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Beginning Arithmetic Grade 1 Part 1, Lessons 1-85
by Rod And StaffWorkbook part 1 for Beginning Arithmetic 3rd Edition (current). This current 3rd edition is not compatible with the 2nd Edition (1991). Lessons 1-85. This course is designed to start at the beginning of the first grade. 170 lessons are bound in two workbook with 1 tear-out sheet for each lesson. No unit or chapter divisions are given. Money, measures, place value, fractions, and so forth are taught throughout the year while addition and subtraction facts are being drilled. The workbooks are supported by flash cards, speed drills, and oral drill.
Beginning Arithmetic Grade 2 Workbook 1
by Rod and Staff Publishers Inc.Grade 2 Student Book Unit 1 reviews addition and subtraction facts through 10 and other skills taught in Grade 1. Speed drills are now included in the student book.
Beginning Arithmetic, Grade 2, Workbook 2: Lessons 41-82
by Rod And StaffBeginning Arithmetic Grade 2 Workbook 2 Lessons 41-82
Beginning Arithmetic Practice Sheets Grade 2 Workbook 1
by Rod and Staff Publishers Inc.Rod & Staff tear-out practice sheets provide extra practice for each student. These are separated into two books; quizzes for the whole course are contained in Book 1.
Beginning Data Science with R
by Manas A. Pathak"We live in the age of data. In the last few years, the methodology of extracting insights from data or "data science" has emerged as a discipline in its own right. The R programming language has become one-stop solution for all types of data analysis. The growing popularity of R is due its statistical roots and a vast open source package library. The goal of "Beginning Data Science with R" is to introduce the readers to some of the useful data science techniques and their implementation with the R programming language. The book attempts to strike a balance between the how: specific processes and methodologies, and understanding the why: going over the intuition behind how a particular technique works, so that the reader can apply it to the problem at hand. This book will be useful for readers who are not familiar with statistics and the R programming language.
Beginning & Intermediate Algebra (Fifth Edition)
by Elayn Martin-GayThe book intends to provide a solid foundation in algebra for students who might not have previous experience in algebra.
Beginning Mathematica and Wolfram for Data Science: Applications in Data Analysis, Machine Learning, and Neural Networks
by Jalil Villalobos AlvaEnhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. This second edition introduces the latest LLM Wolfram capabilities, delves into the exploration of data types in Mathematica, covers key programming concepts, and includes code performance and debugging techniques for code optimization. You’ll gain a deeper understanding of data science from a theoretical and practical perspective using Mathematica and the Wolfram Language. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. Existing topics have been reorganized for better context and to accommodate the introduction of Notebook styles. The book also incorporates new functionalities in code versions 13 and 14 for imported and exported data. You’ll see how to use Mathematica, where data management and mathematical computations are needed. Along the way, you’ll appreciate how Mathematica provides an entirely integrated platform: its symbolic and numerical calculation result in a mized syntax, allowing it to carry out various processes without superfluous lines of code. You’ll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. What You Will Learn Create datasets, work with data frames, and create tables Import, export, analyze, and visualize data Work with the Wolfram data repository Build reports on the analysis Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering Who This Book Is For Data scientists who are new to using Wolfram and Mathematica as a programming language or tool. Programmers should have some prior programming experience, but can be new to the Wolfram language.
Beginning MATLAB and Simulink: From Novice to Professional
by Sulaymon EshkabilovEmploy essential and hands-on tools and functions of the MATLAB and Simulink packages, which are explained and demonstrated via interactive examples and case studies. This book contains dozens of simulation models and solved problems via m-files/scripts and Simulink models which help you to learn programming and modeling essentials. You’ll become efficient with many of the built-in tools and functions of MATLAB/Simulink while solving engineering and scientific computing problems. Beginning MATLAB and Simulink explains various practical issues of programming and modelling in parallel by comparing MATLAB and Simulink. After reading and using this book, you'll be proficient at using MATLAB and applying the source code from the book's examples as templates for your own projects in data science or engineering. What You Will LearnGet started using MATLAB and SimulinkCarry out data visualization with MATLABGain the programming and modeling essentials of MATLABBuild a GUI with MATLABWork with integration and numerical root finding methodsApply MATLAB to differential equations-based models and simulationsUse MATLAB for data science projectsWho This Book Is ForEngineers, programmers, data scientists, and students majoring in engineering and scientific computing.
