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Learning ICT with Maths (Teaching ICT through the Primary Curriculum)
by Richard BennettProviding practical guidance on enhancing learning through ICT in maths, this book is made up of a series of projects that supplement, augment and extend the QCA ICT scheme and provide much-needed links with Units in other subjects’ schemes of work. It includes: fact cards that support each project and clearly outline its benefits in relation to teaching and learning examples of how activities work in 'real' classrooms links to research, inspection evidence and background reading to support each project adaptable planning examples and practical ideas provided on an accompanying CD ROM. Suitable for all trainee and practising primary teachers.
Learning IPython for Interactive Computing and Data Visualization
by Cyrille RossantA practical hands-on guide which focuses on interactive programming, numerical computing, and data analysis with IPython.This book is for Python developers who use Python as a scripting language or for software development, and are interested in learning IPython for increasing their productivity during interactive sessions in the console. Knowledge of Python is required, whereas no knowledge of IPython is necessary.
Learning MATLAB
by Walter GanderThis comprehensive and stimulating introduction to Matlab, a computer language now widely used for technical computing, is based on an introductory course held at Qian Weichang College, Shanghai University, in the fall of 2014. Teaching and learning a substantial programming language aren't always straightforward tasks. Accordingly, this textbook is not meant to cover the whole range of this high-performance technical programming environment, but to motivate first- and second-year undergraduate students in mathematics and computer science to learn Matlab by studying representative problems, developing algorithms and programming them in Matlab. While several topics are taken from the field of scientific computing, the main emphasis is on programming. A wealth of examples are completely discussed and solved, allowing students to learn Matlab by doing: by solving problems, comparing approaches and assessing the proposed solutions.
Learning Mathematics by Cultural-Historical Theory Implementation: Understanding Vygotsky’s Approach (Early Childhood Research and Education: An Inter-theoretical Focus #7)
by Aleksander Veraksa Yulia SolovievaThis book is devoted to the topic of mathematical skills development, which was the focus of Vygotsky's cultural-historical theory. It offers descriptions of studies of development of visual modelling in children and its use for educational purposes. Special attention is given to concrete examples of Vygotsky’s work and educational programs that makes it possible to replicate the results in various settings. The work also addresses conditions, means and predictors of mathematical concepts acquisition at different ages and educational levels (preschool, primary and middle secondary education). The book shows theoretical solidity of cultural-historical approach and experience of its implementation in teaching of mathematical knowledge in childhood and the study of the process of psychological development.
Learning Mathematics in Elementary and Middle School: A Learner-Centered Approach
by George S. Cathcart Yvonne M. Pothier James H. Vance Nadine S. BezukThis popular text promotes a learner-centered approach to teaching elementary and middle school mathematics. It provides valuable research-based instructional strategies, resources, and activities to help teachers evaluate how children think mathematically and how to link that knowledge to developmentally appropriate teaching practice. With its strong focus on Common Core Standards and analyzing children’s work to meet the individual needs of students, the book helps ensure that all students and teachers can be successful with math. The new edition features integration of the Common Core State Standards throughout, new examples of children’s work, updated internet links, expanded videos of children and classrooms, expanded “In Practice” features, and updated research on mathematics teaching and learning.
Learning Mathematics in a Mobile App-Supported Math Trail Environment (SpringerBriefs in Education)
by Adi Nur CahyonoThis brief presents the results of a study on the development of the mobile app-supported math trail program for learning mathematics. This study is a part of the MathCityMap-Project, a project of the MATIS I Team from IDMI Goethe-Universität Frankfurt, Germany, that comprises math trails around the city that are supported by the use of GPS-enabled mobile phone technology. The project offers an activity that is designed to support students in constructing their own mathematical knowledge by solving the prepared mathematical tasks on the math trail and interacting with the environment, including the digital environment. The brief focuses specifically on the development of a model for a mobile app-supported math trail programme and the implementation of this programme in Indonesia. It offers both an empirical exploration of its implementation as well as critical assessment of students’ motivation in mathematics, their own performance, as well as teachers’ mathematics beliefs. It concludes with a future-forward perspective by recommending strategies for implementation in schools, among the general public of the existing math trails (including its supporting tool). It also discusses strategies for developing and designing new trails and suggests further research in other geographical regions and contexts for continued project development and implementation. Learning Mathematics in a Mobile App-Supported Math Trail Environment articulates an innovative and exciting future for integrating real mathematical tasks and geographic and digital environment into effective mathematics education.
