- Table View
- List View
Learning R for Geospatial Analysis
by Michael DormanThis book is intended for anyone who wants to learn how to efficiently analyze geospatial data with R, including GIS analysts, researchers, educators, and students who work with spatial data and who are interested in expanding their capabilities through programming. The book assumes familiarity with the basic geographic information concepts (such as spatial coordinates), but no prior experience with R and/or programming is required. By focusing on R exclusively, you will not need to depend on any external software--a working installation of R is all that is necessary to begin.
Learning R: A Step-by-Step Function Guide to Data Analysis
by Richard CottonLearn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, youâ??ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what youâ??ve learned, and concludes with exercises, most of which involve writing R code.Write a simple R program, and discover what the language can doUse data types such as vectors, arrays, lists, data frames, and stringsExecute code conditionally or repeatedly with branches and loopsApply R add-on packages, and package your own work for othersLearn how to clean data you import from a variety of sourcesUnderstand data through visualization and summary statisticsUse statistical models to pass quantitative judgments about data and make predictionsLearn what to do when things go wrong while writing data analysis code
Learning RStudio for R Statistical Computing
by Edwin De Jonge Mark Van LooA practical tutorial covering how to leverage RStudio functionality to effectively perform R Development, analysis, and reporting with RStudio. The book is aimed at R developers and analysts who wish to do R statistical development while taking advantage of RStudio functionality to ease their development efforts. Familiarity with R is assumed. Those who want to get started with R development using RStudio will also find the book useful. Even if you already use R but want to create reproducible statistical analysis projects or extend R with self-written packages, this book shows how to quickly achieve this using RStudio.
Learning Regression Analysis by Simulation
by Kunio TakezawaThe standard approach of most introductory books for practical statistics is that readers first learn the minimum mathematical basics of statistics and rudimentary concepts of statistical methodology. They then are given examples of analyses of data obtained from natural and social phenomena so that they can grasp practical definitions of statistical methods. Finally they go on to acquaint themselves with statistical software for the PC and analyze similar data to expand and deepen their understanding of statistical methods. This book, however, takes a slightly different approach, using simulation data instead of actual data to illustrate the functions of statistical methods. Also, R programs listed in the book help readers realize clearly how these methods work to bring intrinsic values of data to the surface. R is free software enabling users to handle vectors, matrices, data frames, and so on. For example, when a statistical theory indicates that an event happens with a 5 % probability, readers can confirm the fact using R programs that this event actually occurs with roughly that probability, by handling data generated by pseudo-random numbers. Simulation gives readers populations with known backgrounds and the nature of the population can be adjusted easily. This feature of the simulation data helps provide a clear picture of statistical methods painlessly. Most readers of introductory books of statistics for practical purposes do not like complex mathematical formulae, but they do not mind using a PC to produce various numbers and graphs by handling a huge variety of numbers. If they know the characteristics of these numbers beforehand, they treat them with ease. Struggling with actual data should come later. Conventional books on this topic frighten readers by presenting unidentified data to them indiscriminately. This book provides a new path to statistical concepts and practical skills in a readily accessible manner.
Learning SAS by Example: A Programmer's Guide
by Ron CodyLearn to program SAS by example! Learning SAS by Example: A Programmer’s Guide, Second Edition, teaches SAS programming from very basic concepts to more advanced topics. Because most programmers prefer examples rather than reference-type syntax, this book uses short examples to explain each topic. The second edition has brought this classic book on SAS programming up to the latest SAS version, with new chapters that cover topics such as PROC SGPLOT and Perl regular expressions. This book belongs on the shelf (or e-book reader) of anyone who programs in SAS, from those with little programming experience who want to learn SAS to intermediate and even advanced SAS programmers who want to learn new techniques or identify new ways to accomplish existing tasks. <P><P>In an instructive and conversational tone, author Ron Cody clearly explains each programming technique and then illustrates it with one or more real-life examples, followed by a detailed description of how the program works. The text is divided into four major sections: Getting Started, DATA Step Processing, Presenting and Summarizing Your Data, and Advanced Topics. Subjects addressed include
Learning SciPy for Numerical and Scientific Computing
by Francisco J. Blanco-SilvaA step-by-step practical tutorial with plenty of examples on research-based problems from various areas of science, that prove how simple, yet effective, it is to provide solutions based on SciPy.This book is targeted at anyone with basic knowledge of Python, a somewhat advanced command of mathematics/physics, and an interest in engineering or scientific applications---this is broadly what we refer to as scientific computing.This book will be of critical importance to programmers and scientists who have basic Python knowledge and would like to be able to do scientific and numerical computations with SciPy.
Learning SciPy for Numerical and Scientific Computing - Second Edition
by Sergio J. Rojas G. Erik A ChristensenThis book targets programmers and scientists who have basic Python knowledge and who are keen to perform scientific and numerical computations with SciPy.
