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How Things Work The Computer Science Edition

The Future of Work and Technology Global Trends Challenges and Policies with an Australian Perspective

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
1

Introductory Concepts for Abstract Mathematics

Computational Methods for Numerical Analysis with R

Python for Beginners

Python for Beginners

Python is an amazing programming language. It can be applied to almost any programming task. It allows for rapid development and debugging. Getting started with Python is like learning any new skill: it’s important to find a resource you connect with to guide your learning. Luckily there’s no shortage of excellent books that can help you learn both the basic concepts of programming and the specifics of programming in Python. With the abundance of resources it can be difficult to identify which book would be best for your situation. Python for Beginners is a concise single point of reference for all material on python. Provides concise need-to-know information on Python types and statements special method names built-in functions and exceptions commonly used standard library modules and other prominent Python tools Offers practical advice for each major area of development with both Python 3. x and Python 2. x Based on the latest research in cognitive science and learning theory Helps the reader learn how to write effective idiomatic Python code by leveraging its best—and possibly most neglected—features This book focuses on enthusiastic research aspirants who work on scripting languages for automating the modules and tools development of web applications handling big data complex calculations workflow creation rapid prototyping and other software development purposes. It also targets graduates postgraduates in computer science information technology academicians practitioners and research scholars.

GBP 120.00
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Geometry for the Artist

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

Image Processing for Cinema

Numerical Methods for Engineers

Numerical Methods for Engineers

Although pseudocodes Mathematica® and MATLAB® illustrate how algorithms work designers of engineering systems write the vast majority of large computer programs in the Fortran language. Using Fortran 95 to solve a range of practical engineering problems Numerical Methods for Engineers Second Edition provides an introduction to numerical methods incorporating theory with concrete computing exercises and programmed examples of the techniques presented. Covering a wide range of numerical applications that have immediate relevancy for engineers the book describes forty-nine programs in Fortran 95. Many of the programs discussed use a sub-program library called nm_lib that holds twenty-three subroutines and functions. In addition there is a precision module that controls the precision of calculations. Well-respected in their field the authors discuss a variety of numerical topics related to engineering. Some of the chapter features include…The numerical solution of sets of linear algebraic equationsRoots of single nonlinear equations and sets of nonlinear equations Numerical quadrature or numerical evaluation of integralsAn introduction to the solution of partial differential equations using finite difference and finite element approachesDescribing concise programs that are constructed using sub-programs wherever possible this book presents many different contexts of numerical analysis forming an excellent introduction to more comprehensive subroutine libraries such as the numerical algorithm group (NAG).

GBP 59.99
1

Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers

Tree-Based Methods for Statistical Learning in R

Tree-Based Methods for Statistical Learning in R

Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus both conventional and contemporary. Building a strong foundation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level which lie at the cutting edge of modern statistical and machine learning methodology. The book follows up most ideas and mathematical concepts with code-based examples in the R statistical language; with an emphasis on using as few external packages as possible. For example users will be exposed to writing their own random forest and gradient tree boosting functions using simple for loops and basic tree fitting software (like rpart and party/partykit) and more. The core chapters also end with a detailed section on relevant software in both R and other opensource alternatives (e. g. Python Spark and Julia) and example usage on real data sets. While the book mostly uses R it is meant to be equally accessible and useful to non-R programmers. Consumers of this book will have gained a solid foundation (and appreciation) for tree-based methods and how they can be used to solve practical problems and challenges data scientists often face in applied work. Features: Thorough coverage from the ground up of tree-based methods (e. g. CART conditional inference trees bagging boosting and random forests). A companion website containing additional supplementary material and the code to reproduce every example and figure in the book. A companion R package called treemisc which contains several data sets and functions used throughout the book (e. g. there’s an implementation of gradient tree boosting with LAD loss that shows how to perform the line search step by updating the terminal node estimates of a fitted rpart tree). Interesting examples that are of practical use; for example how to construct partial dependence plots from a fitted model in Spark MLlib (using only Spark operations) or post-processing tree ensembles via the LASSO to reduce the number of trees while maintaining or even improving performance.