Beginning MATLAB and Simulink: From Beginner to Pro
by Sulaymon EshkabilovEmploy essential tools and functions of the MATLAB and Simulink packages, which are explained and demonstrated via interactive examples and case studies. This revised edition covers features from the latest MATLAB 2022b release, as well as other features that have been released since the first edition published. This book contains dozens of simulation models and solved problems via m-files/scripts and Simulink models which will help you to learn programming and modelling essentials. You’ll become efficient with many of the built-in tools and functions of MATLAB/Simulink while solving engineering and scientific computing problems. Beginning MATLAB and Simulink, Second Edition explains various practical issues of programming and modelling in parallel by comparing MATLAB and Simulink. After studying and using this book, you'll be proficient at using MATLAB and Simulink and applying the source code and models from the book's examples as templates for your own projects in data science or engineering. What You Will LearnMaster the programming and modelling essentials of MATLAB and SimulinkCarry out data visualization with MATLABBuild a GUI and develop App with MATLABWork with integration and numerical root finding methodsApply MATLAB to differential equations-based models and simulationsUse MATLAB and Simulink for data science projects Who This Book Is ForEngineers, programmers, data scientists, and students majoring in engineering and scientific computing who are new to MATLAB and Simulink.
Beginning Neo4j
by Chris KemperThis book is your introduction in the world of graph databases, and the benefits they can bring to your applications. Neo4j is the most established graph database on the market, and it's always improving to bring more of its benefits to you. Beginning Neo4j will take you from the installation of Neo4j through to building a full application with Neo4j at its heart, and everything in between. Using this book, you'll get everything up and running, and then learn how to use Neo4j to build up recommendations, relationships, and calculate the shortest route between two locations. With example data models, and an application putting everything together, this book will give you everything you need to really get started with Neo4j. Neo4j is being used by social media and ecommerce industry giants. You can take advantage of Neo4j's powerful features and benefits - add Beginning Neo4j to your library today. What you'll learn Learn what Neo4j is and methods on how to install it Using Cypher, Neo4js query language to retrieve data Example data structures to help shape your application How to create relationships and recommendations between data Create an application that uses PHP and Neo4j together Taking your new Neo4j application into production Use Neo4j to calculate the shortest path between two locations Who this book is for This book is for anyone who wants to get started with graph databases, and doesn't know where to start. Table of Contents Chapter One: Introduction to Graph Databases Chapter Two: Getting to Know Neo4j Chapter Three: Get Up and Running with Neo4j Chapter Four: Meet Cypher Chapter Five: Managing Your Data in Neo4j Chapter Six: Importing and Exporting Data Chapter Seven: Using Data in Neo4j Chapter Eight: Building an App in Neo4j Chapter Nine: Neo4j in Production
Beginning Partial Differential Equations
by Peter V. O'NeilA broad introduction to PDEs with an emphasis on specialized topics and applications occurring in a variety of fieldsFeaturing a thoroughly revised presentation of topics, Beginning Partial Differential Equations, Third Edition provides a challenging, yet accessible,combination of techniques, applications, and introductory theory on the subjectof partial differential equations. The new edition offers nonstandard coverageon material including Burger's equation, the telegraph equation, damped wavemotion, and the use of characteristics to solve nonhomogeneous problems.The Third Edition is organized around four themes: methods of solution for initial-boundary value problems; applications of partial differential equations; existence and properties of solutions; and the use of software to experiment with graphics and carry out computations. With a primary focus on wave and diffusion processes, Beginning Partial Differential Equations, Third Edition also includes:Proofs of theorems incorporated within the topical presentation, such as the existence of a solution for the Dirichlet problemThe incorporation of MapleTM to perform computations and experimentsUnusual applications, such as Poe's pendulumAdvanced topical coverage of special functions, such as Bessel, Legendre polynomials, and spherical harmonicsFourier and Laplace transform techniques to solve important problemsBeginning of Partial Differential Equations, Third Edition is an ideal textbook for upper-undergraduate and first-year graduate-level courses in analysis and applied mathematics, science, and engineering.
