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Statistical Modeling and Machine Learning for Molecular Biology

Statistical Modeling and Machine Learning for Molecular Biology

Molecular biologists are performing increasingly large and complicated experiments but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs ortholog identification motif finding inference of population structure protein fold prediction and many more. The book takes a pragmatic approach focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics. | Statistical Modeling and Machine Learning for Molecular Biology

GBP 180.00
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Introductory Mathematical Analysis for Quantitative Finance

Introductory Mathematical Analysis for Quantitative Finance

Introductory Mathematical Analysis for Quantitative Finance is a textbook designed to enable students with little knowledge of mathematical analysis to fully engage with modern quantitative finance. A basic understanding of dimensional Calculus and Linear Algebra is assumed. The exposition of the topics is as concise as possible since the chapters are intended to represent a preliminary contact with the mathematical concepts used in Quantitative Finance. The aim is that this book can be used as a basis for an intensive one-semester course. Features:Written with applications in mind and maintaining mathematical rigor. Suitable for undergraduate or master's level students with an Economics or Management background. Complemented with various solved examples and exercises to support the understanding of the subject.

GBP 39.99
1

Error Correcting Codes A Mathematical Introduction

Error Correcting Codes A Mathematical Introduction

Assuming little previous mathematical knowledge Error Correcting Codes provides a sound introduction to key areas of the subject. Topics have been chosen for their importance and practical significance which Baylis demonstrates in a rigorous but gentle mathematical style. Coverage includes optimal codes; linear and non-linear codes; general techniques of decoding errors and erasures; error detection; syndrome decoding and much more. Error Correcting Codes contains not only straight maths but also exercises on more investigational problem solving. Chapters on number theory and polynomial algebra are included to support linear codes and cyclic codes and an extensive reminder of relevant topics in linear algebra is given. Exercises are placed within the main body of the text to encourage active participation by the reader with comprehensive solutions provided. Error Correcting Codes will appeal to undergraduate students in pure and applied mathematical fields software engineering communications engineering computer science and information technology and to organizations with substantial research and development in those areas. | Error Correcting Codes A Mathematical Introduction

GBP 180.00
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C++ for Financial Mathematics

C++ for Financial Mathematics

If you know a little bit about financial mathematics but don’t yet know a lot about programming then C++ for Financial Mathematics is for you. C++ is an essential skill for many jobs in quantitative finance but learning it can be a daunting prospect. This book gathers together everything you need to know to price derivatives in C++ without unnecessary complexities or technicalities. It leads the reader step-by-step from programming novice to writing a sophisticated and flexible financial mathematics library. At every step each new idea is motivated and illustrated with concrete financial examples. As employers understand there is more to programming than knowing a computer language. As well as covering the core language features of C++ this book teaches the skills needed to write truly high quality software. These include topics such as unit tests debugging design patterns and data structures. The book teaches everything you need to know to solve realistic financial problems in C++. It can be used for self-study or as a textbook for an advanced undergraduate or master’s level course.

GBP 44.99
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The Effect An Introduction to Research Design and Causality

The Effect An Introduction to Research Design and Causality

The Effect: An Introduction to Research Design and Causality is about research design specifically concerning research that uses observational data to make a causal inference. It is separated into two halves each with different approaches to that subject. The first half goes through the concepts of causality with very little in the way of estimation. It introduces the concept of identification thoroughly and clearly and discusses it as a process of trying to isolate variation that has a causal interpretation. Subjects include heavy emphasis on data-generating processes and causal diagrams. Concepts are demonstrated with a heavy emphasis on graphical intuition and the question of what we do to data. When we “add a control variable” what does that actually do? Key Features: • Extensive code examples in R Stata and Python • Chapters on overlooked topics in econometrics classes: heterogeneous treatment effects simulation and power analysis new cutting-edge methods and uncomfortable ignored assumptions • An easy-to-read conversational tone • Up-to-date coverage of methods with fast-moving literatures like difference-in-differences | The Effect An Introduction to Research Design and Causality

GBP 29.99
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Rasch Measurement Theory Analysis in R

Rasch Measurement Theory Analysis in R

Rasch Measurement Theory Analysis in R provides researchers and practitioners with a step-by-step guide for conducting Rasch measurement theory analyses using R. It includes theoretical introductions to major Rasch measurement principles and techniques demonstrations of analyses using several R packages that contain Rasch measurement functions and sample interpretations of results. Features: Accessible to users with relatively little experience with R programming Reproducible data analysis examples that can be modified to accommodate users’ own data Accompanying e-book website with links to additional resources and R code updates as needed Features dichotomous and polytomous (rating scale) Rasch models that can be applied to data from a wide range of disciplines This book is designed for graduate students researchers and practitioners across the social health and behavioral sciences who have a basic familiarity with Rasch measurement theory and with R. Readers will learn how to use existing R packages to conduct a variety of analyses related to Rasch measurement theory including evaluating data for adherence to measurement requirements applying the dichotomous Rating Scale Partial Credit and Many-Facet Rasch models examining data for evidence of differential item functioning and considering potential interpretations of results from such analyses.

