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Geocomputation with R

Geocomputation with R

Geocomputation with R is for people who want to analyze visualize and model geographic data with open source software. It is based on R a statistical programming language that has powerful data processing visualization and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data including those with scientific societal and environmental implications. This book will interest people from many backgrounds especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations aimed at getting you up-to-speed with geographic data in R (II) extensions which covers advanced techniques and (III) applications to real-world problems. The chapters cover progressively more advanced topics with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping) bridges to GIS sharing reproducible code and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems including representing and modeling transport systems finding optimal locations for stores or services and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr. github. io/geocompkg/articles/. Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds where he has taught R for geographic research over many years with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena where he develops and teaches a range of geographic methods with a focus on ecological modeling statistical geocomputing and predictive mapping. All three are active developers and work on a number of R packages including stplanr sabre and RQGIS.

GBP 44.99
1

Linear Models with Python

Linear Models with Python

Praise for Linear Models with R: This book is a must-have tool for anyone interested in understanding and applying linear models. The logical ordering of the chapters is well thought out and portrays Faraway’s wealth of experience in teaching and using linear models. … It lays down the material in a logical and intricate manner and makes linear modeling appealing to researchers from virtually all fields of study. Biometrical Journal Throughout it gives plenty of insight … with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice well epitomized with the examples chosen…I read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear models. Journal of the Royal Statistical Society Like its widely praised best-selling companion version Linear Models with R this book replaces R with Python to seamlessly give a coherent exposition of the practice of linear modeling. Linear Models with Python offers up-to-date insight on essential data analysis topics from estimation inference and prediction to missing data factorial models and block designs. Numerous examples illustrate how to apply the different methods using Python. Features: Python is a powerful open source programming language increasingly being used in data science machine learning and computer science. Python and R are similar but R was designed for statistics while Python is multi-talented. This version replaces R with Python to make it accessible to a greater number of users outside of statistics including those from Machine Learning. A reader coming to this book from an ML background will learn new statistical perspectives on learning from data. Topics include Model Selection Shrinkage Experiments with Blocks and Missing Data. Includes an Appendix on Python for beginners. Linear Models with Python explains how to use linear models in physical science engineering social science and business applications. It is ideal as a textbook for linear models or linear regression courses.

GBP 82.99
1

Data Mining with R Learning with Case Studies Second Edition

Data Mining with R Learning with Case Studies Second Edition

Data Mining with R: Learning with Case Studies Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition this new edition is divided into two parts. The first part will feature introductory material including a new chapter that provides an introduction to data mining to complement the already existing introduction to R. The second part includes case studies and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies and they facilitate the do-it-yourself approach followed in the book. Designed for users of data analysis tools as well as researchers and developers the book should be useful for anyone interested in entering the world of R and data mining. About the AuthorLuís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA. | Data Mining with R Learning with Case Studies Second Edition

GBP 44.99
1

Telling Stories with Data With Applications in R

Telling Stories with Data With Applications in R

The book equips students with the end-to-end skills needed to do data science. That means gathering cleaning preparing and sharing data then using statistical models to analyse data writing about the results of those models drawing conclusions from them and finally using the cloud to put a model into production all done in a reproducible way. At the moment there are a lot of books that teach data science but most of them assume that you already have the data. This book fills that gap by detailing how to go about gathering datasets cleaning and preparing them before analysing them. There are also a lot of books that teach statistical modelling but few of them teach how to communicate the results of the models and how they help us learn about the world. Very few data science textbooks cover ethics and most of those that do have a token ethics chapter. Finally reproducibility is not often emphasised in data science books. This book is based around a straight-forward workflow conducted in an ethical and reproducible way: gather data prepare data analyse data and communicate those findings. This book will achieve the goals by working through extensive case studies in terms of gathering and preparing data and integrating ethics throughout. It is specifically designed around teaching how to write about the data and models so aspects such as writing are explicitly covered. And finally the use of GitHub and the open-source statistical language R are built in throughout the book. Key Features: Extensive code examples. Ethics integrated throughout. Reproducibility integrated throughout. Focus on data gathering messy data and cleaning data. Extensive formative assessment throughout. | Telling Stories with Data With Applications in R

GBP 74.99
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Computational Genomics with R