Beginning Quantitative Research (The SAGE Quantitative Research Kit)
by Malcolm Williams Richard D. Wiggins W.P. VogtThis concise text provides a clear and digestible introduction to completing quantitative research. Taking you step-by-step through the process of completing your quantitative research project, it offers guidance on: · Formulating your research question · Completing literature reviews and meta-analysis · Formulating a research design and specifying your target population and data source · Choosing an appropriate method and analysing your findings Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.
Beginning Quantitative Research (The SAGE Quantitative Research Kit)
by Malcolm Williams Richard D. Wiggins W.P. VogtThis concise text provides a clear and digestible introduction to completing quantitative research. Taking you step-by-step through the process of completing your quantitative research project, it offers guidance on: · Formulating your research question · Completing literature reviews and meta-analysis · Formulating a research design and specifying your target population and data source · Choosing an appropriate method and analysing your findings Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.
Beginning R
by Mark GardenerConquer the complexities of this open source statistical languageR is fast becoming the de facto standard for statistical computing and analysis in science, business, engineering, and related fields. This book examines this complex language using simple statistical examples, showing how R operates in a user-friendly context. Both students and workers in fields that require extensive statistical analysis will find this book helpful as they learn to use R for simple summary statistics, hypothesis testing, creating graphs, regression, and much more. It covers formula notation, complex statistics, manipulating data and extracting components, and rudimentary programming.R, the open source statistical language increasingly used to handle statistics and produces publication-quality graphs, is notoriously complex This book makes R easier to understand through the use of simple statistical examples, teaching the necessary elements in the context in which R is actually usedCovers getting started with R and using it for simple summary statistics, hypothesis testing, and graphsShows how to use R for formula notation, complex statistics, manipulating data, extracting components, and regressionProvides beginning programming instruction for those who want to write their own scriptsBeginning R offers anyone who needs to perform statistical analysis the information necessary to use R with confidence.
Beginning R 4: From Beginner to Pro
by Matt Wiley Joshua F. WileyLearn how to use R 4, write and save R scripts, read in and write out data files, use built-in functions, and understand common statistical methods. This in-depth tutorial includes key R 4 features including a new color palette for charts, an enhanced reference counting system (useful for big data), and new data import settings for text (as well as the statistical methods to model text-based, categorical data). Each chapter starts with a list of learning outcomes and concludes with a summary of any R functions introduced in that chapter, along with exercises to test your new knowledge. The text opens with a hands-on installation of R and CRAN packages for both Windows and macOS. The bulk of the book is an introduction to statistical methods (non-proof-based, applied statistics) that relies heavily on R (and R visualizations) to understand, motivate, and conduct statistical tests and modeling.Beginning R 4 shows the use of R in specific cases such as ANOVA analysis, multiple and moderated regression, data visualization, hypothesis testing, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done.You will:Acquire and install R and RStudioImport and export data from multiple file formatsAnalyze data and generate graphics (including confidence intervals)Interactively conduct hypothesis testingCode multiple and moderated regression solutions Who This Book Is For Programmers and data analysts who are new to R. Some prior experience in programming is recommended.