GBP 56.99
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The Art of Algorithm Design

The Art of Algorithm Design

The Art of Algorithm Design is a complementary perception of all books on algorithm design and is a roadmap for all levels of learners as well as professionals dealing with algorithmic problems. Further the book provides a comprehensive introduction to algorithms and covers them in considerable depth yet makes their design and analysis accessible to all levels of readers. All algorithms are described and designed with a pseudo-code to be readable by anyone with little knowledge of programming. This book comprises of a comprehensive set of problems and their solutions against each algorithm to demonstrate its executional assessment and complexity with an objective to: Understand the introductory concepts and design principles of algorithms and their complexities Demonstrate the programming implementations of all the algorithms using C-Language Be an excellent handbook on algorithms with self-explanatory chapters enriched with problems and solutions While other books may also cover some of the same topics this book is designed to be both versatile and complete as it traverses through step-by-step concepts and methods for analyzing each algorithmic complexity with pseudo-code examples. Moreover the book provides an enjoyable primer to the field of algorithms. This book is designed for undergraduates and postgraduates studying algorithm design.

GBP 150.00
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Introduction to Financial Mathematics With Computer Applications

Introduction to Financial Mathematics With Computer Applications

This book’s primary objective is to educate aspiring finance professionals about mathematics and computation in the context of financial derivatives. The authors offer a balance of traditional coverage and technology to fill the void between highly mathematical books and broad finance books. The focus of this book is twofold: To partner mathematics with corresponding intuition rather than diving so deeply into the mathematics that the material is inaccessible to many readers. To build reader intuition understanding and confidence through three types of computer applications that help the reader understand the mathematics of the models. Unlike many books on financial derivatives requiring stochastic calculus this book presents the fundamental theories based on only undergraduate probability knowledge. A key feature of this book is its focus on applying models in three programming languages –R Mathematica and EXCEL. Each of the three approaches offers unique advantages. The computer applications are carefully introduced and require little prior programming background. The financial derivative models that are included in this book are virtually identical to those covered in the top financial professional certificate programs in finance. The overlap of financial models between these programs and this book is broad and deep. | Introduction to Financial Mathematics With Computer Applications

GBP 89.99
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Design of Experiments for Generalized Linear Models

Design of Experiments for Generalized Linear Models

Generalized Linear Models (GLMs) allow many statistical analyses to be extended to important statistical distributions other than the Normal distribution. While numerous books exist on how to analyse data using a GLM little information is available on how to collect the data that are to be analysed in this way. This is the first book focusing specifically on the design of experiments for GLMs. Much of the research literature on this topic is at a high mathematical level and without any information on computation. This book explains the motivation behind various techniques reduces the difficulty of the mathematics or moves it to one side if it cannot be avoided and gives examples of how to write and run computer programs using R. FeaturesThe generalisation of the linear model to GLMsBackground mathematics and the use of constrained optimisation in RCoverage of the theory behind the optimality of a designIndividual chapters on designs for data that have Binomial or Poisson distributionsBayesian experimental designAn online resource contains R programs used in the bookThis book is aimed at readers who have done elementary differentiation and understand minimal matrix algebra and have familiarity with R. It equips professional statisticians to read the research literature. Nonstatisticians will be able to design their own experiments by following the examples and using the programs provided. | Design of Experiments for Generalized Linear Models

GBP 38.99
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The R Student Companion

The R Student Companion

R is the amazing free open-access software package for scientific graphs and calculations used by scientists worldwide. The R Student Companion is a student-oriented manual describing how to use R in high school and college science and mathematics courses. Written for beginners in scientific computation the book assumes the reader has just some high school algebra and has no computer programming background. The author presents applications drawn from all sciences and social sciences and includes the most often used features of R in an appendix. In addition each chapter provides a set of computational challenges: exercises in R calculations that are designed to be performed alone or in groups. Several of the chapters explore algebra concepts that are highly useful in scientific applications such as quadratic equations systems of linear equations trigonometric functions and exponential functions. Each chapter provides an instructional review of the algebra concept followed by a hands-on guide to performing calculations and graphing in R. R is intuitive even fun. Fantastic publication-quality graphs of data equations or both can be produced with little effort. By integrating mathematical computation and scientific illustration early in a student‘s development R use can enhance one's understanding of even the most difficult scientific concepts. While R has gained a strong reputation as a package for statistical analysis The R Student Companion approaches R more completely as a comprehensive tool for scientific computing and graphing.