Learning Spark: Lightning-Fast Data Analytics
by Denny Lee Jules S. Damji Brooke Wenig Tathagata DasData is bigger, arrives faster, and comes in a variety of formatsâ??and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark.Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, youâ??ll be able to:Learn Python, SQL, Scala, or Java high-level Structured APIsUnderstand Spark operations and SQL EngineInspect, tune, and debug Spark operations with Spark configurations and Spark UIConnect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or KafkaPerform analytics on batch and streaming data using Structured StreamingBuild reliable data pipelines with open source Delta Lake and SparkDevelop machine learning pipelines with MLlib and productionize models using MLflow
Learning Statistics Using R
by Randall E. SchumackerProviding easy-to-use R script programs that teach descriptive statistics, graphing, and other statistical methods, Learning Statistics Using R shows readers how to run and utilize R, a free integrated statistical suite that has an extensive library of functions. Schumacker’s comprehensive book describes the processing of variables in statistical procedures. Covering a wide range of topics, from probability and sampling distribution to statistical theorems and chi-square, this introductory book helps readers learn not only how to use formulae to calculate statistics, but also how specific statistics fit into the overall research process. Each chapter includes discussion and explanations, tables and graphs, and R functions and outputs to enrich readers' understanding of statistics through statistical computing and modeling.
Learning Statistics Using R
by Randall E. SchumackerProviding easy-to-use R script programs that teach descriptive statistics, graphing, and other statistical methods, Learning Statistics Using R shows readers how to run and utilize R, a free integrated statistical suite that has an extensive library of functions. Schumacker’s comprehensive book describes the processing of variables in statistical procedures. Covering a wide range of topics, from probability and sampling distribution to statistical theorems and chi-square, this introductory book helps readers learn not only how to use formulae to calculate statistics, but also how specific statistics fit into the overall research process. Each chapter includes discussion and explanations, tables and graphs, and R functions and outputs to enrich readers' understanding of statistics through statistical computing and modeling.
Learning Tableau
by Joshua N. MilliganIf you want to understand your data using data visualization and don't know where to start, then this is the book for you. Whether you are a beginner or have years of experience, this book will help you to quickly acquire the skills and techniques used to discover, analyze, and communicate data visually. Some familiarity with databases and data structures is helpful, but not required.
Learning Through Teaching Mathematics
by Rina Zazkis Roza LeikinThis volume explores how and when teachers' knowledge develops through teaching. The book presents international views on teachers' learning from their practice; the chapters are written by mathematicians or mathematics educators from Brazil, Canada, Israel, Mexico, UK, and USA. They address diverse content - numerical literacy, geometry, algebra, and real analysis - and a variety of levels - elementary school, secondary school, undergraduate mathematics, and teacher education courses. The authors employ different methodological tools and different theoretical perspectives as they consider teaching in different learning environments: lecturing, small group work on problems and tasks, mathematical explorations with the support of technological software, or e-learning. Despite these differences, the authors exemplify and analyze teachers' learning that occurred and address the question: "What kinds of knowledge are developed as a result of teaching mathematics and what are the factors that support or impede such development?" Further, the chapters explore interactions and interrelationships between the enhancement of mathematical and pedagogical knowledge. The important and original contribution of this book is that it ties together the notions of teachers' knowledge and complexity of teacher's work, while presenting them from a relatively unexplored perspective - learning through teaching mathematics.
Learning To Love Math: Teaching Strategies that Change Student Attitudes and Get Results
by Judy WillisIs there a way to get students to love math? Dr. Judy Willis responds with an emphatic yes in this informative guide to getting better results in math class. Tapping into abundant research on how the brain works, Willis presents a practical approach for how we can improve academic results by demonstrating certain behaviors and teaching students in a way that minimizes negativity. With a straightforward and accessible style, Willis shares the knowledge and experience she has gained through her dual careers as a math teacher and a neurologist. In addition to learning basic brain anatomy and function, readers will learn how to* Improve deep-seated negative attitudes toward math. * Plan lessons with the goal of "achievable challenge" in mind. * Reduce mistake anxiety with techniques such as errorless math and estimation. * Teach to different individual learning strengths and skill levels. * Spark motivation. * Relate math to students' personal interests and goals. * Support students in setting short-term and long-term goals. * Convince students that they can change their intelligence. With dozens of strategies teachers can use right now, Learning to Love Math puts the power of research directly into the hands of educators. A Brain Owner's Manual, which dives deeper into the structure and function of the brain, is also included--providing a clear explanation of how memories are formed and how skills are learned. With informed teachers guiding them, students will discover that they can build a better brain . . . and learn to love math!
