authored by a master teacher. This book provides a mathematically rigorous introduction to analysis of real­valued functions of one variable. This intuitive student-friendly text is written in a manner that will help to ease the transition from primarily computational to primarily theoretical mathematics. The material is presented clearly and as intuitive as possible while maintaining mathematical integrity. The author supplies the ideas of the proof and leaves the write-up as an exercise. The text also states why a step in a proof is the reasonable thing to do and which techniques are recurrent. Examples while no substitute for a proof are a valuable tool in helping to develop intuition and are an important feature of this text. Examples can also provide a vivid reminder that what one hopes might be true is not always true. Features of the Third Edition: Begins with a discussion of the axioms of the real number system. The limit is introduced via sequences. Examples motivate what is to come highlight the need for hypothesis in a theorem and make abstract ideas more concrete. A new section on the Cantor set and the Cantor function. Additional material on connectedness. Exercises range in difficulty from the routine getting your feet wet types of problems to the moderately challenging problems. Topology of the real number system is developed to obtain the familiar properties of continuous functions. Some exercises are devoted to the construction of counterexamples. The author presents the material to make the subject understandable and perhaps exciting to those who are beginning their study of abstract mathematics. Table of Contents Preface Introduction The Real Number System Sequences of Real Numbers Topology of the Real Numbers Continuous Functions Differentiation Integration Series of Real Numbers Sequences and Series of Functions Fourier Series Bibliography Hints and Answers to Selected Exercises Index Biography James R. Kirkwood holds a Ph. D. from University of Virginia. He has authored fifteen published mathematics textbooks on various topics including calculus real analysis mathematical biology and mathematical physics. His original research was in mathematical physics and he co-authored the seminal paper in a topic now called Kirkwood-Thomas Theory in mathematical physics. During the summer he teaches real analysis to entering graduate students at the University of Virginia. He has been awarded several National Science Foundation grants. His texts Elementary Linear Algebra Linear Algebra and Markov Processes are also published by CRC Press. | An Introduction to Analysis

GBP 82.99
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Statistics in Engineering With Examples in MATLAB and R Second Edition

Statistics in Engineering With Examples in MATLAB and R Second Edition

Engineers are expected to design structures and machines that can operate in challenging and volatile environments while allowing for variation in materials and noise in measurements and signals. Statistics in Engineering Second Edition: With Examples in MATLAB and R covers the fundamentals of probability and statistics and explains how to use these basic techniques to estimate and model random variation in the context of engineering analysis and design in all types of environments. The first eight chapters cover probability and probability distributions graphical displays of data and descriptive statistics combinations of random variables and propagation of error statistical inference bivariate distributions and correlation linear regression on a single predictor variable and the measurement error model. This leads to chapters including multiple regression; comparisons of several means and split-plot designs together with analysis of variance; probability models; and sampling strategies. Distinctive features include: All examples based on work in industry consulting to industry and research for industry Examples and case studies include all engineering disciplinesEmphasis on probabilistic modeling including decision trees Markov chains and processes and structure functionsIntuitive explanations are followed by succinct mathematical justificationsEmphasis on random number generation that is used for stochastic simulations of engineering systems demonstration of key concepts and implementation of bootstrap methods for inferenceUse of MATLAB and the open source software R both of which have an extensive range of statistical functions for standard analyses and also enable programing of specific applicationsUse of multiple regression for times series models and analysis of factorial and central composite designs Inclusion of topics such as Weibull analysis of failure times and split-plot designs that are commonly used in industry but are not usually included in introductory textbooksExperiments designed to show fundamental concepts that have been tested with large classes working in small groupsWebsite with additional materials that is regularly updatedAndrew Metcalfe David Green Andrew Smith and Jonathan Tuke have taught probability and statistics to students of engineering at the University of Adelaide for many years and have substantial industry experience. Their current research includes applications to water resources engineering mining and telecommunications. Mahayaudin Mansor worked in banking and insurance before teaching statistics and business mathematics at the Universiti Tun Abdul Razak Malaysia and is currently a researcher specializing in data analytics and quantitative research in the Health Economics and Social Policy Research Group at the Australian Centre for Precision Health University of South Australia. Tony Greenfield formerly Head of Process Computing and Statistics at the British Iron and Steel Research Association is a statistical consultant. He has been awarded the Chambers Medal for outstanding services to the Royal Statistical Society; the George Box Medal by the European Network for Business and Industrial Statistics for Outstanding Contributions to Industrial Statistics; and the William G. Hunter Award by the American Society for | Statistics in Engineering With Examples in MATLAB® and R Second Edition

GBP 44.99
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