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Clean Numerical Simulation

Clean Numerical Simulation

This book describes the basic principles of Clean Numerical Simulation (CNS) proposed by the author in 2009 as well as several of its applications. Unlike conventional algorithms CNS gives in a sufficiently long interval of time a convergent chaotic trajectory whose numerical noise is much lower than the true physical solution so that one can gain accurately. Thus CNS provides for the first time an ability to check statistics stability of chaos leading to a completely new concept of ultra-chaos which has both trajectory instability and statistics instability and thus is of a higher disorder. Notably it is impossible to repeat experimental results of ultra-chaos even in the statistical sense. However the reproducibility of physical experiments forms a cornerstone for modern science. Thus ultra-chaos reveals an incompleteness of the modern science paradigm. In addition it also reveals statistics stability as a precondition for use of conventional algorithms including direct numerical simulation (DNS). In Clean Numerical Simulation several conjectures and open problems are proposed including a modified fourth Clay millennium problem. Indeed CNS opens the door for us to enter the clean numerical world of chaos and turbulence.

GBP 74.99
1

Explorations in Computing An Introduction to Computer Science and Python Programming

Explorations in Computing An Introduction to Computer Science and Python Programming

An Active Learning Approach to Teaching the Main Ideas in Computing Explorations in Computing: An Introduction to Computer Science and Python Programming teaches computer science students how to use programming skills to explore fundamental concepts and computational approaches to solving problems. Tbook gives beginning students an introduction to computer science concepts and computer programming. Designed for CS0 and CS1 courses it is very well suited for alternative lecture styles including flipped classrooms. Prepares Students for Advanced Work in Computer ScienceA revised and updated version of the author’s Explorations in Computing: An Introduction to Computer Science this text incorporates two major differences. It now uses Python instead of Ruby as the lab software so that students can seamlessly transition from introductory projects to more advanced studies in later courses. The book also introduces Python programming providing students with sufficient programming skills so they can implement their own programs. Practical Step-by-Step ProjectsThe interactive lab projects in each chapter allow students to examine important ideas in computer science particularly how algorithms offer computational solutions to problems. Students can type expressions view results and run experiments that help them understand the concepts in a hands-on way. Web ResourcesThe Python software modules for each lab project are available on the author’s website. The modules include data files and sample Python code that students can copy and modify. In addition the site provides a lab manual of installation instructions and tips for editing programs and running commands in a terminal emulator. | Explorations in Computing An Introduction to Computer Science and Python Programming

GBP 46.99
1

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques reinforced via realistic applications. The book is accessible and doesn’t prove theorems or dwell on mathematical theory. The goal is to present topics at an intuitive level with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth including Hidden Markov Models (HMM) Support Vector Machines (SVM) and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN) boosting Random Forests and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation Convolutional Neural Networks (CNN) Multilayer Perceptrons (MLP) and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented including Long Short-Term Memory (LSTM) Generative Adversarial Networks (GAN) Extreme Learning Machines (ELM) Residual Networks (ResNet) Deep Belief Networks (DBN) Bidirectional Encoder Representations from Transformers (BERT) and Word2Vec. Finally several cutting-edge deep learning topics are discussed including dropout regularization attention explainability and adversarial attacks. Most of the examples in the book are drawn from the field of information security with many of the machine learning and deep learning applications focused on malware. The applications presented serve to demystify the topics by illustrating the use of various learning techniques in straightforward scenarios. Some of the exercises in this book require programming and elementary computing concepts are assumed in a few of the application sections. However anyone with a modest amount of computing experience should have no trouble with this aspect of the book. Instructor resources including PowerPoint slides lecture videos and other relevant material are provided on an accompanying website: http://www. cs. sjsu. edu/~stamp/ML/.