GBP 175.00
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Philosophy of Mathematics Classic and Contemporary Studies

Philosophy of Mathematics Classic and Contemporary Studies

The philosophy of mathematics is an exciting subject. Philosophy of Mathematics: Classic and Contemporary Studies explores the foundations of mathematical thought. The aim of this book is to encourage young mathematicians to think about the philosophical issues behind fundamental concepts and about different views on mathematical objects and mathematical knowledge. With this new approach the author rekindles an interest in philosophical subjects surrounding the foundations of mathematics. He offers the mathematical motivations behind the topics under debate. He introduces various philosophical positions ranging from the classic views to more contemporary ones including subjects which are more engaged with mathematical logic. Most books on philosophy of mathematics have little to no focus on the effects of philosophical views on mathematical practice and no concern on giving crucial mathematical results and their philosophical relevance consequences reasons etc. This book fills this gap. The book can be used as a textbook for a one-semester or even one-year course on philosophy of mathematics. Other textbooks on the philosophy of mathematics are aimed at philosophers. This book is aimed at mathematicians. Since the author is a mathematician it is a valuable addition to the literature. Mark Balaguer California State University Los Angeles There are not many such texts available for mathematics students. I applaud efforts to foster the dialogue between mathematics and philosophy. Michele Friend George Washington University and CNRS Lille France | Philosophy of Mathematics Classic and Contemporary Studies

GBP 48.99
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Understanding Statistics for the Social Sciences with IBM SPSS

Understanding Statistics for the Social Sciences with IBM SPSS

Modern statistical software provides the ability to compute statistics in a timely orderly fashion. This introductory statistics textbook presents clear explanations of basic statistical concepts and introduces students to the IBM SPSS program to demonstrate how to conduct statistical analyses via the popular point-and-click and the syntax file methods. The focal point is to show students how easy it is to analyse data using SPSS once they have learned the basics. Provides clear explanation of basic statistical concepts that provides the foundation for the beginner students’ statistical journey. Introduces the SPSS software program. Gives clear explanation of the purpose of specific statistical procedures (e. g. frequency distributions measures of central tendencies measures of variability etc. ). Avoids the conventional cookbook approach that contributes very little to students’ understanding of the rationale of how the correct results were obtained. The advantage of learning the IBM SPSS software package at the introductory class level is that most social sciences students will employ this program in their later years of study. This is because SPSS is one of the most popular of the many statistical packages currently available. Learning how to use this program at the very start not only familiarizes students with the utility of this program but also provides them with the experience to employ the program to conduct more complex analyses in their later years. | Understanding Statistics for the Social Sciences with IBM SPSS

GBP 44.99
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Discrete Event Simulation for Health Technology Assessment

Discrete Event Simulation for Health Technology Assessment

Discover How to Apply DES to Problems Encountered in HTADiscrete event simulation (DES) has traditionally been used in the engineering and operations research fields. The use of DES to inform decisions about health technologies is still in its infancy. Written by specialists at the forefront of this area Discrete Event Simulation for Health Technology Assessment is the first book to make all the central concepts of DES relevant for health technology assessment (HTA). Accessible to beginners the book requires no prerequisites and describes the concepts with as little jargon as possible. The book first covers the essential concepts and their implementation. It next provides a fully worked out example using both a widely available spreadsheet program (Microsoft Excel) and a popular specialized simulation package (Arena). It then presents approaches to analyze the simulations including the treatment of uncertainty; tackles the development of the required equations; explains the techniques to verify that the models are as efficient as possible; and explores the indispensable topic of validation. The book also covers a variety of non-essential yet handy topics such as the animation of a simulation and extensions of DES and incorporates a real case study involving screening strategies for breast cancer surveillance. This book guides you in leveraging DES in your assessments of health technologies. After reading the chapters in sequence you will be able to construct a realistic model designed to help in the assessment of a new health technology.

