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Reproducible Finance with R Code Flows and Shiny Apps for Portfolio Analysis

Reproducible Finance with R Code Flows and Shiny Apps for Portfolio Analysis

Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment management that explores the three major R/finance coding paradigms emphasizes data visualization and explains how to build a cohesive suite of functioning Shiny applications. The full source code asset price data and live Shiny applications are available at reproduciblefinance. com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple yet practical real-world examples. The book begins with the first step in data science: importing and wrangling data which in the investment context means importing asset prices converting to returns and constructing a portfolio. The next section covers risk and tackles descriptive statistics such as standard deviation skewness kurtosis and their rolling histories. The third section focuses on portfolio theory analyzing the Sharpe Ratio CAPM and Fama French models. The book concludes with applications for finding individual asset contribution to risk and for running Monte Carlo simulations. For each of these tasks the three major coding paradigms are explored and the work is wrapped into interactive Shiny dashboards. | Reproducible Finance with R Code Flows and Shiny Apps for Portfolio Analysis

GBP 59.99
1

Anyone Can Code The Art and Science of Logical Creativity

Anyone Can Code The Art and Science of Logical Creativity

Anyone Can Code: The Art and Science of Logical Creativity introduces computer programming as a way of problem-solving through logical thinking. It uses the notion of modularization as a central lens through which we can make sense of many software concepts. This book takes the reader through fundamental concepts in programming by illustrating them in three different and distinct languages: C/C++ Python and Javascript. Key features: Focuses on problem-solving and algorithmic thinking instead of programming functions syntax and libraries; Includes engaging examples including video games and visual effects; Provides exercises and reflective questions. This book gives beginner and intermediate learners a strong understanding of what they are doing so that they can do it better and with any other tool or language that they may end up using later. | Anyone Can Code The Art and Science of Logical Creativity

GBP 56.99
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C Programming Learn to Code

C Programming Learn to Code

The C programming language is a popular language in industries as well as academics. Since its invention and standardized as ANSI C several other standards known as C99 C11 and C17 were published with new features in subsequent years. This book covers all the traits of ANSI C and includes new features present in other standards. The content of this book helps a beginner to learn the fundamental concept of the C language. The book contains a step-by-step explanation of every program that allows a learner to understand the syntax and builds a foundation to write similar programs. The explanation clarity exercises and illustrations present in this book make it a complete textbook in all aspects. Features: Other than ANSI C the book explains the new C standards like C99 C11 and C17. Most basic and easy-to-follow programs are chosen to explain the concepts and their syntax. More emphasis is given to the topics like Functions Pointers and Structures. Recursion is emphasized with numerous programming examples and diagrams. A separate chapter on the command-line argument and preprocessors is included that concisely explains their usage. Several real-life figures are taken to explain the concepts of dynamic memory allocation file handling and the difference between structure and union. The book contains more than 260 illustrations more than 200 programs and exercises at the end of each chapter. This book serves as a textbook for UG/PG courses in science and engineering. The researcher postgraduate engineers and embedded software developers can also keep this book as reference material for their fundamental learning. | C Programming Learn to Code

GBP 105.00
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Sample Size Calculations in Clinical Research

Sample Size Calculations in Clinical Research

Praise for the Second Edition:… this is a useful comprehensive compendium of almost every possible sample size formula. The strong organization and carefully defined formulae will aid any researcher designing a study. BiometricsThis impressive book contains formulae for computing sample size in a wide range of settings. One-sample studies and two-sample comparisons for quantitative binary and time-to-event outcomes are covered comprehensively with separate sample size formulae for testing equality non-inferiority and equivalence. Many less familiar topics are also covered … – Journal of the Royal Statistical SocietySample Size Calculations in Clinical Research Third Edition presents statistical procedures for performing sample size calculations during various phases of clinical research and development. A comprehensive and unified presentation of statistical concepts and practical applications this book includes a well-balanced summary of current and emerging clinical issues regulatory requirements and recently developed statistical methodologies for sample size calculation. Features:Compares the relative merits and disadvantages of statistical methods for sample size calculationsExplains how the formulae and procedures for sample size calculations can be used in a variety of clinical research and development stagesPresents real-world examples from several therapeutic areas including cardiovascular medicine the central nervous system anti-infective medicine oncology and women’s healthProvides sample size calculations for dose response studies microarray studies and Bayesian approachesThis new edition is updated throughout includes many new sections and five new chapters on emerging topics: two stage seamless adaptive designs cluster randomized trial design zero-inflated Poisson distribution clinical trials with extremely low incidence rates and clinical trial simulation.