GBP 62.99
1

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
1

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
1

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
1

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

Correspondence Analysis in Practice

Correspondence Analysis in Practice

Drawing on the author’s 45 years of experience in multivariate analysis Correspondence Analysis in Practice Third Edition shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. CA and its variants subset CA multiple CA and joint CA translate two-way and multi-way tables into more readable graphical forms — ideal for applications in the social environmental and health sciences as well as marketing economics linguistics archaeology and more. Michael Greenacre is Professor of Statistics at the Universitat Pompeu Fabra Barcelona Spain where he teaches a course amongst others on Data Visualization. He has authored and co-edited nine books and 80 journal articles and book chapters mostly on correspondence analysis the latest being Visualization and Verbalization of Data in 2015. He has given short courses in fifteen countries to environmental scientists sociologists data scientists and marketing professionals and has specialized in statistics in ecology and social science.

GBP 48.99
1

A Concise Introduction to Statistical Inference

A Concise Introduction to Statistical Inference

This short book introduces the main ideas of statistical inference in a way that is both user friendly and mathematically sound. Particular emphasis is placed on the common foundation of many models used in practice. In addition the book focuses on the formulation of appropriate statistical models to study problems in business economics and the social sciences as well as on how to interpret the results from statistical analyses. The book will be useful to students who are interested in rigorous applications of statistics to problems in business economics and the social sciences as well as students who have studied statistics in the past but need a more solid grounding in statistical techniques to further their careers. Jacco Thijssen is professor of finance at the University of York UK. He holds a PhD in mathematical economics from Tilburg University Netherlands. His main research interests are in applications of optimal stopping theory stochastic calculus and game theory to problems in economics and finance. Professor Thijssen has earned several awards for his statistics teaching. | A Concise Introduction to Statistical Inference

GBP 180.00
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An Introduction to Metric Spaces

An Introduction to Metric Spaces

This book serves as a textbook for an introductory course in metric spaces for undergraduate or graduate students. The goal is to present the basics of metric spaces in a natural and intuitive way and encourage students to think geometrically while actively participating in the learning of this subject. In this book the authors illustrated the strategy of the proofs of various theorems that motivate readers to complete them on their own. Bits of pertinent history are infused in the text including brief biographies of some of the central players in the development of metric spaces. The textbook is divided into seven chapters that contain the main materials on metric spaces; namely introductory concepts completeness compactness connectedness continuous functions and metric fixed point theorems with applications. Some of the noteworthy features of this book include · Diagrammatic illustrations that encourage readers to think geometrically · Focus on systematic strategy to generate ideas for the proofs of theorems · A wealth of remarks observations along with a variety of exercises · Historical notes and brief biographies appearing throughout the text | An Introduction to Metric Spaces

GBP 32.99
1

An Introduction to Nonparametric Statistics

An Introduction to Nonparametric Statistics

An Introduction to Nonparametric Statistics presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data. Rank-based and resampling techniques are heavily represented but robust techniques are considered as well. These techniques include one-sample testing and estimation multi-sample testing and estimation and regression. Attention is paid to the intellectual development of the field with a thorough review of bibliographical references. Computational tools in R and SAS are developed and illustrated via examples. Exercises designed to reinforce examples are included. Features Rank-based techniques including sign Kruskal-Wallis Friedman Mann-Whitney and Wilcoxon tests are presented Tests are inverted to produce estimates and confidence intervals Multivariate tests are explored Techniques reflecting the dependence of a response variable on explanatory variables are presented Density estimation is explored The bootstrap and jackknife are discussed This text is intended for a graduate student in applied statistics. The course is best taken after an introductory course in statistical methodology elementary probability and regression. Mathematical prerequisites include calculus through multivariate differentiation and integration and ideally a course in matrix algebra.

GBP 84.99
1

An Introduction to Optimization with Applications in Machine Learning and Data Analytics

An Introduction to Optimization with Applications in Machine Learning and Data Analytics