Beginning Spring Boot 2: Applications and Microservices with the Spring Framework
by K. Siva Prasad ReddyLearn Spring Boot and how to build Java-based enterprise, web, and microservice applications with it. In this book, you'll see how to work with relational and NoSQL databases, build your first microservice, enterprise, or web application, and enhance that application with REST APIs. You'll also learn how to build reactive web applications using Spring Boot along with Spring Web Reactive. Then you'll secure your Spring Boot-created application or service before testing and deploying it. After reading and learning with Beginning Spring Boot 2, you'll have the skills and techniques to start building your first Spring Boot applications and microservices with confidence to take the next steps in your career journey. What You'll Learn Use Spring Boot autoconfiguration Work with relational and NoSQL databases Build web applications with Spring Boot Apply REST APIs using Spring Boot Cre ate reactive web applications using Spring Web Reactive Secure your Spring Boot applications or web services Test and deploy your Spring Boot applications Who This Book Is For Experienced Java and Spring Framework developers who are new to the new Spring Boot micro-framework.
Beginning Statistics: An Introduction for Social Scientists
by Liam Foster Ian Diamond Julie BantonIn the Second Edition of this bestselling textbook, the authors use real-world examples to introduce basic principles in statistics with no prior knowledge or experience assumed. With an emphasis on describing concepts, showing through example and illustrating points with graphs and displays, this book will provide readers with a step-by-step introduction to using statistics. Chapters address the following questions: Why bother learning statistics in the first place and are they relevant to real life? How do I make sensible tables and informative graphs? What are descriptive and inferential statistics and how are they used? What are regression and correlation anyway?
Beginning Statistics with Data Analysis
by Frederick Mosteller Robert E.K. Rourke Stephen E. FienbergThis introduction to the world of statistics draws examples from a broad range of applications, including business, government, medicine, social sciences, biology, and everyday life. The first four chapters present a brief course in exploratory data analysis. The authors place special emphasis on methods for collecting data and offer an elementary treatment of multiple regression. A concluding chapter on the design of comparative experiments links the basic ideas of design to previously discussed methods of analysis. Each chapter and section concludes with review problems. Suitable for undergraduate students, the text requires only one year of high school algebra. This authoritative treatment was co-written by Frederick Mosteller, one of the twentieth century's preeminent statisticians and the founding chairman of Harvard's Department of Statistics, and Stephen Fienberg, a leader of statistics departments at the University of Minnesota and Carnegie Mellon University and who is internationally known for innovative statistical research and its application to social science and public policy.
Begründen bei Geometrieaufgaben der Grundschule: Eine Untersuchung der schulischen Anforderungen und der Kompetenzen im dritten und vierten Schuljahr (Perspektiven der Mathematikdidaktik)
by Stephanie GerloffIn dem vorliegenden Buch widmet sich Stephanie Gerloff dem schriftlichen Begründen im Geometrieunterricht der Grundschule. Ausgehend von einer umfassenden theoretischen Analyse der vorliegenden Begriffsauffassungen zum Begründen und Argumentieren wird das Begründen als Anforderung und als Kompetenz in zwei empirischen Teilen näher untersucht. Dafür befasst sich ein erster empirischer Teil mit dem Begründen in Geometrieaufgaben und zeigt mithilfe einer Schulbuchanalyse die vielfältigen Möglichkeiten impliziter wie expliziter Aufgabenformate zum Begründen auf. Ein zweiter empirischer Teil befasst sich mit der demgegenüber vorliegenden Kompetenz von Grundschulkindern der dritten und vierten Klasse. Die dazu vorliegenden Ergebnisse ermöglichen sowohl eine qualitative Ausdifferenzierung der Begründungskompetenz mithilfe eines entwickelten Niveaustufenmodells als auch eine quantitative Einschätzung des vorliegenden Begründungspotentials von Grundschulkindern.
Begründungsprozesse im Kontext von: Wissensentwicklung auf der Grundlage empirischer Settings (MINTUS – Beiträge zur mathematisch-naturwissenschaftlichen Bildung)
by Frederik DillingDas vorliegende Buch beschäftigt sich mit Begründungsprozessen von Schüler*innen auf der Grundlage empirischer Settings. Hierunter werden Lernumgebungen verstanden, in denen empirische Objekte im Sinne des wissenschaftstheoretischen Strukturalismus eine bedeutende Rolle spielen. Zu diesem Zweck wird ein theoretisches Modell entwickelt (CSC-Modell) und in Fallstudien zur Beschreibung von Lernprozessen von Schüler*innen mit verschiedenen (digitalen) Medien angewendet.