GBP 38.99
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Implementing Reproducible Research

Implementing Reproducible Research

In computational science reproducibility requires that researchers make code and data available to others so that the data can be analyzed in a similar manner as in the original publication. Code must be available to be distributed data must be accessible in a readable format and a platform must be available for widely distributing the data and code. In addition both data and code need to be licensed permissively enough so that others can reproduce the work without a substantial legal burden. Implementing Reproducible Research covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result. Divided into three parts the book discusses the tools practices and dissemination platforms for ensuring reproducibility in computational science. It describes:Computational tools such as Sweave knitr VisTrails Sumatra CDE and the Declaratron systemOpen source practices good programming practices trends in open science and the role of cloud computing in reproducible researchSoftware and methodological platforms including open source software packages RunMyCode platform and open access journalsEach part presents contributions from leaders who have developed software and other products that have advanced the field. Supplementary material is available at www. ImplementingRR. org.

GBP 44.99
1

Exploratory Data Analysis with MATLAB

Exploratory Data Analysis with MATLAB

Praise for the Second Edition:The authors present an intuitive and easy-to-read book. … accompanied by many examples proposed exercises good references and comprehensive appendices that initiate the reader unfamiliar with MATLAB. —Adolfo Alvarez Pinto International Statistical Review Practitioners of EDA who use MATLAB will want a copy of this book. … The authors have done a great service by bringing together so many EDA routines but their main accomplishment in this dynamic text is providing the understanding and tools to do EDA. —David A Huckaby MAA Reviews Exploratory Data Analysis (EDA) is an important part of the data analysis process. The methods presented in this text are ones that should be in the toolkit of every data scientist. As computational sophistication has increased and data sets have grown in size and complexity EDA has become an even more important process for visualizing and summarizing data before making assumptions to generate hypotheses and models. Exploratory Data Analysis with MATLAB Third Edition presents EDA methods from a computational perspective and uses numerous examples and applications to show how the methods are used in practice. The authors use MATLAB code pseudo-code and algorithm descriptions to illustrate the concepts. The MATLAB code for examples data sets and the EDA Toolbox are available for download on the book’s website. New to the Third Edition Random projections and estimating local intrinsic dimensionality Deep learning autoencoders and stochastic neighbor embedding Minimum spanning tree and additional cluster validity indices Kernel density estimation Plots for visualizing data distributions such as beanplots and violin plots A chapter on visualizing categorical data

GBP 44.99
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Displaying Time Series Spatial and Space-Time Data with R

Displaying Time Series Spatial and Space-Time Data with R

Focusing on the exploration of data with visual methods Displaying Time Series Spatial and Space-Time Data with R Second Edition presents methods and R code for producing high-quality static graphics interactive visualizations and animations of time series spatial and space-time data. Practical examples using real-world datasets help you understand how to apply the methods and code. The book illustrates how to display a dataset starting with an easy and direct approach and progressively adds improvements that involve more complexity. Each of the three parts of the book is devoted to different types of data. In each part the chapters are grouped according to the various visualization methods or data characteristics. The first edition of this book was mainly focused on static graphics. Four years later recent developments in the htmlwidgets family of packages are covered in this second edition with many new interactive graphics. In addition the ggplot2 approach is now used in most of the spatial graphics thanks to the new sf package. Finally code has been cleaned and improved and data has been updated. Features• Offers detailed information on producing high-quality graphics interactive visualizations and animations• Uses real data from meteorological climate economic social science energy engineering environmental and epidemiological research in many practical examples• Shows how to improve graphics based on visualization theory• Provides the graphics data and R code on the author’s website enabling you to practice with the methods and modify the code to suit your own needs.