The primary goal of this text is a practical one. Equipping students with enough knowledge and creating an independent research platform the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of optimization. The initial goal of this text is to develop a marketable skill set for mathematics majors as well as for students of engineering computer science economics statistics and business. Optimization reaches into many different fields. This text provides a balance where one is needed. Mathematics optimization books are often too heavy on theory without enough applications; texts aimed at business students are often strong on applications but weak on math. The book represents an attempt at overcoming this imbalance for all students taking such a course. The book contains many practical applications but also explains the mathematics behind the techniques including stating definitions and proving theorems. Optimization techniques are at the heart of the first spam filters are used in self-driving cars play a great role in machine learning and can be used in such places as determining a batting order in a Major League Baseball game. Additionally optimization has seemingly limitless other applications in business and industry. In short knowledge of this subject offers an individual both a very marketable skill set for a wealth of jobs as well as useful tools for research in many academic disciplines. Many of the problems rely on using a computer. Microsoft’s Excel is most often used as this is common in business but Python and other languages are considered. The consideration of other programming languages permits experienced mathematics and engineering students to use MATLAB® or Mathematica and the computer science students to write their own programs in Java or Python. | An Introduction to Optimization with Applications in Machine Learning and Data Analytics

GBP 82.99
1

Acceptance Sampling in Quality Control

Acceptance Sampling in Quality Control

Acceptance Sampling in Quality Control Third Edition presents the state of the art in the methodology of sampling while integrating both theory and best practices. It discusses various standards including those from the ISO MIL-STD and ASTM and explores how to set quality levels. The book also includes problems at the end of each chapter with solutions. This edition improves upon the previous editions especially in the areas of software applications and compliance sampling plans. New to the Third Edition: Numerous Microsoft Excel templates to address sampling plans are used. Commercial software applications are discussed at the end of many chapters. Discussion of quick switching systems has been expanded to account for the considerable recent activity in this area. Added discussion of zero acceptance number chained quick switching systems.

GBP 115.00
1

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
1

Introduction to Number Theory

Introduction to Number Theory

Introduction to Number Theory covers the essential content of an introductory number theory course including divisibility and prime factorization congruences and quadratic reciprocity. The instructor may also choose from a collection of additional topics. Aligning with the trend toward smaller essential texts in mathematics the author strives for clarity of exposition. Proof techniques and proofs are presented slowly and clearly. The book employs a versatile approach to the use of algebraic ideas. Instructors who wish to put this material into a broader context may do so though the author introduces these concepts in a non-essential way. A final chapter discusses algebraic systems (like the Gaussian integers) presuming no previous exposure to abstract algebra. Studying general systems helps students to realize unique factorization into primes is a more subtle idea than may at first appear; students will find this chapter interesting fun and quite accessible. Applications of number theory include several sections on cryptography and other applications to further interest instructors and students alike.

GBP 38.99
1

A First Course in Functional Analysis

A First Course in Functional Analysis

Written as a textbook A First Course in Functional Analysis is an introduction to basic functional analysis and operator theory with an emphasis on Hilbert space methods. The aim of this book is to introduce the basic notions of functional analysis and operator theory without requiring the student to have taken a course in measure theory as a prerequisite. It is written and structured the way a course would be designed with an emphasis on clarity and logical development alongside real applications in analysis. The background required for a student taking this course is minimal; basic linear algebra calculus up to Riemann integration and some acquaintance with topological and metric spaces.

GBP 46.99
1

New Centrality Measures in Networks How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes

New Centrality Measures in Networks How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes

Over the last number of years there has been a growing interest in the analysis of complex networks which describe a wide range of real-world systems in nature and society. Identification of the central elements in such networks is one of the key research areas. Solutions to this problem are important for making strategic decisions and studying the behavior of dynamic processes e. g. epidemic spread. The importance of nodes has been studied using various centrality measures. Generally it should be considered that most real systems are not homogeneous: nodes may have individual attributes and influence each other in groups while connections between nodes may describe different types of relations. Thus critical nodes detection is not a straightforward process. New Centrality Measures in Networks presents a class of new centrality measures which take into account individual attributes of nodes the possibility of group influence and long-range interactions and discusses all their new features. The book provides a wide range of applications of network analysis in several fields – financial networks international migration global trade global food network arms transfers networks of terrorist groups and networks of international journals in economics. Real-world studies of networks indicate that the proposed centrality measures can identify important nodes in different applications. Starting from the basic ideas the development of the indices and their advantages compared to existing centrality measures are presented. Features Built around real-world case studies in a variety of different areas (finance migration trade etc. ) Suitable for students and professional researchers with an interest in complex network analysis Paired with a software package for readers who wish to apply the proposed models of centrality (in Python) available at https://github. com/SergSHV/slric. | New Centrality Measures in Networks How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes

