The most authoritative and complete study guide for people beginning to work with data in the Azure cloud In MC Azure Data Fundamentals Study Guide: Exam DP-900, expert Cloud Solution Architect Jake Switzer delivers a hands-on blueprint to acing the DP-900 Azure data certification. The book prepares you for the test - and for a new career in Azure data analytics, architecture, science, and more - with a laser-focus on the job roles and responsibilities of Azure data professionals. You'll receive a foundational knowledge of core data concepts, like relational and non-relational data and transactional and analytical data workloads, while diving deep into every competency covered on the DP-900 exam. You'll also get: Access to complimentary online study tools, including hundreds of practice exam questions, electronic flashcards, and a searchable glossary Additional prep assistance with access to Sybex's superior interactive online learning environment and test bank Walkthroughs of skills and knowledge that are absolutely necessary for current and aspiring Azure data pros in introductory roles Perfect for anyone just beginning to work with data in the cloud, MC Azure Data Fundamentals Study Guide: Exam DP-900 is a can't-miss resource for anyone prepping for the DP-900 exam or considering a new career working with Azure data.

ICMI (or IMUK) was founded in 1908 and initiated the establishment of national subcommissions to launch national activities in response to the IMUK agenda and to promote the reform proposals within each member country.While ICMI's activities were thoroughly studied, the activities of the national subcommissions are studied only very marginally. In the meantime, their work has been of major importance - both because of their role in exploring and documenting the development of mathematics education at the beginning of the 20th century, and because of the changes and new ideas which they brought to their countries. Importantly, even if some results of their activities were analyzed within their countries in the corresponding languages, almost nothing is known internationally. This book is planned to deepen our knowledge on at least some of the national subcommissions. The book will interest both researchers and others interested in mathematics education and its development.

Accessible introductory textbook on optimization theory and methods, with an emphasis on engineering design, featuring MATLAB exercises and worked examples Fully updated to reflect modern developments in the field, the Fifth Edition of An Introduction to Optimization fills the need for an accessible, yet rigorous, introduction to optimization theory and methods, featuring innovative coverage and a straightforward approach. The book begins with a review of basic definitions and notations while also providing the related fundamental background of linear algebra, geometry, and calculus. With this foundation, the authors explore the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. In addition, the book includes an introduction to artificial neural networks, convex optimization, multi-objective optimization, and applications of optimization in machine learning. Numerous diagrams and figures found throughout the book complement the written presentation of key concepts, and each chapter is followed by MATLAB® exercises and practice problems that reinforce the discussed theory and algorithms. The Fifth Edition features a new chapter on Lagrangian (nonlinear) duality, expanded coverage on matrix games, projected gradient algorithms, machine learning, and numerous new exercises at the end of each chapter. An Introduction to Optimization includes information on: The mathematical definitions, notations, and relations from linear algebra, geometry, and calculus used in optimization Optimization algorithms, covering one-dimensional search, randomized search, and gradient, Newton, conjugate direction, and quasi-Newton methods Linear programming methods, covering the simplex algorithm, interior point methods, and duality Nonlinear constrained optimization, covering theory and algorithms, convex optimization, and Lagrangian duality Applications of optimization in machine learning, including neural network training, classification, stochastic gradient descent, linear regression, logistic regression, support vector machines, and clustering. An Introduction to Optimization is an ideal textbook for a one- or two-semester senior undergraduate or beginning graduate course in optimization theory and methods. The text is also of value for researchers and professionals in mathematics, operations research, electrical engineering, economics, statistics, and business.

Features A broad and accessible overview of non-homogeneous Markov chains and systems Fills a significant gap in the current literature A good balance of theory and applications, with advanced mathematical details separated from the main results Many illustrative examples of potential applications from a variety of fields Suitable for use as a course text for postgraduate students of applied probability, or for self-study Potential applications included could open it up to other quantitative areas

This open access book is the first major publication on the topic of "Interdisciplinary Mathematics Education" and arose from the work of the first International Topic Study Group of the same name at the ICME-13 conference in Hamburg in 2016. It offers extensive theoretical insights, empirical research, and practitioner accounts of interdisciplinary mathematics work in STEM and beyond (e.g. in music and the arts). Scholars and practitioners from four continents contributed to this comprehensive book, and present studies on: the conceptualizations of interdisciplinarity; implementation cases at schools and tertiary institutions; teacher education; and implications for policy and practice. Each chapter, and the book itself, closes with an assessment of the most significant aspects that those involved in policy and practice, as well as future researchers, should take into account.