GBP 69.99
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Automata and Computability A Programmer's Perspective

Automata and Computability A Programmer's Perspective

Automata and Computability is a class-tested textbook which provides a comprehensive and accessible introduction to the theory of automata and computation. The author uses illustrations engaging examples and historical remarks to make the material interesting and relevant for students. It incorporates modern/handy ideas such as derivative-based parsing and a Lambda reducer showing the universality of Lambda calculus. The book also shows how to sculpt automata by making the regular language conversion pipeline available through a simple command interface. A Jupyter notebook will accompany the book to feature code YouTube videos and other supplements to assist instructors and studentsFeatures Uses illustrations engaging examples and historical remarks to make the material accessible Incorporates modern/handy ideas such as derivative-based parsing and a Lambda reducer showing the universality of Lambda calculus Shows how to sculpt automata by making the regular language conversion pipeline available through simple command interface Uses a mini functional programming (FP) notation consisting of lambdas maps filters and set comprehension (supported in Python) to convey math through PL constructs that are succinct and resemble math Provides all concepts are encoded in a compact Functional Programming code that will tesselate with Latex markup and Jupyter widgets in a document that will accompany the books. Students can run code effortlessly. All the code can be accessed here. | Automata and Computability A Programmer's Perspective

GBP 39.99
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Computational Mathematics An introduction to Numerical Analysis and Scientific Computing with Python

Computational Mathematics An introduction to Numerical Analysis and Scientific Computing with Python

This textbook is a comprehensive introduction to computational mathematics and scientific computing suitable for undergraduate and postgraduate courses. It presents both practical and theoretical aspects of the subject as well as advantages and pitfalls of classical numerical methods alongside with computer code and experiments in Python. Each chapter closes with modern applications in physics engineering and computer science. Features: No previous experience in Python is required. Includes simplified computer code for fast-paced learning and transferable skills development. Includes practical problems ideal for project assignments and distance learning. Presents both intuitive and rigorous faces of modern scientific computing. Provides an introduction to neural networks and machine learning. | Computational Mathematics An introduction to Numerical Analysis and Scientific Computing with Python

GBP 99.99
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Introduction to R for Social Scientists A Tidy Programming Approach

Introduction to R for Social Scientists A Tidy Programming Approach

Introduction to R for Social Scientists: A Tidy Programming Approach introduces the Tidy approach to programming in R for social science research to help quantitative researchers develop a modern technical toolbox. The Tidy approach is built around consistent syntax common grammar and stacked code which contribute to clear efficient programming. The authors include hundreds of lines of code to demonstrate a suite of techniques for developing and debugging an efficient social science research workflow. To deepen the dedication to teaching Tidy best practices for conducting social science research in R the authors include numerous examples using real world data including the American National Election Study and the World Indicators Data. While no prior experience in R is assumed readers are expected to be acquainted with common social science research designs and terminology. Whether used as a reference manual or read from cover to cover readers will be equipped with a deeper understanding of R and the Tidyverse as well as a framework for how best to leverage these powerful tools to write tidy efficient code for solving problems. To this end the authors provide many suggestions for additional readings and tools to build on the concepts covered. They use all covered techniques in their own work as scholars and practitioners. | Introduction to R for Social Scientists A Tidy Programming Approach

GBP 52.99
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Advanced R Second Edition