Learning Mathematics in the Context of 3D Printing: Proceedings of the International Symposium on 3D Printing in Mathematics Education (MINTUS – Beiträge zur mathematisch-naturwissenschaftlichen Bildung)
by Frederik Dilling Felicitas Pielsticker Ingo WitzkeThe volume presents a collection of articles on the use of 3D printing technology in mathematics education and in mathematics teacher training. It contains both basic research-oriented contributions as well as reflected descriptions of concrete developments for teaching. The authors of this compilation share a positive attitude towards the possibilities that the use of 3D printing technology (understood as an interplay of software and hardware) can unfold for mathematics education, but critically evaluate from a mathematics education research perspective when, where and how an application can enable an added value for the learning of a mathematical content.
Learning Microeconometrics with R (Chapman & Hall/CRC The R Series)
by Christopher P. AdamsThis book provides an introduction to the field of microeconometrics through the use of R. The focus is on applying current learning from the field to real world problems. It uses R to both teach the concepts of the field and show the reader how the techniques can be used. It is aimed at the general reader with the equivalent of a bachelor’s degree in economics, statistics or some more technical field. It covers the standard tools of microeconometrics, OLS, instrumental variables, Heckman selection and difference in difference. In addition, it introduces bounds, factor models, mixture models and empirical Bayesian analysis. Key Features: Focuses on the assumptions underlying the algorithms rather than their statistical properties. Presents cutting-edge analysis of factor models and finite mixture models. Uses a hands-on approach to examine the assumptions made by the models and when the models fail to estimate accurately. Utilizes interesting real-world data sets that can be used to analyze important microeconomic problems. Introduces R programming concepts throughout the book. Includes appendices that discuss some of the standard statistical concepts and R programming used in the book.
Learning Path - C# 7 and .NET: The Open Source Revolution Of . Net Core
by Mark J. Price Ovais Mehboob KhanThis Learning Path is designed for developers who want to gain a solid foundation with C# and .NET Core, and want to build cross-platform applications. To gain maximum benefits from this Learning Path, you must have the basic knowledge of C#.
Learning Path - Modern C++: Efficient and Scalable Application Development
by Richard Grimes Marius BancilaThis Learning Path is designed for developers who want to gain a solid foundation with C++. A computer, an Internet connection, and the desire to learn how to code in C++ are all you need to get started with this Learning Path.
Learning Path - Spring 5: End to End Programming
by Rajesh R V Dinesh Rajput Claudio Eduardo OliveiraIf you're a developer starting out with Spring, then this learning path will help you learn about the new Spring 5.0 framework concepts followed by their implementation in Java and Kotlin. If you are an experienced Spring developer, then this Learning Path will enable you to gain insights into the new features added in Spring 5.0.
Learning Policy: When State Education Reform Works
by David K. Cohen Heather C. HillThis book reports on one of the more significant school-improvement policies in late-twentieth-century America: California's decade-long effort to change and improve mathematics teaching in the state's public schools.
Learning Predictive Analytics with Python
by Ashish KumarGain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python About This Book * A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices * Get to grips with the basics of Predictive Analytics with Python * Learn how to use the popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering Who This Book Is For If you wish to learn how to implement Predictive Analytics algorithms using Python libraries, then this is the book for you. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about Predictive Analytics algorithms, this book will also help you. The book will be beneficial to and can be read by any Data Science enthusiasts. Some familiarity with Python will be useful to get the most out of this book, but it is certainly not a prerequisite. What You Will Learn * Understand the statistical and mathematical concepts behind Predictive Analytics algorithms and implement Predictive Analytics algorithms using Python libraries * Analyze the result parameters arising from the implementation of Predictive Analytics algorithms * Write Python modules/functions from scratch to execute segments or the whole of these algorithms * Recognize and mitigate various contingencies and issues related to the implementation of Predictive Analytics algorithms * Get to know various methods of importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and numpy * Create dummy datasets and simple mathematical simulations using the Python numpy and pandas libraries * Understand the best practices while handling datasets in Python and creating predictive models out of them In Detail Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form - It needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age. This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. You'll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. Style and approach All the concepts in this book been explained and illustrated using a dataset, and in a step-by-step manner. The Python code snippet to implement a method or concept is followed by the output, such as charts, dataset heads, pictures, and so on. The statistical concepts are explained in detail wherever required.