Learning To Teach in an Age of Accountability
by Arthur T. Costigan Karen Kepler Zumwalt Margaret Smith CroccoThis book documents the "brave new world" of teacher, administrator, school, and student accountability that has swept across the United States in recent years. Its particular vantage point is the perspective of dozens of new teachers trying to make their way through their first months and years working in schools in the New York City metropolitan area. The issues they grapple with are not, however, unique to this context, but common problems found today in urban, suburban, and rural schools across the United States. The stories in this book offer a compelling portrait of these teachers' encounters with the new culture of accountability and the strategies they develop for coping, even succeeding, within such demanding settings. Learning to Teach in an Age of Accountability: *introduces research on teaching and engages the "big ideas" concerning teacher research, highlighting what we know and where that leads us; *offers a rich set of teacher narratives that are organized to widen the angle of vision from biography, to classrooms, schools, and society; and *includes questions and activities to encourage discussion and further research about the ideas raised; and *addresses the possibilities for best practice and curricular decision making in light of the issues and ideas presented in the book. This volume--unique in its portrayal of new teachers' encounters with issues of accountability--makes a singular contribution to the educational literature on new teachers. It is relevant to everyone interested in the contemporary world of teaching, and is particularly appropriate as a text for preservice and in-service students. All readers who believe that the key to a good school lies in attracting and keeping good teachers will find the issues presented here both personally engaging and deeply troubling.
Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
by Bernhard Schölkopf Alexander J. Smola Francis BachLearning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.
Learning Xero
by Jon JenkinsLearn to use Xero to make bookkeeping tasks simple and gain valuable business insights effortlessly About This Book * Explore the process of setting up and using Xero * Concise step-by-step instructions to teach you best practice bookkeeping * Discover performance enhancing add-ons to reduce your daily work Who This Book Is For Intended for those who want to learn how to use Xero to get better insights from their business data and learn the best practices of bookkeeping using Xero. Perhaps you have never used Xero, or perhaps you want to migrate from an existing accounting application to Xero. In either case, this book will get you up and running quickly. It would be useful to have a bit of familiarity with basic bookkeeping concepts, but no prior experience of Xero is required. What You Will Learn * Configure Xero from scratch and fine-tune it ready for use * Set up bank feeds and automate the bank reconciliation process * Create workflows and segregation of duties for sales and purchases * Run payrolls, giving employees the ability to request time off and generate their own payslips * Produce reports to gain a better understanding of your business data and make better quality decisions * Import and export data ready for analysis * Use the power of a mobile device to run your business from the palm of your hand * Manage your inventory with fully automated transaction processing In Detail The book begins by tackling the initial set up of Xero to ensure optimum configuration for success. From there, the next logical step is to set up the automated bank feeds, which is the best innovation in bookkeeping in years. Now that your bank data is ready for importing, we will tackle the most common transactional items, being sales invoices and purchase bills. Despite these being largely transactional, we will work through ways to automate the process where possible, save time, and avoid potential human errors along the way. Then we will start checking reports and analyze what is working or not and make changes to workflows and setups. The end result is that you will have a fully configured system ready to use and years of experience offering best practice solutions to what have been, for years, unnecessary roadblocks in your business. Style and approach This book contains easy-to-follow, step-by-step examples, explaining from start to finish how to set up and use Xero while implementing best practices of bookkeeping.
Learning YARN
by Akhil Arora Shrey MehrotraThis book is intended for those who want to understand what YARN is and how to efficiently used it for resource management of large clusters. For cluster administrators, this book gives a detailed explanation of provisioning and managing YARN clusters. If you are an or a Java developer or an open-source contributor, this book will help you to drill down the YARN architecture and application execution phases. This book would also help big data engineers explore YARN integration with real-time analytics technologies like Spark and Storm.
Learning and Intelligent Optimization
by Panos Pardalos Giuseppe NicosiaThis book constitutes the proceedings of the 7th International Conference on Learning and Optimization, LION 7, which was held in Catania, Italy, in January 2013. The 49 contributions presented in this volume were carefully reviewed and selected from 101 submissions. They explore the intersections and uncharted territories between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems.
Learning and Intelligent Optimization
by Paola Festa Meinolf Sellmann Joaquin VanschorenThis book constitutes the thoroughly refereed post-conference proceedings of the 10th International Conference on Learning and Optimization, LION 10, which was held on Ischia, Italy, in May/June 2016. The 14 full papers presented together with 9 short papers and 2 GENOPT papers were carefully reviewed and selected from 47 submissions. The papers address all fields between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems. Special focus is given to new ideas and methods; challenges and opportunities in various application areas; general trends, and specific developments.