Interest in microarrays has increased considerably in the last ten years. This increase in the use of microarray technology has led to the need for good standards of microarray experimental notation, data representation, and the introduction of standard experimental controls, as well as standard data normalization and analysis techniques. "Statistics for Microarrays: Design, Analysis and Inference" is the first book that presents a coherent and systematic overview of statistical methods in all stages in the process of analysing microarray data - from getting good data to obtaining meaningful results.Provides an overview of statistics for microarrays, including experimental design, data preparation, image analysis, normalization, quality control, and statistical inference.Features many examples throughout using real data from microarray experiments.Computational techniques are integrated into the text.Takes a very practical approach, suitable for statistically-minded biologists.Supported by a Website featuring colour images, software, and data sets.

Primarily aimed at statistically-minded biologists, bioinformaticians, biostatisticians, and computer scientists working with microarray data, the book is also suitable for postgraduate students of bioinformatics.

Matrix-analytic methods (MAM) were introduced by Professor Marcel Neuts and have been applied to a variety of stochastic models since. In order to provide a clear and deep understanding of MAM while showing their power, this book presents MAM concepts and explains the results using a number of worked-out examples. This book's approach will inform and kindle the interest of researchers attracted to this fertile field. To allow readers to practice and gain experience in the algorithmic and computational procedures of MAM, Introduction to Matrix Analytic Methods in Queues 1 provides a number of computational exercises. It also incorporates simulation as another tool for studying complex stochastic models, especially when the state space of the underlying stochastic models under analytic study grows exponentially. The book's detailed approach will make it more accessible for readers interested in learning about MAM in stochastic models.

As long as algebra and geometry The unreasonable effectiveness of proceeded along separate paths, mathematics in science . . . Eugene Wigner their advance was slow and their applications limited. But when these sciences joined Weil, if you knows of a better 'oie, company, they drew from each go to it. Bruce Bairnsfather other fresh vitality and thence forward marched on at a rapid pace What is now proved was once only towards perfeetion. imagined. Wi1liam Blake J oseph Louis Lagrange Growing specialization and diversification have brought a host of monographs and textbooks on increasingly specialized topics. However, the 'tree' of knowledge of mathematics and related fields does not grow only by putting forth new branches. It also happens, quite often in fact, that branches which were thought to be completely disparate are suddenly seen to be related. This series of books, Mathematics and Its Applications, is devoted to such (new) interrelations as exempla gratia: - a central concept which plays an important role in several different mathematical and/or scientific specialized areas; Editor's Preface 8 - new applications of the results and ideas from one area of scientific endeavor into another; - influences which the results, problems and concepts of one field of inquiry have and have had on the development of another. With books on topics such as these, of moderate length and price, which are stimulating rather than definitive, intriguing rather than encyclopaedic, we hope to contribute something towards better communication among the practitioners in diversified fields."

"Welcome to the maze.

A logical maze, a magical maze. A maze of the mind.

The maze is mathematics. The mind is yours. Let's see what happens when we put them together.

What is mathematics? What do mathematicians do?

What is a mathematician? Someone who does mathematics?

Not exactly. That's too easy an answer, and it creates too simple a maze—a circular loop of self-referential logic. No, a mathematician is more than just somebody who does mathematics. Think of it this way: what is a businessperson? Someone who does business? Yes, but not just that. A businessperson is someone who sees an opportunity for doing business where the rest of us see nothing; while we're complaining that there's no restaurant in the area, he or she is organizing a telephone pizza delivery service. Similarly, a mathematician is someone who sees opportunities for doing mathematics that the rest of us miss.