GBP 48.99
1

From Computing to Computational Thinking

From Computing to Computational Thinking

Computational Thinking (CT) involves fundamental concepts and reasoning distilled from computer science and other computational sciences which become powerful general mental tools for solving problems increasing efficiency reducing complexity designing procedures or interacting with humans and machines. An easy-to-understand guidebook From Computing to Computational Thinking gives you the tools for understanding and using CT. It does not assume experience or knowledge of programming or of a programming language but explains concepts and methods for CT with clarity and depth. Successful applications in diverse disciplines have shown the power of CT in problem solving. The book uses puzzles games and everyday examples as starting points for discussion and for connecting abstract thinking patterns to real-life situations. It provides an interesting and thought-provoking way to gain general knowledge about modern computing and the concepts and thinking processes underlying modern digital technologies.

GBP 180.00
1

Introduction to Web Interaction Design With HTML and CSS

Introduction to Web Interaction Design With HTML and CSS

This book introduces standard and new HTML5 elements and attributes and CSS3 properties commonly used in Web design as well as design guidelines for their effective use. Its approach of explaining every line of code in the examples it uses to show the usage of the HTML elements and CSS properties introduced makes it an invaluable Web design resource for beginners as well as intermediates looking to fill in gaps in their knowledge. In addition the inclusion of user-centered design process stages and how they are best managed in website development makes the book unique in its area. Also the book's approach of including challenges after each topic to help refresh readers' knowledge as well as make them think ensures that there are ample activities to keep learners motivated and engaged. Key Features Comprehensively covers standard and new HTML5 elements and attributes and CSS3 properties. Includes a lot of challenges/exercises; one after each HTML element or CSS property introduced and demonstrated with examples. Example codes can be copied and pasted as-is to implement and experiment with. For every HTML element or CSS property introduced guidelines are provided where relevant on how to best use them in a design to enhance usability and accessibility. Includes comprehensive explanation of flexible box and grid layout models and how to use them to create responsive and adaptive Web design. Covers the importance of visual aesthetics in design including design elements and principles and examples of how they can be applied in Web design to produce good user experience. Includes comprehensive guidelines on how to design for standard and mobile screens including discussion of touch gesture interaction design and standard gestures and the functions for which they are most commonly used. Introduces the stages of user-centered design process including Web accessibility and user-experience testing and managerial aspects of Web development including intellectual property. Provides a brief introduction on how to make HTML and CSS codes more compact and more efficient and how to combine them with other languages commonly used in Web design and development such as JavaScript AJAX and PHP. | Introduction to Web Interaction Design With HTML and CSS

GBP 59.99
1

Real-World Evidence in a Patient-Centric Digital Era

Real-World Evidence in a Patient-Centric Digital Era

Real-world evidence is defined as evidence generated from real-world data outside randomized controlled trials. As scientific discoveries and methodologies continue to advance real-world data and their companion technologies offer powerful new tools for evidence generation. Real-World Evidence in a Patient-Centric Digital Era provides perspectives examples and insights on the innovative application of real-world evidence to meet patient needs and improve healthcare with a focus on the pharmaceutical industry. This book presents an overview of key analytical issues and best practices. Special attention is paid to the development methodologies and other salient features of the statistical and data science techniques that are customarily used to generate real-world evidence. It provides a review of key topics and emerging trends in cutting-edge data science and health innovation. Features: Provides an overview of statistical and analytic methodologies in real-world evidence to generate insights on healthcare with a special focus on the pharmaceutical industry Examines timely topics of high relevance to industry such as bioethical considerations regulatory standards and compliance requirements Highlights emerging and current trends and provides guidelines for best practices Illustrates methods through examples and use-case studies to demonstrate impact Provides guidance on software choices and digital applications for successful analytics Real-World Evidence in a Patient-Centric Digital Era will be a vital reference for medical researchers health technology innovators data scientists epidemiologists population health analysts health economists outcomes researchers policymakers and analysts in the healthcare industry.