Advanced R Second Edition

Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language and programmers experienced in other languages who want to understand what makes R different and special. This book will teach you the foundations of R; three fundamental programming paradigms (functional object-oriented and metaprogramming); and powerful techniques for debugging and optimisingyour code. By reading this book you will learn: The difference between an object and its name and why the distinction is important The important vector data structures how they fit together and how you can pull them apart using subsetting The fine details of functions and environments The condition system which powers messages warnings and errors The powerful functional programming paradigm which can replace many for loops The three most important OO systems: S3 S4 and R6 The tidy eval toolkit for metaprogramming which allows you to manipulate code and control evaluation Effective debugging techniques that you can deploy regardless of how your code is run How to find and remove performance bottlenecks The second edition is a comprehensive update: New foundational chapters: Names and values Control flow and Conditions comprehensive coverage of object oriented programming with chapters on S3 S4 R6 and how to choose between them Much deeper coverage of metaprogramming including the new tidy evaluation framework use of new package like rlang (http://rlang. r-lib. org) which provides a clean interface to low-level operations and purr (http://purrr. tidyverse. org/) for functional programming Use of color in code chunks and figuresHadley Wickham is Chief Scientist at RStudio an Adjunct Professor at Stanford University and the University of Auckland and a member of the R Foundation. He is the lead developer of the tidyverse a collection of R packages including ggplot2 and dplyr designed to support data science. He is also the author of R for Data Science (with Garrett Grolemund) R Packages and ggplot2: Elegant Graphics for Data Analysis. | Advanced R Second Edition

GBP 48.99
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Sample Sizes for Clinical Trials

Sample Sizes for Clinical Trials

Sample Sizes for Clinical Trials Second Edition is a practical book that assists researchers in their estimation of the sample size for clinical trials. Throughout the book there are detailed worked examples to illustrate both how to do the calculations and how to present them to colleagues or in protocols. The book also highlights some of the pitfalls in calculations as well as the key steps that lead to the final sample size calculation. Features: Comprehensive coverage of sample size calculations including Normal binary ordinal and survival outcome data Covers superiority equivalence non-inferiority bioequivalence and precision objectives for both parallel group and crossover designs Highlights how trial objectives impact the study design with respect to both the derivation of sample formulae and the size of the study Motivated with examples of real-life clinical trials showing how the calculations can be applied New edition is extended with all chapters revised some substantially and four completely new chapters on multiplicity cluster trials pilot studies and single arm trials The book is primarily aimed at researchers and practitioners of clinical trials and biostatistics and could be used to teach a course on sample size calculations. The importance of a sample size calculation when designing a clinical trial is highlighted in the book. It enables readers to quickly find an appropriate sample size formula with an associated worked example complemented by tables to assist in the calculations.

GBP 89.99
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Exercises in Programming Style

Exercises in Programming Style

The first edition of Exercises in Programming Style was honored as an ACM Notable Book and praised as The best programming book of the decade. This new edition retains the same presentation but has been upgraded to Python 3 and there is a new section on neural network styles. Using a simple computational task (term frequency) to illustrate different programming styles Exercises in Programming Style helps readers understand the various ways of writing programs and designing systems. It is designed to be used in conjunction with code provided on an online repository. The book complements and explains the raw code in a way that is accessible to anyone who regularly practices the art of programming. The book can also be used in advanced programming courses in computer science and software engineering programs. The book contains 40 different styles for writing the term frequency task. The styles are grouped into ten categories: historical basic function composition objects and object interactions reflection and metaprogramming adversity data-centric concurrency interactivity and neural networks. The author states the constraints in each style and explains the example programs. Each chapter first presents the constraints of the style next shows an example program and then gives a detailed explanation of the code. Most chapters also have sections focusing on the use of the style in systems design as well as sections describing the historical context in which the programming style emerged.

GBP 35.99
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R Markdown Cookbook

R Markdown Cookbook

This new book written by the developers of R Markdown is an essential reference that will help users learn and make full use of the software. Those new to R Markdown will appreciate the short practical examples that address the most common issues users encounter. Frequent users will also benefit from the wide ranging tips and tricks that expose ‘hidden’ features support customization and demonstrate the many new and varied applications of the software. After reading this book users will learn how to: Enhance your R Markdown content with diagrams citations and dynamically generated text Streamline your workflow with child documents code chunk references and caching Control the formatting and layout with Pandoc markdown syntax or by writing custom HTML and LaTeX templates Utilize chunk options and hooks to fine-tune how your code is processed Switch between different language engineers to seamlessly incorporate python D3 and more into your analysis | R Markdown Cookbook

GBP 28.99
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An Introduction to Excel VBA Programming with Applications in Finance and Insurance

An Introduction to Excel VBA Programming with Applications in Finance and Insurance