Learning and Intelligent Optimization: 13th International Conference, LION 13, Chania, Crete, Greece, May 27–31, 2019, Revised Selected Papers (Lecture Notes in Computer Science #11968)
by Panos Pardalos Nikolaos F. Matsatsinis Yannis MarinakisThis book constitutes the thoroughly refereed pChania, Crete, Greece, in May 2019. The 38 full papers presented have been carefully reviewed and selected from 52 submissions. The papers focus on advancedresearch developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence and describe advanced ideas, technologies, methods, and applications in optimization and machine learning.
Learning and Intelligent Optimization: 14th International Conference, LION 14, Athens, Greece, May 24–28, 2020, Revised Selected Papers (Lecture Notes in Computer Science #12096)
by Panos M. Pardalos Ilias S. KotsireasThis book constitutes the refereed post-conference proceedings on Learning and Intelligent Optimization, LION 14, held in Athens, Greece, in May 2020. The 37 full papers presented together with one invited paper have been carefully reviewed and selected from 75 submissions. LION deals with designing and engineering ways of "learning" about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components. Due to the COVID-19 pandemic, LION 14 was not held as a physical meeting.
Learning and Intelligent Optimization: 17th International Conference, LION 17, Nice, France, June 4–8, 2023, Revised Selected Papers (Lecture Notes in Computer Science #14286)
by Kevin Tierney Meinolf SellmannThis book constitutes the refereed proceedings of the 17th International Conference on Learning and Intelligent Optimization, LION-17, held in Nice, France, during June 4–8, 2023.The 40 full papers presented have been carefully reviewed and selected from 83 submissions. They focus on all aspects of unleashing the potential of integrating machine learning and optimization approaches, including automatic heuristic selection, intelligent restart strategies, predict-then-optimize, Bayesian optimization, and learning to optimize.
Learning and Intelligent Optimization: 18th International Conference, LION 18, Ischia Island, Italy, June 9–13, 2024, Revised Selected Papers (Lecture Notes in Computer Science #14990)
by Paola Festa Ornella Pisacane Daniele Ferone Tommaso PastoreThis book constitutes the refereed proceedings of the 18th International Conference on Learning and Intelligent Optimization, LION 18, held in Ischia Island, Italy, in June 2024. The 31 full papers and 4 short papers presented in these proceedings were carefully reviewed and selected from 58 submissions. These papers focus on the current research, challenges and applications in the fields of Artificial Intelligent, Machine Learning and Operations Research.
Learning and Teaching Early Math: The Learning Trajectories Approach (Studies in Mathematical Thinking and Learning Series)
by Douglas H. Clements Julie SaramaThe third edition of this significant and groundbreaking book summarizes current research into how young children learn mathematics and how best to develop foundational knowledge to realize more effective teaching. Using straightforward, practical language, early math experts Douglas Clements and Julie Sarama show how learning trajectories help teachers understand children’s level of mathematical understanding and lead to better teaching. By focusing on the inherent delight and curiosity behind young children’s mathematical reasoning, learning trajectories ultimately make teaching more joyous: helping teachers understand the varying levels of knowledge exhibited by individual students, it allows them to better meet the learning needs of all children. This thoroughly revised and contemporary third edition of Learning and Teaching Early Math remains the definitive, research-based resource to help teachers understand the learning trajectories of early mathematics and become confident, credible professionals. The new edition draws on numerous new research studies, offers expanded international examples, and includes updated illustrations throughout. This new edition is closely linked with Learning and Teaching with Learning Trajectories–[LT]²–an open-access, web-based tool for early childhood educators to learn about how children think and learn about mathematics. Head to LearningTrajectories.org for ongoing updates, interactive games, and practical tools that support classroom learning.
Learning and Teaching Early Math: The Learning Trajectories Approach (Studies in Mathematical Thinking and Learning Series)
by Douglas H. Clements Julie SaramaIn this important book for pre- and in-service teachers, early math experts Douglas Clements and Julie Sarama show how "learning trajectories" help diagnose a child's level of mathematical understanding and provide guidance for teaching. By focusing on the inherent delight and curiosity behind young children's mathematical reasoning, learning trajectories ultimately make teaching more joyous. They help teachers understand the varying levels of knowledge exhibited by individual students, which in turn allows them to better meet the learning needs of all children. Using straightforward, no-nonsense language, this book summarizes the current research about how children learn mathematics, and how to build on what children already know to realize more effective teaching. This second edition of Learning and Teaching Early Math remains the definitive, research-based resource to help teachers understand the learning trajectories of early mathematics and become quintessential professionals. Updates to the new edition include: * Explicit connections between Learning Trajectories and the new Common Core State Standards. * New coverage of patterns and patterning. * A companion website featuring student support materials such as a glossary of technical terms and pedagogical activities. * Incorporation of hundreds of recent research studies.