GBP 44.99
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Risk Monetization Converting Threats and Opportunities into Impact on Project Value

Risk Monetization Converting Threats and Opportunities into Impact on Project Value

Risk Monetization: Converting Threats and Opportunities into Impact on Project Value addresses the organizational political cultural and technical issues related to implementing a successful risk assessment management and monetization process. Suitable for readers in any organization or area of expertise the book assumes no prior background in risk assessment management or monetization. With more than three decades of experience in risk-process implementation the author first explains the benefits of the risk-monetization process and how risk matters are generally not handled properly in contemporary organizations. He then introduces the terms and definitions essential to making risk monetization successful in a project. The text goes on to give examples of risk-monetization techniques applied in a variety of settings before discussing the typical risk situation for most projects and the shortcomings of conventional processes. It also describes how risk identification assessment management and monetization processes are set up in an ideal environment as well as in imperfect situations. The final chapter focuses on how investment decisions are made based on the monetization and ranking of risks. Enhancing your project’s value this book offers step-by-step practical guidance on identifying assessing managing and monetizing both threats and opportunities so that risk impedes the bottom line as little as possible. It shows you how to convert probable risks into positive impacts on the chance of success and/or profitability of any project. | Risk Monetization Converting Threats and Opportunities into Impact on Project Value

GBP 69.99
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Time Series for Data Science Analysis and Forecasting

Time Series for Data Science Analysis and Forecasting

Data Science students and practitioners want to find a forecast that “works” and don’t want to be constrained to a single forecasting strategy Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. Covering time series regression models exponential smoothing Holt-Winters forecasting and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject. This book is an accessible guide that doesn’t require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed. Features: Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing Holt Winters ARMA and ARIMA deep learning models including RNNs LSTMs GRUs and ensemble models composed of combinations of these models. Introduces the factor table representation of ARMA and ARIMA models. This representation is not available in any other book at this level and is extremely useful in both practice and pedagogy. Uses real world examples that can be readily found via web links from sources such as the US Bureau of Statistics Department of Transportation and the World Bank. There is an accompanying R package that is easy to use and requires little or no previous R experience. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use. | Time Series for Data Science Analysis and Forecasting

GBP 99.99
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Hands-On Machine Learning with R

Hands-On Machine Learning with R

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R which includes using various R packages such as glmnet h2o ranger xgboost keras and others to effectively model and gain insight from their data. The book favors a hands-on approach providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book the reader will be exposed to the entire machine learning process including feature engineering resampling hyperparameter tuning model evaluation and interpretation. The reader will be exposed to powerful algorithms such as regularized regression random forests gradient boosting machines deep learning generalized low rank models and more! By favoring a hands-on approach and using real word data the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages understand when and how to tune the various hyperparameters and be able to interpret model results. By the end of this book the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering resampling deep learning and more. · Uses a hands-on approach and real world data.

GBP 82.99
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Evaluating What Works An Intuitive Guide to Intervention Research for Practitioners

Evaluating What Works An Intuitive Guide to Intervention Research for Practitioners

Those who work in allied health professions and education aim to make people’s lives better. Often however it is hard to know how effective this work has been: would change have occurred if there was no intervention? Is it possible we are doing more harm than good? To answer these questions and develop a body of knowledge about what works we need to evaluate interventions. Objective intervention research is vital to improve outcomes but this is a complex area where it is all too easy to misinterpret evidence. This book uses practical examples to increase awareness of the numerous sources of bias that can lead to mistaken conclusions when evaluating interventions. The focus is on quantitative research methods and exploration of the reasons why those both receiving and implementing intervention behave in the ways they do. Evaluating What Works: Intuitive Guide to Intervention Research for Practitioners illustrates how different research designs can overcome these issues and points the reader to sources with more in-depth information. This book is intended for those with little or no background in statistics to give them the confidence to approach statistics in published literature with a more critical eye recognise when more specialist advice is needed and give them the ability to communicate more effectively with statisticians. Key Features: Strong focus on quantitative research methods Complements more technical introductions to statistics Provides a good explanation of how quantitative studies are designed and what biases and pitfalls they can involve | Evaluating What Works An Intuitive Guide to Intervention Research for Practitioners

GBP 44.99
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A Pen and Paper Introduction to Statistics