Excel Visual Basic for Applications (VBA) can be used to automate operations in Excel and is one of the most frequently used software programs for manipulating data and building models in banks and insurance companies. An Introduction to Excel VBA Programming: with Applications in Finance and Insurance introduces readers to the basic fundamentals of VBA Programming while demonstrating applications of VBA to solve real-world problems in finance and insurance. Assuming no prior programming experience and with reproducible examples using code and data this text is suitable for advanced undergraduate students graduate students actuaries and financial analysts who wish to learn VBA. Features: Presents the theory behind the algorithms in detail Includes more than 100 exercises with selected solutions Provides VBA code in Excel files and data to reproduce the results in the book Offers a solutions manual for qualified instructors | An Introduction to Excel VBA Programming with Applications in Finance and Insurance

GBP 44.99
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Compositional Data Analysis in Practice

Compositional Data Analysis in Practice

Compositional data are quantitative descriptions of the parts of some whole conveying exclusively relative information. Examples are found in various fields including geology medicine chemistry agriculture economics social science etc. This concise book presents a very applied introduction to compositional data analysis focussing on the use of R for analysis. It includes lots of real examples code snippets and colour figures to illustrate the methods. | Compositional Data Analysis in Practice

GBP 46.99
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Hands-On Data Science for Librarians

Hands-On Data Science for Librarians

Librarians understand the need to store use and analyze data related to their collection patrons and institution and there has been consistent interest over the last 10 years to improve data management analysis and visualization skills within the profession. However librarians find it difficult to move from out-of-the-box proprietary software applications to the skills necessary to perform the range of data science actions in code. This book will focus on teaching R through relevant examples and skills that librarians need in their day-to-day lives that includes visualizations but goes much further to include web scraping working with maps creating interactive reports machine learning and others. While there’s a place for theory ethics and statistical methods librarians need a tool to help them acquire enough facility with R to utilize data science skills in their daily work no matter what type of library they work at (academic public or special). By walking through each skill and its application to library work before walking the reader through each line of code this book will support librarians who want to apply data science in their daily work. Hands-On Data Science for Librarians is intended for librarians (and other information professionals) in any library type (public academic or special) as well as graduate students in library and information science (LIS). Key Features: Only data science book available geared toward librarians that includes step-by-step code examples Examples include all library types (public academic special) Relevant datasets Accessible to non-technical professionals Focused on job skills and their applications

GBP 52.99
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Security for Software Engineers

Security for Software Engineers

Security for Software Engineers is designed to introduce security concepts to undergraduate software engineering students. The book is divided into four units each targeting activities that a software engineer will likely be involved in within industry. The book explores the key areas of attack vectors code hardening privacy and social engineering. Each topic is explored from a theoretical and a practical-application standpoint. Features:Targets software engineering students - one of the only security texts to target this audience. Focuses on the white-hat side of the security equation rather than the black-hat side. Includes many practical and real-world examples that easily translate into the workplace. Covers a one-semester undergraduate course. Describes all aspects of computer security as it pertains to the job of a software engineer and presents problems similar to that which an engineer will encounter in the industry. This text will equip students to make knowledgeable security decisions be productive members of a security review team and write code that protects a user’s information assets.

GBP 39.99
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Parallel Programming for Modern High Performance Computing Systems

Parallel Programming for Modern High Performance Computing Systems

In view of the growing presence and popularity of multicore and manycore processors accelerators and coprocessors as well as clusters using such computing devices the development of efficient parallel applications has become a key challenge to be able to exploit the performance of such systems. This book covers the scope of parallel programming for modern high performance computing systems. It first discusses selected and popular state-of-the-art computing devices and systems available today These include multicore CPUs manycore (co)processors such as Intel Xeon Phi accelerators such as GPUs and clusters as well as programming models supported on these platforms. It next introduces parallelization through important programming paradigms such as master-slave geometric Single Program Multiple Data (SPMD) and divide-and-conquer. The practical and useful elements of the most popular and important APIs for programming parallel HPC systems are discussed including MPI OpenMP Pthreads CUDA OpenCL and OpenACC. It also demonstrates through selected code listings how selected APIs can be used to implement important programming paradigms. Furthermore it shows how the codes can be compiled and executed in a Linux environment. The book also presents hybrid codes that integrate selected APIs for potentially multi-level parallelization and utilization of heterogeneous resources and it shows how to use modern elements of these APIs. Selected optimization techniques are also included such as overlapping communication and computations implemented using various APIs. Features:Discusses the popular and currently available computing devices and cluster systemsIncludes typical paradigms used in parallel programsExplores popular APIs for programming parallel applicationsProvides code templates that can be used for implementation of paradigmsProvides hybrid code examples allowing multi-level parallelizationCovers the optimization of parallel programs