Computational Genomics with R

Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming to machine learning and statistics to the latest genomic data analysis techniques. The text provides accessible information and explanations always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary it requires different starting points for people with different backgrounds. For example a biologist might skip sections on basic genome biology and start with R programming whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics supervised and unsupervised learning techniques that are important in data modeling and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics such as heatmaps meta-gene plots and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets such as RNA-seq ChIP-seq and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology Max Delbrück Center Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

GBP 42.99
1

Green's Functions with Applications

Green's Functions with Applications

Since publication of the first edition over a decade ago Green’s Functions with Applications has provided applied scientists and engineers with a systematic approach to the various methods available for deriving a Green’s function. This fully revised Second Edition retains the same purpose but has been meticulously updated to reflect the current state of the art. The book opens with necessary background information: a new chapter on the historical development of the Green’s function coverage of the Fourier and Laplace transforms a discussion of the classical special functions of Bessel functions and Legendre polynomials and a review of the Dirac delta function. The text then presents Green’s functions for each class of differential equation (ordinary differential wave heat and Helmholtz equations) according to the number of spatial dimensions and the geometry of the domain. Detailing step-by-step methods for finding and computing Green’s functions each chapter contains a special section devoted to topics where Green’s functions particularly are useful. For example in the case of the wave equation Green’s functions are beneficial in describing diffraction and waves. To aid readers in developing practical skills for finding Green’s functions worked examples problem sets and illustrations from acoustics applied mechanics antennas and the stability of fluids and plasmas are featured throughout the text. A new chapter on numerical methods closes the book. Included solutions and hundreds of references to the literature on the construction and use of Green's functions make Green’s Functions with Applications Second Edition a valuable sourcebook for practitioners as well as graduate students in the sciences and engineering. | Green's Functions with Applications

GBP 62.99
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Population Genomics with R

Population Genomics with R

Population Genomics With R presents a multidisciplinary approach to the analysis of population genomics. The methods treated cover a large number of topics from traditional population genetics to large-scale genomics with high-throughput sequencing data. Several dozen R packages are examined and integrated to provide a coherent software environment with a wide range of computational statistical and graphical tools. Small examples are used to illustrate the basics and published data are used as case studies. Readers are expected to have a basic knowledge of biology genetics and statistical inference methods. Graduate students and post-doctorate researchers will find resources to analyze their population genetic and genomic data as well as help them design new studies. The first four chapters review the basics of population genomics data acquisition and the use of R to store and manipulate genomic data. Chapter 5 treats the exploration of genomic data an important issue when analysing large data sets. The other five chapters cover linkage disequilibrium population genomic structure geographical structure past demographic events and natural selection. These chapters include supervised and unsupervised methods admixture analysis an in-depth treatment of multivariate methods and advice on how to handle GIS data. The analysis of natural selection a traditional issue in evolutionary biology has known a revival with modern population genomic data. All chapters include exercises. Supplemental materials are available on-line (http://ape-package. ird. fr/PGR. html).

GBP 48.99
1

Stochastic Processes with R An Introduction

Stochastic Processes with R An Introduction

Stochastic Processes with R: An Introduction cuts through the heavy theory that is present in most courses on random processes and serves as practical guide to simulated trajectories and real-life applications for stochastic processes. The light yet detailed text provides a solid foundation that is an ideal companion for undergraduate statistics students looking to familiarize themselves with stochastic processes before going on to more advanced courses. Key Features Provides complete R codes for all simulations and calculations Substantial scientific or popular applications of each process with occasional statistical analysis Helpful definitions and examples are provided for each process End of chapter exercises cover theoretical applications and practice calculations | Stochastic Processes with R An Introduction

GBP 82.99
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Computational Methods for Numerical Analysis with R

Computational Methods for Numerical Analysis with R

Computational Methods for Numerical Analysis with R is an overview of traditional numerical analysis topics presented using R. This guide shows how common functions from linear algebra interpolation numerical integration optimization and differential equations can be implemented in pure R code. Every algorithm described is given with a complete function implementation in R along with examples to demonstrate the function and its use. Computational Methods for Numerical Analysis with R is intended for those who already know R but are interested in learning more about how the underlying algorithms work. As such it is suitable for statisticians economists and engineers and others with a computational and numerical background.