A Pen and Paper Introduction to Statistics

Statistics is central in the biosciences social sciences and other disciplines yet many students often struggle to learn how to perform statistical tests and to understand how and why statistical tests work. Although there are many approaches to teaching statistics a common framework exists between them: starting with probability and distributions then sampling from distribution and descriptive statistics and later introducing both simple and complex statistical tests typically ending with regression analysis (linear models). This book proposes to reverse the way statistics is taught by starting with the introduction of linear models. Today many statisticians know that the one unifying principle of statistical tests is that most of them are instances of linear models. This teaching method has two advantages: all statistical tests in a course can be presented under the same unifying framework simplifying things; second linear models can be expressed as lines over squared paper replacing any equation with a drawing. This book explains how and why statistics works without using a single equation just lines and squares over grid paper. The reader will have the opportunity to work through the examples and compute sums of squares by just drawing and counting and finally evaluating whether observed differences are statistically significant by using the tables provided. Intended for students scientists and those with little prior knowledge of statistics this book is for all with simple and clear examples computations and drawings helping the reader to not only do statistical tests but also understand statistics. | A Pen and Paper Introduction to Statistics

GBP 31.99
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Nonparametric Statistical Tests A Computational Approach

Nonparametric Statistical Tests A Computational Approach

Nonparametric Statistical Tests: A Computational Approach describes classical nonparametric tests as well as novel and little-known methods such as the Baumgartner-Weiss-Schindler and the Cucconi tests. The book presents SAS and R programs allowing readers to carry out the different statistical methods such as permutation and bootstrap tests. The author considers example data sets in each chapter to illustrate methods. Numerous real-life data from various areas including the bible and their analyses provide for greatly diversified reading. The book covers: Nonparametric two-sample tests for the location-shift model specifically the Fisher-Pitman permutation test the Wilcoxon rank sum test and the Baumgartner-Weiss-Schindler test Permutation tests location-scale tests tests for the nonparametric Behrens-Fisher problem and tests for a difference in variability Tests for the general alternative including the (Kolmogorov-)Smirnov test ordered categorical and discrete numerical data Well-known one-sample tests such as the sign test and Wilcoxon’s signed rank test a modification suggested by Pratt (1959) a permutation test with original observations and a one-sample bootstrap test are presented. Tests for more than two groups the following tests are described in detail: the Kruskal-Wallis test the permutation F test the Jonckheere-Terpstra trend test tests for umbrella alternatives and the Friedman and Page tests for multiple dependent groups The concepts of independence and correlation and stratified tests such as the van Elteren test and combination tests The applicability of computer-intensive methods such as bootstrap and permutation tests for non-standard situations and complex designs Although the major development of nonparametric methods came to a certain end in the 1970s their importance undoubtedly persists. What is still needed is a computer assisted evaluation of their main properties. This book closes that gap. | Nonparametric Statistical Tests A Computational Approach

GBP 69.99
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Sets Functions and Logic An Introduction to Abstract Mathematics Third Edition

Sets Functions and Logic An Introduction to Abstract Mathematics Third Edition

Keith Devlin. You know him. You've read his columns in MAA Online you've heard him on the radio and you've seen his popular mathematics books. In between all those activities and his own research he's been hard at work revising Sets Functions and Logic his standard-setting text that has smoothed the road to pure mathematics for legions of undergraduate students. Now in its third edition Devlin has fully reworked the book to reflect a new generation. The narrative is more lively and less textbook-like. Remarks and asides link the topics presented to the real world of students' experience. The chapter on complex numbers and the discussion of formal symbolic logic are gone in favor of more exercises and a new introductory chapter on the nature of mathematics-one that motivates readers and sets the stage for the challenges that lie ahead. Students crossing the bridge from calculus to higher mathematics need and deserve all the help they can get. Sets Functions and Logic Third Edition is an affordable little book that all of your transition-course students not only can afford but will actually read and enjoy and learn from. About the AuthorDr. Keith Devlin is Executive Director of Stanford University's Center for the Study of Language and Information and a Consulting Professor of Mathematics at Stanford. He has written 23 books one interactive book on CD-ROM and over 70 published research articles. He is a Fellow of the American Association for the Advancement of Science a World Economic Forum Fellow and a former member of the Mathematical Sciences Education Board of the National Academy of Sciences . Dr. Devlin is also one of the world's leading popularizers of mathematics. Known as The Math Guy on NPR's Weekend Edition he is a frequent contributor to other local and national radio and TV shows in the US and Britain writes a monthly column for the Web journal MAA Online and regularly writes on mathematics and co | Sets Functions and Logic An Introduction to Abstract Mathematics Third Edition

GBP 175.00
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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
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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
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Exploratory Data Analysis Using R