I want to open your mind to some of these opportunities."—from The Magical Maze

Praise for Ian Stewart's Previous Books

About Nature's Numbers: "Stewart achieves what other popular mathematics writers merely strive for: an accurate, informative portrayal of contemporary mathematics without a single equation in sight."—Nature

About The Problems of Mathematics:

"From one of mathematics' most gifted expositors . . . challenging and interesting. Those with no knowledge of the subject will be able to glimpse its beauty and appeal."—New Scientist

About The Collapse of Chaos:

"This ambitious book fearlessly asks some big questions, challenging us to look at science a new way."—San Francisco Chronicle

About Another Fine Math You've Got Me Into:

"Ian Stewart's quirky humor and imaginative storytelling entice readers into a fascinating world of mathematical curiosities."—Ivars Peterson author of The Jungles of Randomness

Enter the magical maze of mathematics and explore the surprising passageways of a fantastical world where logic and imagination converge. For mathematics is a maze—a maze in your head—a maze of ideas, a maze of logic. And that maze in your mind is a powerful tool for understanding an even bigger maze—the maze of cause and effect that we call "the universe." That is its special kind of magic. Real magic. Strange magic. Infinitely fascinating magic.

In this adventure of a book, acclaimed author Ian Stewart leads you swiftly and humorously through the junctions, byways, and secret passages of the magical maze to reveal its beauty, its surprise, and its power. Along the way, he reveals the infinite possibilities that arise from what he calls "the two-way trade between the natural world and the human mind."

On your travels you will encounter number magic—both the stage-act variety and the deeper magic of animals, plants, and the physical world. You will come to understand the amazing pattern-forming abilities of the humble slime mold, the numerology of flowers, and the feeding habits of pigs and panthers. You will discover how to solve puzzles the algorithmic way, the artistic way, and the army way. You will be amazed by the deep connections between the founding of Carthage, soap bubbles, and communications networks. You will discover how to use a toy train set as a computer, and find out why this implies that there are unavoidable limits to mathematics. You will join the controversy over cars and goats, find out the terrible truth about confessions, and win endless bets about birthdays. You will see how a new idea about ferns can lead to a multi-million-dollar computer graphics company, and how Jupiter and Mars can combine forces to hurl cosmic rocks at Earth. And you will never again be able to watch a kitten, a kangaroo, or a Chihuahua without noticing the delightfully rhythmic patterns with which they move their feet.

If you've always loved mathematics, you will find endless delights in the twists and turns of The Magical Maze. If you've always hated mathematics, a trip through this marvelous book will do much to change your mind.

Geophysical Data Analysis: Diverse Inverse Theory, Fourth Edition is a revised and expanded introduction to inverse theory and tomography as it is practiced by geophysicists. It demonstrates the methods needed to analyze a broad spectrum of geophysical datasets, with special attention to those methods that generate images of the earth. Data analysis can be a mathematically complex activity, but the treatment in this volume is carefully designed to emphasize those mathematical techniques that readers will find the most familiar and to systematically introduce less-familiar ones. Using problems and case studies, along with MATLAB computer code and summaries of methods, the book provides data scientists and engineers in geophysics with the tools necessary to understand and apply mathematical techniques and inverse theory.

Tessellations: Mathematics, Art and Recreation aims to present a comprehensive introduction to tessellations (tiling) at a level accessible to non-specialists. Additionally, it covers techniques, tips, and templates to facilitate the creation of mathematical art based on tessellations. Inclusion of special topics like spiral tilings and tessellation metamorphoses allows the reader to explore beautiful and entertaining math and art. The book has a particular focus on 'Escheresque' designs, in which the individual tiles are recognizable real-world motifs. These are extremely popular with students and math hobbyists but are typically very challenging to execute. Techniques demonstrated in the book are aimed at making these designs more achievable. Going beyond planar designs, the book contains numerous nets of polyhedra and templates for applying Escheresque designs to them. Activities and worksheets are spread throughout the book, and examples of real-world tessellations are also provided. Key features Introduces the mathematics of tessellations, including symmetry Covers polygonal, aperiodic, and non-Euclidean tilings Contains tutorial content on designing and drawing Escheresque tessellations Highlights numerous examples of tessellations in the real world Activities for individuals or classes Filled with templates to aid in creating Escheresque tessellations Treats special topics like tiling rosettes, fractal tessellations, and decoration of tiles