GBP 48.99
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Survival Analysis with Interval-Censored Data A Practical Approach with Examples in R SAS and BUGS

Survival Analysis with Interval-Censored Data A Practical Approach with Examples in R SAS and BUGS

Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R SAS and BUGS provides the reader with a practical introduction into the analysis of interval-censored survival times. Although many theoretical developments have appeared in the last fifty years interval censoring is often ignored in practice. Many are unaware of the impact of inappropriately dealing with interval censoring. In addition the necessary software is at times difficult to trace. This book fills in the gap between theory and practice. Features:-Provides an overview of frequentist as well as Bayesian methods. Include a focus on practical aspects and applications. Extensively illustrates the methods with examples using R SAS and BUGS. Full programs are available on a supplementary website. The authors:Kris Bogaerts is project manager at I-BioStat KU Leuven. He received his PhD in science (statistics) at KU Leuven on the analysis of interval-censored data. He has gained expertise in a great variety of statistical topics with a focus on the design and analysis of clinical trials. Arnošt Komárek is associate professor of statistics at Charles University Prague. His subject area of expertise covers mainly survival analysis with the emphasis on interval-censored data and classification based on longitudinal data. He is past chair of the Statistical Modelling Society and editor of Statistical Modelling: An International Journal. Emmanuel Lesaffre is professor of biostatistics at I-BioStat KU Leuven. His research interests include Bayesian methods longitudinal data analysis statistical modelling analysis of dental data interval-censored data misclassification issues and clinical trials. He is the founding chair of the Statistical Modelling Society past-president of the International Society for Clinical Biostatistics and fellow of ISI and ASA. | Survival Analysis with Interval-Censored Data A Practical Approach with Examples in R SAS and BUGS

GBP 44.99
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Fortran 2018 with Parallel Programming

Fortran 2018 with Parallel Programming

The programming language Fortran dates back to 1957 when a team of IBM engineers released the first Fortran Compiler. During the past 60 years the language had been revised and updated several times to incorporate more features to enable writing clean and structured computer programs. The present version is Fortran 2018. Since the dawn of the computer era there had been a constant demand for a “larger” and “faster” machine. To increase the speed there are three hurdles. The density of the active components on a VLSI chip cannot be increased indefinitely and with the increase of the density heat dissipation becomes a major problem. Finally the speed of any signal cannot exceed the velocity of the light. However by using several inexpensive processors in parallel coupled with specialized software and hardware programmers can achieve computing speed similar to a supercomputer. This book can be used to learn the modern Fortran from the beginning and the technique of developing parallel programs using Fortran. It is for anyone who wants to learn Fortran. Knowledge beyond high school mathematics is not required. There is not another book on the market yet which deals with Fortran 2018 as well as parallel programming. FEATURES Descriptions of majority of Fortran 2018 instructions Numerical Model String with Variable Length IEEE Arithmetic and Exceptions Dynamic Memory Management Pointers Bit handling C-Fortran Interoperability Object Oriented Programming Parallel Programming using Coarray Parallel Programming using OpenMP Parallel Programming using Message Passing Interface (MPI) THE AUTHOR Dr Subrata Ray is a retired Professor Indian Association for the Cultivation of Science Kolkata. | Fortran 2018 with Parallel Programming

GBP 140.00
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Business Financial Planning with Microsoft Excel

Business Financial Planning with Microsoft Excel

Business Finance Planning with Microsoft® Excel® shows how to visualize plan and put into motion an idea for creating a start-up company. Microsoft Excel is a tool that makes it easier to build a business financial planning process for a new business venture. With an easy-to follow structure the book flows as a six-step process: Presenting a case study of a business start-up Creating goals and objectives Determining expenses from those goals and objectives Estimating potential sales revenue based on what competitors charge their customers Predicting marketing costs Finalizing the financial analysis with a of financial statements. Written around an IT startup case study the book presents a host of Excel worksheets describing the case study along with accompanying blank forms. Readers can use these forms in their own businesses so they can build parts of their own business plans as they go. This is intended to be a practical guide that teaches and demonstrates by example in the end presenting a usable financial model to build and tweak a financial plan with a set of customizable Excel worksheets. The book uses practical techniques to help with the planning processing. These include applying a SWOT (strengths weaknesses opportunities and threats) matrix to evaluate a business idea and SMART (Specific Measurable Achievable Relevant and Time-Bound) objectives to link together goals. As the book concludes readers will be able to develop their own income statement balance sheet and the cash-flow statement for a full analysis of their new business ideas. Worksheets are available to download from: https://oracletroubleshooter. com/business-finance-planning/app/ | Business Financial Planning with Microsoft Excel

GBP 34.99
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Real Analysis With Proof Strategies