GBP 99.99
1

A Bridge to Higher Mathematics

A Bridge to Higher Mathematics

A Bridge to Higher Mathematics is more than simply another book to aid the transition to advanced mathematics. The authors intend to assist students in developing a deeper understanding of mathematics and mathematical thought. The only way to understand mathematics is by doing mathematics. The reader will learn the language of axioms and theorems and will write convincing and cogent proofs using quantifiers. Students will solve many puzzles and encounter some mysteries and challenging problems. The emphasis is on proof. To progress towards mathematical maturity it is necessary to be trained in two aspects: the ability to read and understand a proof and the ability to write a proof. The journey begins with elements of logic and techniques of proof then with elementary set theory relations and functions. Peano axioms for positive integers and for natural numbers follow in particular mathematical and other forms of induction. Next is the construction of integers including some elementary number theory. The notions of finite and infinite sets cardinality of counting techniques and combinatorics illustrate more techniques of proof. For more advanced readers the text concludes with sets of rational numbers the set of reals and the set of complex numbers. Topics like Zorn‘s lemma and the axiom of choice are included. More challenging problems are marked with a star. All these materials are optional depending on the instructor and the goals of the course.

GBP 175.00
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
1

Handbook of Regression Modeling in People Analytics With Examples in R and Python

Handbook of Regression Modeling in People Analytics With Examples in R and Python

Despite the recent rapid growth in machine learning and predictive analytics many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best ‘swiss army knife’ we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent the methods easily transfer to any discipline. The book hits a ‘sweet spot’ where there is just enough mathematical theory to support a strong understanding of the methods but with a step-by-step guide and easily reproducible examples and code so that the methods can be put into practice immediately. This makes the book accessible to a wide readership from public and private sector analysts and practitioners to students and researchers. Key Features: 16 accompanying datasets across a wide range of contexts (e. g. academic corporate sports marketing) Clear step-by-step instructions on executing the analyses Clear guidance on how to interpret results Primary instruction in R but added sections for Python coders Discussion exercises and data exercises for each of the main chapters Final chapter of practice material and datasets ideal for class homework or project work. | Handbook of Regression Modeling in People Analytics With Examples in R and Python

GBP 66.99
1

Analytic Methods in Sports Using Mathematics and Statistics to Understand Data from Baseball Football Basketball and Other Sports

Analytic Methods in Sports Using Mathematics and Statistics to Understand Data from Baseball Football Basketball and Other Sports

One of the greatest changes in the sports world in the past 20 years has been the use of mathematical methods to analyze performances recognize trends and patterns and predict results. Analytic Methods in Sports: Using Mathematics and Statistics to Understand Data from Baseball Football Basketball and Other Sports Second Edition provides a concise yet thorough introduction to the analytic and statistical methods that are useful in studying sports. The book gives you all the tools necessary to answer key questions in sports analysis. It explains how to apply the methods to sports data and interpret the results demonstrating that the analysis of sports data is often different from standard statistical analyses. The book integrates a large number of motivating sports examples throughout and offers guidance on computation and suggestions for further reading in each chapter. Features Covers numerous statistical procedures for analyzing data based on sports results Presents fundamental methods for describing and summarizing data Describes aspects of probability theory and basic statistical concepts that are necessary to understand and deal with the randomness inherent in sports data Explains the statistical reasoning underlying the methods Illustrates the methods using real data drawn from a wide variety of sports Offers many of the datasets on the author’s website enabling you to replicate the analyses or conduct related analyses New to the Second Edition R code included for all calculations A new chapter discussing several more advanced methods such as binary response models random effects multilevel models spline methods and principal components analysis and more Exercises added to the end of each chapter to enable use for courses and self-study Full solutions manual available to course instructors. | Analytic Methods in Sports Using Mathematics and Statistics to Understand Data from Baseball Football Basketball and Other Sports

GBP 56.99
1

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
1