The first chapter describes basic features of the program and shows how to use it in simple arithmetic operations with scalars. The next two chapters focus on the topic of arrays (the basis of MATLAB), while the remaining text covers a wide range of other applications. Computer screens, tutorials, samples, and homework questions in math, science, and engineering, provide the student with the practical hands-on experience needed for total proficiency.

    This student-centered, problem-based approach to learning is a central theme of this book. Learning how best to help children believe that mathematics makes sense and that they themselves can make sense of mathematics is an exciting endeavor and a lifelong process. It requires the knowledge gained from research, the wisdom shared by professional colleagues, and the insightful ideas that come from your own daily experiences with students. I hope that this book will assist you on this fantastic journey. Praise for Elementary and Middle School Mathematics This is absolutely the best book on the market. No other book comes close.–Sandra L. McCune, Stephen F. Austin University I particularly like the emphasis on doing math more than one way (invented math) and the emphasis on problem solving. There is a wealth of resources and children’s books for students to use in their development of concepts.–Carol Geller, Radford University This text is not only a valuable tool in making sense of and encouraging the use of mathematics by preservice teachers, but also for those educators already in the field. –Marilyn Nash, Indiana University South Bend About the AuthorJohn A. Van de Walle is Professor Emeritus, Virginia Commonwealth University. As a mathematics education consultant, he regularly works with K—8 teachers and in elementaryschool classrooms and has taught mathematics to children at all levels, K—8. He is also a coauthor of the Scott Foresman, Addison Wesley Mathematics K,6 series. Jennifer M. Bay-Williams has written a Field Experience Guide to accompany the text. Organized to cover all NCATE and NCTM standards and correlated to all chapters of the text, the Field Experience Guide is packed with field-based assignments and reproducible forms to record your experiences. The guide also includes additional activities for students, full-size versions of the Blackline Masters in the text, and 16 extended lesson plans written by John Van de Walle. For more information or to purchase a copy, visit www.ablongman.com and search for the ISBN (0-205-49314-9.

    It moves beyond a mere study of algorithms without sacrificing the rigor that faculty desire. As in every edition, Winston reinforces the book's successful features and coverage with the most recent developments in the field. The Student Suite CD-ROM, which now accompanies every new copy of the text, contains the latest versions of commercial software for optimization, simulation, and decision analysis.

    "Interpersonal Communication, 2e" demonstrates how effective interpersonal communication can make students' lives better. With careful consideration given to the impact of computer-mediated communication, the program reflects the rapid changes of the modern world in which today's students live and interact. The program also helps students understand and build interpersonal skills and choices for their academic, personal, and professional lives.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    The Black Book doesn't include any vocabulary lists, and doesn't advise its readers to rely on math formulas.Instead, The SAT Prep Black Book teaches readers to approach the test the same way that Barrett trains his students. Barrett sees the SAT as a standardized instrument with a specific objective, and reasons that the test must observe certain design guidelines that follow from that unique objective. So this book explains to students how different types of SAT questions are actually written, from the perspective of the College Board: for each question type, Barrett explains the "big secret" of that question type, the rules and patterns that all questions of that type will follow, and, most importantly for readers, exactly how to attack the question type based on the inherent weaknesses of its particular rules and patterns.The book includes over 250 example solutions that demonstrate Barrett's approach against real SAT questions written by the College Board. In order to follow along, students will want a copy of the College Board's book "The Official SAT Study Guide," the source of the questions whose solutions appear in the Black Book.The overall lesson of the SAT Prep Black Book is that the SAT tests very basic concepts in very strange ways, so students should learn the unique habits of the test rather than cramming definitions and formulas. At 330 pages, the book is thorough and detailed in its analysis of the SAT.

    Stevenson's careful explanations and approachable format supports students in understanding the important operations management concepts as well as applying tools and methods. By providing detailed examples, solved problems, questions, and cases students learn by doing, and the Tenth Edition continues to offer more support for 'doing Operations' than any other.

    While the text is descriptive (largely conceptual) it does provide quantitative material, including worked examples in optional boxed sections.

    Each issue is thoughtfully framed with an issue summary, an issue introduction, and a postscript. An instructor's manual with testing material is available for each volume. Using Taking Sides in the Classroom is also an excellent instructor resource with practical suggestions on incorporating this effective approach in the classroom. Each Taking Sides reader features an annotated listing of selected World Wide Web sites and is supported by a student website.

    KEY TOPICS: This classic book enables readers to make connections between classical and modern physics - an indispensable part of a physicist's education. In this new edition, Beams Medal winner Charles Poole and John Safko have updated the book to include the latest topics, applications, and notation, to reflect today's physics curriculum. They introduce readers to the increasingly important role that nonlinearities play in contemporary applications of classical mechanics. New numerical exercises help readers to develop skills in how to use computer techniques to solve problems in physics. Mathematical techniques are presented in detail so that the book remains fully accessible to readers who have not had an intermediate course in classical mechanics. MARKET: For college instructors and students.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

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    It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of "data science" for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (or MOOC) offered by the pioneering MIT--Harvard collaboration edX.Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming.Introduction to Computation and Programming Using Python can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.

    This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

    The approach, which focuses on intersecting inequalities, illustrates how racism, social class, ageism and heterosexism can compound the consequences of gender inequality. Although it focuses primarily on women and men in the United States, international issues and data are incorporated throughout.