Real Analysis With Proof Strategies

Typically undergraduates see real analysis as one of the most difficult courses that a mathematics major is required to take. The main reason for this perception is twofold: Students must comprehend new abstract concepts and learn to deal with these concepts on a level of rigor and proof not previously encountered. A key challenge for an instructor of real analysis is to find a way to bridge the gap between a student’s preparation and the mathematical skills that are required to be successful in such a course. Real Analysis: With Proof Strategies provides a resolution to the bridging-the-gap problem. The book not only presents the fundamental theorems of real analysis but also shows the reader how to compose and produce the proofs of these theorems. The detail rigor and proof strategies offered in this textbook will be appreciated by all readers. Features Explicitly shows the reader how to produce and compose the proofs of the basic theorems in real analysis Suitable for junior or senior undergraduates majoring in mathematics. | Real Analysis With Proof Strategies

GBP 42.99
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Handbook of Statistical Distributions with Applications

Handbook of Statistical Distributions with Applications

Easy-to-Use Reference and Software for Statistical Modeling and TestingHandbook of Statistical Distributions with Applications Second Edition provides quick access to common and specialized probability distributions for modeling practical problems and performing statistical calculations. Along with many new examples and results this edition includes both the author’s StatCalc software and R codes to accurately and easily carry out computations. New to the Second Edition Major changes in binomial Poisson normal gamma Weibull exponential logistic Laplace and Pareto distributions Updated statistical tests and intervals based on recent publications in statistical journals Enhanced PC calculator StatCalc with electronic help manualsR functions for cases where StatCalc is not applicable with the codes available onlineThis highly praised handbook integrates popular probability distribution models formulas applications and software to help you compute a variety of statistical intervals. It covers probability and percentiles algorithms for random number generation hypothesis tests confidence intervals tolerance intervals prediction intervals sample size determination and much more.

GBP 44.99
1

Introduction to Computational Models with Python

Introduction to Computational Models with Python

Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of numerical computing such as the Numpy and Scipy modules. The Python source code and data files are available on the author’s website. The book’s five sections present: An overview of problem solving and simple Python programs introducing the basic models and techniques for designing and implementing problem solutions independent of software and hardware toolsProgramming principles with the Python programming language covering basic programming concepts data definitions programming structures with flowcharts and pseudo-code solving problems and algorithmsPython lists arrays basic data structures object orientation linked lists recursion and running programs under LinuxImplementation of computational models with Python using Numpy with examples and case studies The modeling of linear optimization problems from problem formulation to implementation of computational modelsThis book introduces the principles of computational modeling as well as the approaches of multi- and interdisciplinary computing to beginners in the field. It provides the foundation for more advanced studies in scientific computing including parallel computing using MPI grid computing and other methods and techniques used in high-performance computing.

GBP 44.99
1

Tidy Finance with R

Tidy Finance with R

This textbook shows how to bring theoretical concepts from finance and econometrics to the data. Focusing on coding and data analysis with R we show how to conduct research in empirical finance from scratch. We start by introducing the concepts of tidy data and coding principles using the tidyverse family of R packages. Code is provided to prepare common open-source and proprietary financial data sources (CRSP Compustat Mergent FISD TRACE) and organize them in a database. We reuse these data in all the subsequent chapters which we keep as self-contained as possible. The empirical applications range from key concepts of empirical asset pricing (beta estimation portfolio sorts performance analysis Fama-French factors) to modeling and machine learning applications (fixed effects estimation clustering standard errors difference-in-difference estimators ridge regression Lasso Elastic net random forests neural networks) and portfolio optimization techniques. Highlights 1. Self-contained chapters on the most important applications and methodologies in finance which can easily be used for the reader’s research or as a reference for courses on empirical finance. 2. Each chapter is reproducible in the sense that the reader can replicate every single figure table or number by simply copying and pasting the code we provide. 3. A full-fledged introduction to machine learning with tidymodels based on tidy principles to show how factor selection and option pricing can benefit from Machine Learning methods. 4. Chapter 2 on accessing and managing financial data shows how to retrieve and prepare the most important datasets financial economics: CRSP and Compustat. The chapter also contains detailed explanations of the most relevant data characteristics. 5. Each chapter provides exercises based on established lectures and classes which are designed to help students to dig deeper. The exercises can be used for self-studying or as a source of inspiration for teaching exercises. | Tidy Finance with R

GBP 59.99
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A Concise Introduction to Robot Programming with ROS2

A Concise Introduction to Robot Programming with ROS2

A Concise Introduction to Robot Programming with ROS2 provides the reader with the concepts and tools necessary to bring a robot to life through programming. It will equip the reader with the skills necessary to undertake projects with ROS2 the new version of ROS. It is not necessary to have previous experience with ROS2 as it will describe its concepts tools and methodologies from the beginning. Key Features Uses the two programming languages officially supported in ROS2 (C++ mainly and Python) Approaches ROS2 from three different but complementary dimensions: the Community Computation Graph and the Workspace Includes a complete simulated robot development and testing strategies Behavior Trees and Nav2 description setup and use A GitHub repository with code to assist readers It will appeal to motivated engineering students engineers and professionals working with robot programming.