Exploratory Data Analysis Using R

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of interesting – good bad and ugly – features that can be found in data and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data exploratory analysis and R as well as graphics in R. It then explores working with external data linear regression models and crafting data stories. The second part of the book focuses on developing R programs including good programming practices and examples working with text data and general predictive models. The book ends with a chapter on keeping it all together that includes managing the R installation managing files documenting and an introduction to reproducible computing. The book is designed for both advanced undergraduate entry-level graduate students and working professionals with little to no prior exposure to data analysis modeling statistics or programming. it keeps the treatment relatively non-mathematical even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters and an instructor's solution manual is available. About the Author:Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera a property insurance company in Fairfield California and he has previously held similar positions in a variety of application areas including software development drug safety data analysis and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering the Sciences and Medicine (Oxford University Press 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).

GBP 44.99
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Geographic Data Science with R Visualizing and Analyzing Environmental Change

Geographic Data Science with R Visualizing and Analyzing Environmental Change

The burgeoning field of data science has provided a wealth of techniques for analysing large and complex geospatial datasets including descriptive explanatory and predictive analytics. However applying these methods is just one part of the overall process of geographic data science. Other critical steps include screening for suspect data values handling missing data harmonizing data from multiple sources summarizing the data and visualizing data and analysis results. Although there are many books available on statistical and machine learning methods few encompass the broader topic of scientific workflows for geospatial data processing and analysis. The purpose of Geographic Data Science with R is to fill this gap by providing a series of tutorials aimed at teaching good practices for using geospatial data to address problems in environmental geography. It is based on the R language and environment which currently provides the best option for working with diverse spatial and non-spatial data in a single platform. Fundamental techniques for processing and visualizing tabular vector and raster data are introduced through a series of practical examples followed by case studies that combine multiple types of data to address more complex problems. The book will have a broad audience. Both students and professionals can use it as a workbook to learn high-level techniques for geospatial data processing and analysis with R. It is also suitable as a textbook. Although not intended to provide a comprehensive introduction to R it is designed to be accessible to readers who have at least some knowledge of coding but little to no experience with R. Key Features: Focus on developing practical workflows for processing and integrating multiple sources of geospatial data in R Example-based approach that teaches R programming and data science concepts through real-world applications related to climate land cover and land use and natural hazards. Consistent use of tidyverse packages for tabular data manipulation and visualization. Strong focus on analysing continuous and categorical raster datasets using the new terra package Organized so that each chapter builds on the topics and techniques covered in the preceding chapters Can be used for self-study or as the textbook for a geospatial science course. | Geographic Data Science with R Visualizing and Analyzing Environmental Change

GBP 74.99
1

Artificial Intelligence and the Two Singularities

Artificial Intelligence and the Two Singularities

The science of AI was born a little over 60 years ago but for most of that time its achievements were modest. In 2012 it experienced a big bang when a branch of statistics called Machine Learning (and a sub-branch called Deep Learning) was applied to it. Now machines have surpassed humans in image recognition and they are catching up with us at speech recognition and natural language processing. Every day the media reports the launch of a new service a new product and a new demonstration powered by AI. When will it end? The surprising truth is the AI revolution has only just begun. Artificial Intelligence and the Two Singularities argues that in the course of this century the exponential growth in the capability of AI is likely to bring about two singularities - points at which conditions are so extreme that the normal rules break down. The first is the economic singularity when machine skill reaches a level that renders many of us unemployable and requires an overhaul of our current economic and social systems. The second is the technological singularity when machine intelligence reaches and then surpasses the cognitive abilities of an adult human relegating us to the second smartest species on the planet. These singularities will present huge challenges but this book argues that we can meet these challenges and overcome them. If we do the rewards could be almost unimaginable. This book covers: • Recent developments in AI and its future potential • The economic singularity and the technological singularity in depth • The risks and opportunities presented by AI • What actions we should take Artificial intelligence can turn out to be the best thing ever to happen to humanity making our future wonderful almost beyond imagination. But only if we address head-on the challenges that it will raise. Calum Chace is a best-selling author of fiction and non-fiction books and articles focusing on the subject of artificial intelligence. He is a regular speaker on artificial intelligence and related technologies and runs a blog on the subject at www. pandoras-brain. com. Prior to becoming a full-time writer and speaker he spent 30 years in business as a marketer a strategy consultant and a CEO. He studied philosophy at Oxford University where he discovered that the science fiction he had been reading since boyhood was simply philosophy in fancy dress.

GBP 46.99
1