The present work is an introductory text in statistics, addressed to researchers and students in the field of material science. It aims to give the readers basic knowledge on how statistical reasoning is exploitable in this field, improving their knowledge of statistical tools and helping them to carry out statistical analyses and to interpret the results. It also focuses on establishing a consistent multivariate workflow starting from a correct design of experiment followed by a multivariate analysis process.

New 2017 Cambridge A Level Maths and Further Maths resources to help students with learning and revision. Written for the OCR AS/A Level Further Mathematics specification for first teaching from 2017, this print Student Book covers the Mechanics content for AS and A Level. It balances accessible exposition with a wealth of worked examples, exercises and opportunities to test and consolidate learning, providing a clear and structured pathway for progressing through the course. It is underpinned by a strong pedagogical approach, with an emphasis on skills development and the synoptic nature of the course. Includes answers to aid independent study.

During the years 1903-1914, Ramanujan worked in almost complete isolation in India. During this time, he recorded most of his mathematical discoveries without proofs in notebooks. Although many of his results were already found in the literature, most were not. Almost a decade after Ramanujan's death in 1920, G.N. Watson and B.M. Wilson began to edit Ramanujan's notebooks, but they never completed the task. A photostat edition, with no editing, was published by the Tata Institute of Fundamental Research in Bombay in 1957. This book is the fourth of five volumes devoted to the editing of Ramanujan's notebooks. Parts I, II, and III, published in 1985, 1989, and 1991, contain accounts of Chapters 1-21 in Ramanujan's second notebook as well as a description of his quarterly reports. This is the first of two volumes devoted to proving the results found in the unorganized portions of the second notebook and in the third notebook. The author also proves those results in the first notebook that are not found in the second or third notebooks. For those results that are known, references in the literature are provided. Otherwise, complete proofs are given. Over 1/2 of the results in the notebooks are new. Many of them are so startling and different that there are no results akin to them in the literature.

Provides clear guidance on leveraging computational techniques to answer social science questions In disciplines such as political science, sociology, psychology, and media studies, the use of computational analysis is rapidly increasing. Statistical modeling, machine learning, and other computational techniques are revolutionizing the way electoral results are predicted, social sentiment is measured, consumer interest is evaluated, and much more. Computational Analysis of Communication teaches social science students and practitioners how computational methods can be used in a broad range of applications, providing discipline-relevant examples, clear explanations, and practical guidance. Assuming little or no background in data science or computer linguistics, this accessible textbook teaches readers how to use state-of-the art computational methods to perform data-driven analyses of social science issues. A cross-disciplinary team of authors--with expertise in both the social sciences and computer science--explains how to gather and clean data, manage textual, audio-visual, and network data, conduct statistical and quantitative analysis, and interpret, summarize, and visualize the results. Offered in a unique hybrid format that integrates print, ebook, and open-access online viewing, this innovative resource: Covers the essential skills for social sciences courses on big data, data visualization, text analysis, predictive analytics, and others Integrates theory, methods, and tools to provide unified approach to the subject Includes sample code in Python and links to actual research questions and cases from social science and communication studies Discusses ethical and normative issues relevant to privacy, data ownership, and reproducible social science Developed in partnership with the International Communication Association and by the editors of Computational Communication Research Computational Analysis of Communication is an invaluable textbook and reference for students taking computational methods courses in social sciences, and for professional social scientists looking to incorporate computational methods into their work.