GBP 44.99
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Sequence Space Theory with Applications

Sequence Space Theory with Applications

The book features original chapters on sequence spaces involving the idea of ideal convergence modulus function multiplier sequences Riesz mean Fibonacci difference matrix etc. and illustrate their involvement in various applications. The preliminaries have been presented in the beginning of each chapter and then the advanced discussion takes place so it is useful for both expert and nonexpert on aforesaid topics. The book consists of original thirteen research chapters contributed by the well-recognized researchers in the field of sequence spaces with associated applications. Features Discusses the Fibonacci and vector valued difference sequence spaces Presents the solution of Volterra integral equation in Banach algebra Discusses some sequence spaces involving invariant mean and related to the domain of Jordan totient matrix Presents the Tauberian theorems of double sequences Discusses the paranormed Riesz difference sequence space of fractional order Includes a technique for studying the existence of solutions of infinite system of functional integro-differential equations in Banach sequence spaces The subject of book is an active area of research of present time internationally and would serve as a good source for researcher and educators involved with the topic of sequence spaces. | Sequence Space Theory with Applications

GBP 150.00
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Modern Data Science with R

Modern Data Science with R

From a review of the first edition: Modern Data Science with R… is rich with examples and is guided by a strong narrative voice. What’s more it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf purrr tidymodels and tidytext is now integrated into the text. All chapters have been revised and several have been split re-organized or re-imagined to meet the shifting landscape of best practice.

GBP 82.99
1

Introduction to NFL Analytics with R

Introduction to NFL Analytics with R

It has become difficult to ignore the analytics movement within the NFL. An increasing number of coaches openly integrate advanced numbers into their game plans and commentators throughout broadcasts regularly use terms such as air yards CPOE and EPA on a casual basis. This rapid growth combined with an increasing accessibility to NFL data has helped create a burgeoning amateur analytics movement highlighted by the NFL’s annual Big Data Bowl. Because learning a coding language can be a difficult enough endeavor Introduction to NFL Analytics with R is purposefully written in a more informal format than readers of similar books may be accustomed to opting to provide step-by-step instructions in a structured jargon-free manner. Key Coverage: • Installing R RStudio and necessary packages • Working and becoming fluent in the tidyverse • Finding meaning in NFL data with examples from all the functions in the nflverse family of packages • Using NFL data to create eye-catching data visualizations • Building statistical models starting with simple regressions and progressing to advanced machine learning models using tidymodels and eXtreme Gradient Boosting The book is written for novices of R programming all the way to more experienced coders as well as audiences with differing expected outcomes. Professors can use Introduction to NFL Analytics with R to provide data science lessons through the lens of the NFL while students can use it as an educational tool to create robust visualizations and machine learning models for assignments. Journalists bloggers and arm-chair quarterbacks alike will find the book helpful to underpin their arguments by providing hard data and visualizations to back up their claims.

GBP 52.99
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Discrete Mathematics with Ducks

Discrete Mathematics with Ducks

Discrete Mathematics with Ducks Second Edition is a gentle introduction for students who find the proofs and abstractions of mathematics challenging. At the same time it provides stimulating material that instructors can use for more advanced students. The first edition was widely well received with its whimsical writing style and numerous exercises and materials that engaged students at all levels. The new expanded edition continues to facilitate effective and active learning. It is designed to help students learn about discrete mathematics through problem-based activities. These are created to inspire students to understand mathematics by actively practicing and doing which helps students better retain what they’ve learned. As such each chapter contains a mixture of discovery-based activities projects expository text in-class exercises and homework problems. The author’s lively and friendly writing style is appealing to both instructors and students alike and encourages readers to learn. The book’s light-hearted approach to the subject is a guiding principle and helps students learn mathematical abstraction. Features:The book’s Try This! sections encourage students to construct components of discussed concepts theorems and proofsProvided sets of discovery problems and illustrative examples reinforce learningBonus sections can be used by instructors as part of their regular curriculum for projects or for further study