Learning Predictive Analytics with R
by Eric MayorGet to grips with key data visualization and predictive analytic skills using R About This Book * Acquire predictive analytic skills using various tools of R * Make predictions about future events by discovering valuable information from data using R * Comprehensible guidelines that focus on predictive model design with real-world data Who This Book Is For If you are a statistician, chief information officer, data scientist, ML engineer, ML practitioner, quantitative analyst, and student of machine learning, this is the book for you. You should have basic knowledge of the use of R. Readers without previous experience of programming in R will also be able to use the tools in the book. What You Will Learn * Customize R by installing and loading new packages * Explore the structure of data using clustering algorithms * Turn unstructured text into ordered data, and acquire knowledge from the data * Classify your observations using Naïve Bayes, k-NN, and decision trees * Reduce the dimensionality of your data using principal component analysis * Discover association rules using Apriori * Understand how statistical distributions can help retrieve information from data using correlations, linear regression, and multilevel regression * Use PMML to deploy the models generated in R In Detail R is statistical software that is used for data analysis. There are two main types of learning from data: unsupervised learning, where the structure of data is extracted automatically; and supervised learning, where a labeled part of the data is used to learn the relationship or scores in a target attribute. As important information is often hidden in a lot of data, R helps to extract that information with its many standard and cutting-edge statistical functions. This book is packed with easy-to-follow guidelines that explain the workings of the many key data mining tools of R, which are used to discover knowledge from your data. You will learn how to perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. All chapters will guide you in acquiring the skills in a practical way. Most chapters also include a theoretical introduction that will sharpen your understanding of the subject matter and invite you to go further. The book familiarizes you with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, association rules, principal component analysis, multilevel modeling, k-NN, Naïve Bayes, decision trees, and text mining. It also provides a description of visualization techniques using the basic visualization tools of R as well as lattice for visualizing patterns in data organized in groups. This book is invaluable for anyone fascinated by the data mining opportunities offered by GNU R and its packages. Style and approach This is a practical book, which analyzes compelling data about life, health, and death with the help of tutorials. It offers you a useful way of interpreting the data that's specific to this book, but that can also be applied to any other data.
Learning Quantitative Finance with R
by Dr Param Jeet Prashant VatsImplement machine learning, time-series analysis, algorithmic trading and more About This Book • Understand the basics of R and how they can be applied in various Quantitative Finance scenarios • Learn various algorithmic trading techniques and ways to optimize them using the tools available in R. • Contain different methods to manage risk and explore trading using Machine Learning. Who This Book Is For If you want to learn how to use R to build quantitative finance models with ease, this book is for you. Analysts who want to learn R to solve their quantitative finance problems will also find this book useful. Some understanding of the basic financial concepts will be useful, though prior knowledge of R is not required. What You Will Learn • Get to know the basics of R and how to use it in the field of Quantitative Finance • Understand data processing and model building using R • Explore different types of analytical techniques such as statistical analysis, time-series analysis, predictive modeling, and econometric analysis • Build and analyze quantitative finance models using real-world examples • How real-life examples should be used to develop strategies • Performance metrics to look into before deciding upon any model • Deep dive into the vast world of machine-learning based trading • Get to grips with algorithmic trading and different ways of optimizing it • Learn about controlling risk parameters of financial instruments In Detail The role of a quantitative analyst is very challenging, yet lucrative, so there is a lot of competition for the role in top-tier organizations and investment banks. This book is your go-to resource if you want to equip yourself with the skills required to tackle any real-world problem in quantitative finance using the popular R programming language. You'll start by getting an understanding of the basics of R and its relevance in the field of quantitative finance. Once you've built this foundation, we'll dive into the practicalities of building financial models in R. This will help you have a fair understanding of the topics as well as their implementation, as the authors have presented some use cases along with examples that are easy to understand and correlate. We'll also look at risk management and optimization techniques for algorithmic trading. Finally, the book will explain some advanced concepts, such as trading using machine learning, optimizations, exotic options, and hedging. By the end of this book, you will have a firm grasp of the techniques required to implement basic quantitative finance models in R. Style and approach This book introduces you to the essentials of quantitative finance with the help of easy-to-understand, practical examples and use cases in R. Each chapter presents a specific financial concept in detail, backed with relevant theory and the implementation of a real-life example.
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.