'Freeman Dyson has had an extraordinary range of interests, serious activity and influence. This is reflected in the choice of topics in these essays ... This book is informative, very entertaining and well up to the high standards attained previously by this author!'Contemporary PhysicsMy friendship with Freeman Dyson goes back over a half century. My first contact with him goes back to the late 1950s, when I was at the Institute for Advanced Study, and then evolved when I was a consultant at General Atomics in La Jolla, California. Freeman was then trying to design a space ship - the Orion - which would be propelled by atomic bombs. When I left the Institute, Freeman and I continued our correspondence and I saved his letters. They are written in an almost calligraphically elegant handwriting. It is hard to see how you could make a mistake in a mathematical computation if you wrote that clearly. The letters show his human side and his enormous range of knowledge.There are then two essays involving the physicist Fritz Houtermans who was an extraordinarily colorful character. There is a brief essay on Einstein's collaboration with a fraud. There is even an essay on the Titius-Bode law and the new exo-planets. Because of my enduring interest in nuclear weapons, the reader will find essays devoted to that. There is also a bit of fiction at the end.

The study of dynamical systems is a well established field. Having given graduate-level courses on the subject for many years, the authors have now written this book to provide a panorama of the aspects that are of interest to mathematicians and physicists alike.

Avoiding belaboured proofs, the exposition concentrates instead on abundant illustrations and examples, while still retaining sufficient mathematical precision. Besides the standard topics of the field, questions of physical measurement and stochastic properties of chaotic dynamical systems are given much attention.

This book provides the key technologies involved in an organization's digital transformation. It offers a deep understanding of the key technologies (Blockchain, AI, Big Data, IoT, etc.) involved and details the impact, the decision-making process, and the interplay between technologies, business models, and operations. Managing the Digital Transformation: Aligning Technologies, Business Models, and Operations provides frameworks and models to support digital transformation projects. The book presents the importance of digital transformation as a resilience approach to the operations processes and business models. It covers the essential elements integrating the technology, the organizations, the operations, and supply chain management used to move toward digital transformation. Concepts and mini-case studies are included to provide a deeper understanding of digital transformation projects with a holistic view. The book also examines the role that digital transformation plays with consideration of inter-organizational and intra-organizational capabilities, along with the role of digital culture, the worker's skills, business models, reconfiguration, as well as an operations optimization angle. Practitioners, consultants, governments, managers, scholars, and anyone interested in digital transformation will find the contents of this book very useful.

A Concise Handbook of Mathematics, Physics, and Engineering Sciences takes a practical approach to the basic notions, formulas, equations, problems, theorems, methods, and laws that most frequently occur in scientific and engineering applications and university education. The authors pay special attention to issues that many engineers and students find difficult to understand. The first part of the book contains chapters on arithmetic, elementary and analytic geometry, algebra, differential and integral calculus, functions of complex variables, integral transforms, ordinary and partial differential equations, special functions, and probability theory. The second part discusses molecular physics and thermodynamics, electricity and magnetism, oscillations and waves, optics, special relativity, quantum mechanics, atomic and nuclear physics, and elementary particles. The third part covers dimensional analysis and similarity, mechanics of point masses and rigid bodies, strength of materials, hydrodynamics, mass and heat transfer, electrical engineering, and methods for constructing empirical and engineering formulas. The main text offers a concise, coherent survey of the most important definitions, formulas, equations, methods, theorems, and laws. Numerous examples throughout and references at the end of each chapter provide readers with a better understanding of the topics and methods. Additional issues of interest can be found in the remarks. For ease of reading, the supplement at the back of the book provides several long mathematical tables, including indefinite and definite integrals, direct and inverse integral transforms, and exact solutions of differential equations.

Here is a short, well-written book that covers the material essential for learning LaTeX. It includes incisive examples that teach LaTeX in a powerful yet abbreviated fashion. This is the handbook to have if you don't want to wade through extraneous material. It includes the following crucial features: numerous examples of widely used mathematical expressions; complete documents illustrating the creation of articles, reports, presentations, and posters; troubleshooting tips to help you pinpoint an error; details of how to set up an index and a bibliography; and information about online LaTeX resources. Why do you need to learn LaTeX? LaTeX has become an extremely popular typesetting system and is widely used throughout the sciences. As a student, you may need to typeset reports and theses in LaTeX (particularly if you are a graduate student in any mathematics or computer science discipline). Or you may be someone who had planned to "eventually" get around to learning LaTeX, but you are still using older or less appropriate methods of typesetting. Procrastinate no more! This second edition of the well-regarded and highly successful book includes additional material on the American Mathematical Society packages for typesetting additional mathematical symbols and multi-line displays; the BiBTeX program for creating bibliographies; the Beamer package for creating presentations; and the a0poster class for creating posters.