GBP 38.99
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Data Science with Julia

Data Science with Julia

This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist. Professor Charles Bouveyron INRIA Chair in Data Science Université Côte d’Azur Nice France Julia an open-source programming language was created to be as easy to use as languages such as R and Python while also as fast as C and Fortran. An accessible intuitive and highly efficient base language with speed that exceeds R and Python makes Julia a formidable language for data science. Using well known data science methods that will motivate the reader Data Science with Julia will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work. Features: Covers the core components of Julia as well as packages relevant to the input manipulation and representation of data. Discusses several important topics in data science including supervised and unsupervised learning. Reviews data visualization using the Gadfly package which was designed to emulate the very popular ggplot2 package in R. Readers will learn how to make many common plots and how to visualize model results. Presents how to optimize Julia code for performance. Will be an ideal source for people who already know R and want to learn how to use Julia (though no previous knowledge of R or any other programming language is required). The advantages of Julia for data science cannot be understated. Besides speed and ease of use there are already over 1 900 packages available and Julia can interface (either directly or through packages) with libraries written in R Python Matlab C C++ or Fortran. The book is for senior undergraduates beginning graduate students or practicing data scientists who want to learn how to use Julia for data science. This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist. Professor Charles BouveyronINRIA Chair in Data ScienceUniversité Côte d’Azur Nice France

GBP 51.99
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Computational Statistics Handbook with MATLAB

Computational Statistics Handbook with MATLAB

A Strong Practical Focus on Applications and AlgorithmsComputational Statistics Handbook with MATLAB® Third Edition covers today’s most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. The text keeps theoretical concepts to a minimum emphasizing the implementation of the methods. New to the Third EditionThis third edition is updated with the latest version of MATLAB and the corresponding version of the Statistics and Machine Learning Toolbox. It also incorporates new sections on the nearest neighbor classifier support vector machines model checking and regularization partial least squares regression and multivariate adaptive regression splines. Web ResourceThe authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of algorithms in data analysis. The MATLAB code examples and data sets are available online.

GBP 44.99
1

Applied Meta-Analysis with R and Stata

Applied Meta-Analysis with R and Stata

Review of the First Edition: The authors strive to reduce theory to a minimum which makes it a self-learning text that is comprehensible for biologists physicians etc. who lack an advanced mathematics background. Unlike in many other textbooks R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses graphics and simulations directly relating to meta-analysis… A useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs. —Journal of Applied Statistics Statistical Meta-Analysis with R and Stata Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step by step using appropriate R/Stata functions which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions. What’s New in the Second Edition: Adds Stata programs along with the R programs for meta-analysis Updates all the statistical meta-analyses with R/Stata programs Covers fixed-effects and random-effects MA meta-regression MA with rare-event and MA-IPD vs MA-SS Adds five new chapters on multivariate MA publication bias missing data in MA MA in evaluating diagnostic accuracy and network MA Suitable as a graduate-level text for a meta-data analysis course the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health medical research governmental agencies and the pharmaceutical industry. | Applied Meta-Analysis with R and Stata

GBP 44.99
1

Advanced Number Theory with Applications

Advanced Number Theory with Applications

Exploring one of the most dynamic areas of mathematics Advanced Number Theory with Applications covers a wide range of algebraic analytic combinatorial cryptographic and geometric aspects of number theory. Written by a recognized leader in algebra and number theory the book includes a page reference for every citing in the bibliography and more than 1 500 entries in the index so that students can easily cross-reference and find the appropriate data. With numerous examples throughout the text begins with coverage of algebraic number theory binary quadratic forms Diophantine approximation arithmetic functions p-adic analysis Dirichlet characters density and primes in arithmetic progression. It then applies these tools to Diophantine equations before developing elliptic curves and modular forms. The text also presents an overview of Fermat’s Last Theorem (FLT) and numerous consequences of the ABC conjecture including Thue–Siegel–Roth theorem Hall’s conjecture the Erdös–Mollin-–Walsh conjecture and the Granville–Langevin Conjecture. In the appendix the author reviews sieve methods such as Eratothesenes’ Selberg’s Linnik’s and Bombieri’s sieves. He also discusses recent results on gaps between primes and the use of sieves in factoring. By focusing on salient techniques in number theory this textbook provides the most up-to-date and comprehensive material for a second course in this field. It prepares students for future study at the graduate level.

GBP 69.99
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