GBP 44.99
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Cluster Randomised Trials

Cluster Randomised Trials

Cluster Randomised Trials Second Edition discusses the design conduct and analysis of trials that randomise groups of individuals to different treatments. It explores the advantages of cluster randomisation with special attention given to evaluating the effects of interventions against infectious diseases. Avoiding unnecessary mathematical detail the book covers basic concepts underlying the use of cluster randomisation such as direct indirect and total effects. In the time since the publication of the first edition the use of cluster randomised trials (CRTs) has increased substantially which is reflected in the updates to this edition. There are greatly expanded sections on randomisation sample size estimation and alternative designs including new material on stepped wedge designs. There is a new section on handling ordinal outcome data and an appendix with descriptions and/or generating code of the example data sets. Although the book mainly focuses on medical and public health applications it shows that the rigorous evidence of intervention effects provided by CRTs has the potential to inform public policy in a wide range of other areas. The book encourages readers to apply the methods to their own trials reproduce the analyses presented and explore alternative approaches.

GBP 48.99
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Linear Mixed Models A Practical Guide Using Statistical Software

Linear Mixed Models A Practical Guide Using Statistical Software

Highly recommended by JASA Technometrics and other leading statistical journals the first two editions of this bestseller showed how to easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using Statistical Software Third Edition continues to lead readers step-by-step through the process of fitting LMMs. The third edition provides a comprehensive update of the available tools for fitting linear mixed-effects models in the newest versions of SAS SPSS R Stata and HLM. All examples have been updated with a focus on new tools for visualization of results and interpretation. New conceptual and theoretical developments in mixed-effects modeling have been included and there is a new chapter on power analysis for mixed-effects models. Features:•Dedicates an entire chapter to the key theories underlying LMMs for clustered longitudinal and repeated measures data•Provides descriptions explanations and examples of software code necessary to fit LMMs in SAS SPSS R Stata and HLM•Contains detailed tables of estimates and results allowing for easy comparisons across software procedures•Presents step-by-step analyses of real-world data sets that arise from a variety of research settings and study designs including hypothesis testing interpretation of results and model diagnostics•Integrates software code in each chapter to compare the relative advantages and disadvantages of each package•Supplemented by a website with software code datasets additional documents and updates Ideal for anyone who uses software for statistical modeling this book eliminates the need to read multiple software-specific texts by covering the most popular software programs for fitting LMMs in one handy guide. The authors illustrate the models and methods through real-world examples that enable comparisons of model-fitting options and results across the software procedures. | Linear Mixed Models A Practical Guide Using Statistical Software

GBP 89.99
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Practitioner’s Guide to Data Science

Practitioner’s Guide to Data Science

This book aims to increase the visibility of data science in real-world which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics machine learning and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields soft and programming skills in data science projects and potential career paths. Traditional data-related practitioners such as statisticians business analysts and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes. Key Features: • It covers both technical and soft skills. • It has a chapter dedicated to the big data cloud environment. For industry applications the practice of data science is often in such an environment. • It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems if possible. The best way to learn data science is to do it! | Practitioner’s Guide to Data Science

GBP 52.99
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The Art of Coding The Language of Drawing Graphics and Animation