This book contains the most interesting problems from the first 24 years of the 'Mathematical Duel', an annual international mathematics competition between the students of four schools: the Gymnazium Mikulase Kopernika in Bilovec, Czech Republic, the Akademicki Zespol Szkol Ogolnoksztalcacych in Chorzow, Poland, the Bundesrealgymnasium Kepler in Graz, Austria and the Gymnazium Jakuba Skody in Prerov, Czech Republic.The problems are presented by topic, grouped under the headings Geometry, Combinatorics, Number Theory and Algebra, which is typical for olympiad-style competitions.Above all, it is of interest to students preparing for mathematics competitions as well as teachers looking for material to prepare their students, as well as mathematically interested enthusiasts from all walks of life looking for an intellectual challenge.

What is terrorism? What can we learn and what cannot we learn from terrorism data? What are the perspectives and limitations of the analysis of terrorism data? Over the last decade, scholars have generated unprecedented insight from the statistical analysis of ever-growing databases on terrorism. Yet their findings have not reached the public. This book translates the current state of knowledge on global patterns of terrorism free of unnecessary jargon. Readers will be gradually introduced to statistical reasoning and tools applied to critically analyze terrorism data within a rigorous framework. Debunking Seven Terrorism Myths Using Statistics communicates evidence-based research work on terrorism to a general audience. It describes key statistics that provide an overview of the extent and magnitude of terrorist events perpetrated by actors independent of state governments across the world. The books brings a coherent and rigorous methodological framework to address issues stemming from the statistical analysis of terrorism data and its interpretations. Features Uses statistical reasoning to identify and address seven major misconceptions about terrorism. Discusses the implications of major issues about terrorism data on the interpretation of its statistical analysis. Gradually introduces the complexity of statistical methods to familiarize the non-statistician reader with important statistical concepts to analyze data. Use illustrated examples to help the reader develop a critical approach applied to the quantitative analysis of terrorism data. Includes chapters focusing on major aspects of terrorism: definitional issues, lethality, geography, temporal and spatial patterns, and the predictive ability of models.

Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc. Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms. This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials.

Provides a comprehensive and updated study of GARCH models and their applications in finance, covering new developments in the discipline This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation, and tests. The book also provides new coverage of several extensions such as multivariate models, looks at financial applications, and explores the very validation of the models used. GARCH Models: Structure, Statistical Inference and Financial Applications, 2nd Edition features a new chapter on Parameter-Driven Volatility Models, which covers Stochastic Volatility Models and Markov Switching Volatility Models. A second new chapter titled Alternative Models for the Conditional Variance contains a section on Stochastic Recurrence Equations and additional material on EGARCH, Log-GARCH, GAS, MIDAS, and intraday volatility models, among others. The book is also updated with a more complete discussion of multivariate GARCH; a new section on Cholesky GARCH; a larger emphasis on the inference of multivariate GARCH models; a new set of corrected problems available online; and an up-to-date list of references. Features up-to-date coverage of the current research in the probability, statistics, and econometric theory of GARCH models Covers significant developments in the field, especially in multivariate models Contains completely renewed chapters with new topics and results Handles both theoretical and applied aspects Applies to researchers in different fields (time series, econometrics, finance) Includes numerous illustrations and applications to real financial series Presents a large collection of exercises with corrections Supplemented by a supporting website featuring R codes, Fortran programs, data sets and Problems with corrections GARCH Models, 2nd Edition is an authoritative, state-of-the-art reference that is ideal for graduate students, researchers, and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.