Behavior Analysis with Machine Learning Using R (Chapman & Hall/CRC The R Series)
by Enrique Garcia CejaBehavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.
Behavior and Evolutionary Dynamics in Crowd Networks: An Evolutionary Game Approach (Lecture Notes in Social Networks)
by Yan Chen H. Vicky ZhaoThis book offers a holistic framework to study behavior and evolutionary dynamics in large-scale, decentralized, and heterogeneous crowd networks. In the emerging crowd cyber-ecosystems, millions of deeply connected individuals, smart devices, government agencies, and enterprises actively interact with each other and influence each other’s decisions. It is crucial to understand such intelligent entities’ behaviors and to study their strategic interactions in order to provide important guidelines on the design of reliable networks capable of predicting and preventing detrimental events with negative impacts on our society and economy.This book reviews the fundamental methodologies to study user interactions and evolutionary dynamics in crowd networks and discusses recent advances in this emerging interdisciplinary research field. Using information diffusion over social networks as an example, it presents a thorough investigation of the impact of user behavior on the network evolution process and demonstrates how this can help improve network performance.Intended for graduate students and researchers from various disciplines, including but not limited to, data science, networking, signal processing, complex systems, and economics, the book encourages researchers in related research fields to explore the many untouched areas in this domain, and ultimately to design crowd networks with efficient, effective, and reliable services.
Behavior Engineering and Applications (International Series on Computer Entertainment and Media Technology)
by Raymond Wong Chi-Hung Chi Patrick C. HungMany industrial applications built today are increasingly using emerging behavior engineering technologies: this book looks at various research and practical issues for researchers and students working in computer science and engineering, and for industry technology providers interested in behavior engineering and applications. Behavior Engineering and Applications encompasses intelligent and efficient computational solutions, including models, architectures, algorithms and specific applications, focused on processing, discovering, understanding and analyzing the behavior captured by the above data. Focusing on applying any engineering paradigm to systemically process, discover, understand and analyze these data, this book also addresses problems in a variety of areas and applications that related to behavior engineering.This book includes chapters derived from selected papers from The 2016 International Conference on Behavior Engineering (ICBE), as well as separate contributions the editors selected cutting-edge research related to behavior engineering.
Behavior of Materials under Impact, Explosion, High Pressures and Dynamic Strain Rates (Advanced Structured Materials #176)
by Maxim Yu. Orlov Visakh P. M.This book presents the results of experimental and theoretical studies of the destruction of solids under impact, explosion, high pressures, and strain rates. The content identifies the basic laws of the destruction of bodies under dynamic loads. The results of numerical studies were obtained using numerical methods on the Lagrangian, Euler, and ALE approaches to the description of the motion of continuous media. Numerical methods and mathematical models have been tested by comparison with experimental data and well-known analytical solutions (for instance, Rankin–Hugoniot laws). Experimental studies were performed on unique ballistic installations with the registration of fast processes (high-speed shooting). The results are used as new tests to verify the developing modeling methods. The research objects were metal multilayer plates, functionally graded materials, advanced, smart, and natural materials, etc. The book is interesting to specialists in the field of mathematical modeling and experimental methods for studying fast processes under dynamic loading.
Behavioral Computational Social Science (Wiley Series in Computational and Quantitative Social Science)
by Riccardo BoeroThis book is organized in two parts: the first part introduces the reader to all the concepts, tools and references that are required to start conducting research in behavioral computational social science. The methodological reasons for integrating the two approaches are also presented from the individual and separated viewpoints of the two approaches.The second part of the book, presents all the advanced methodological and technical aspects that are relevant for the proposed integration. Several contributions which effectively merge the computational and the behavioral approaches are presented and discussed throughout