The Art of Coding The Language of Drawing Graphics and Animation

As the title suggests this book explores the concepts of drawing graphics and animation in the context of coding. In this endeavour in addition to initiating the process with some historical perspectives on programming languages it prides itself by presenting complex concepts in an easy-to-understand fashion for students artists hobbyists as well as those interested in computer science computer graphics digital media or interdisciplinary studies. Being able to code requires abstract thinking mathematics skills spatial ability logical thinking imagination and creativity. All these abilities can be acquired with practice and can be mastered by practical exposure to art music and literature. This book discusses art poetry and other forms of writing while pondering difficult concepts in programming; it looks at how we use our senses in the process of learning computing and programming. Features: Introduces coding in a visual way Explores the elegance behind coding and the outcome Includes types of outcomes and options for coding Covers the transition from front-of-classroom instruction to the use of online-streamed video tutorials Encourages abstract and cognitive thinking as well as creativity The Art of Coding contains a collection of learning projects for students instructors and teachers to select specific themes from. Problems and projects are aimed at making the learning process entertaining while also involving social exchange and sharing. This process allows for programming to become interdisciplinary enabling projects to be co-developed by specialists from different backgrounds enriching the value of coding and what it can achieve. The authors of this book hail from three different continents and have several decades of combined experience in academia education science and visual arts. Source Code: The source code for the book can be accessed here. | The Art of Coding The Language of Drawing Graphics and Animation

GBP 48.99
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Design and Analysis of Pragmatic Trials

Design and Analysis of Pragmatic Trials

This book begins with an introduction of pragmatic cluster randomized trials (PCTs) and reviews various pragmatic issues that need to be addressed by statisticians at the design stage. It discusses the advantages and disadvantages of each type of PCT and provides sample size formulas sensitivity analyses and examples for sample size calculation. The generalized estimating equation (GEE) method will be employed to derive sample size formulas for various types of outcomes from the exponential family including continuous binary and count variables. Experimental designs that have been frequently employed in PCTs will be discussed including cluster randomized designs matched-pair cluster randomized design stratified cluster randomized design stepped-wedge cluster randomized design longitudinal cluster randomized design and crossover cluster randomized design. It demonstrates that the GEE approach is flexible to accommodate pragmatic issues such as hierarchical correlation structures different missing data patterns randomly varying cluster sizes etc. It has been reported that the GEE approach leads to under-estimated variance with limited numbers of clusters. The remedy for this limitation is investigated for the design of PCTs. This book can assist practitioners in the design of PCTs by providing a description of the advantages and disadvantages of various PCTs and sample size formulas that address various pragmatic issues facilitating the proper implementation of PCTs to improve health care. It can also serve as a textbook for biostatistics students at the graduate level to enhance their knowledge or skill in clinical trial design. Key Features: Discuss the advantages and disadvantages of each type of PCTs and provide sample size formulas sensitivity analyses and examples. Address an unmet need for guidance books on sample size calculations for PCTs; A wide variety of experimental designs adopted by PCTs are covered; The sample size solutions can be readily implemented due to the accommodation of common pragmatic issues encountered in real-world practice; Useful to both academic and industrial biostatisticians involved in clinical trial design; Can be used as a textbook for graduate students majoring in statistics and biostatistics. | Design and Analysis of Pragmatic Trials

GBP 89.99
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A Criminologist's Guide to R Crime by the Numbers

A Criminologist's Guide to R Crime by the Numbers

A Criminologist's Guide to R: Crime by the Numbers introduces the programming language R and covers the necessary skills to conduct quantitative research in criminology. By the end of this book a person without any prior programming experience can take raw crime data be able to clean it visualize the data present it using R Markdown and change it to a format ready for analysis. A Criminologist's Guide to R focuses on skills specifically for criminology such as spatial joins mapping and scraping data from PDFs however any social scientist looking for an introduction to R for data analysis will find this useful. Key Features: Introduction to RStudio including how to change user preference settings. Basic data exploration and cleaning – subsetting loading data regular expressions aggregating data. Graphing with ggplot2. How to make maps (hotspot maps choropleth maps interactive maps). Webscraping and PDF scraping. Project management – how to prepare for a project how to decide which projects to do best ways to collaborate with people how to store your code (using git) and how to test your code. | A Criminologist's Guide to R Crime by the Numbers

GBP 74.99
1