GBP 82.99
1

Artificial Intelligence for Cognitive Modeling Theory and Practice

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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Introduction to R for Social Scientists A Tidy Programming Approach

Probability and Statistics for Computer Scientists

Probability and Statistics for Computer Scientists

Praise for the Second Edition: The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter. [He] has taken great care to select examples that are interesting and practical for computer scientists. . The content is illustrated with numerous figures and concludes with appendices and an index. The book is erudite and … could work well as a required text for an advanced undergraduate or graduate course. Computing Reviews Probability and Statistics for Computer Scientists Third Edition helps students understand fundamental concepts of Probability and Statistics general methods of stochastic modeling simulation queuing and statistical data analysis; make optimal decisions under uncertainty; model and evaluate computer systems; and prepare for advanced probability-based courses. Written in a lively style with simple language and now including R as well as MATLAB this classroom-tested book can be used for one- or two-semester courses. Features: Axiomatic introduction of probability Expanded coverage of statistical inference and data analysis including estimation and testing Bayesian approach multivariate regression chi-square tests for independence and goodness of fit nonparametric statistics and bootstrap Numerous motivating examples and exercises including computer projects Fully annotated R codes in parallel to MATLAB Applications in computer science software engineering telecommunications and related areas In-Depth yet Accessible Treatment of Computer Science-Related TopicsStarting with the fundamentals of probability the text takes students through topics heavily featured in modern computer science computer engineering software engineering and associated fields such as computer simulations Monte Carlo methods stochastic processes Markov chains queuing theory statistical inference and regression. It also meets the requirements of the Accreditation Board for Engineering and Technology (ABET). About the Author Michael Baron is David Carroll Professor of Mathematics and Statistics at American University in Washington D. C. He conducts research in sequential analysis and optimal stopping change-point detection Bayesian inference and applications of statistics in epidemiology clinical trials semiconductor manufacturing and other fields. M. Baron is a Fellow of the American Statistical Association and a recipient of the Abraham Wald Prize for the best paper in Sequential Analysis and the Regents Outstanding Teaching Award. M. Baron holds a Ph. D. in statistics from the University of Maryland. In his turn he supervised twelve doctoral students mostly employed on academic and research positions.

GBP 99.99
1

R for Political Data Science A Practical Guide

R for Political Data Science A Practical Guide

R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists thinking about the many real-world problems faced in their work. The book has 16 chapters and is organized in three sections. The first on the use of R is for those users who are learning R or are migrating from another software. The second section on econometric models covers OLS binary and survival models panel data and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation fuzzy merge of large datasets web mining quantitative text analysis network analysis mapping spatial cluster analysis and principal component analysis. Key features: Each chapter has the most up-to-date and simple option available for each task assuming minimal prerequisites and no previous experience in R Makes extensive use of the Tidyverse the group of packages that has revolutionized the use of R Provides a step-by-step guide that you can replicate using your own data Includes exercises in every chapter for course use or self-study Focuses on practical-based approaches to statistical inference rather than mathematical formulae Supplemented by an R package including all data As the title suggests this book is highly applied in nature and is designed as a toolbox for the reader. It can be used in methods and data science courses at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD political consultants or a public official all of whom need to transform their datasets into substantive and easily interpretable conclusions. | R for Political Data Science A Practical Guide

GBP 44.99
1

Applications of Regression for Categorical Outcomes Using R

Applications of Regression for Categorical Outcomes Using R

This book covers the main models within the GLM (i. e. logistic Poisson negative binomial ordinal and multinomial). For each model estimations interpretations model fit diagnostics and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata SPSS and SAS to using R and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge and for Quantitative social scientists due to it’s ability to act as a practitioners guide. Key Features: Applied- in the sense that we will provide code that others can easily adapt Flexible- R is basically just a fancy calculator. Our programs will enable users to derive quantities that they can use in their work Timely- many in the social sciences are currently transitioning to R or are learning it now. Our book will be a useful resource Versatile- we will write functions into an R package that can be applied to all of the regression models we will cover in the book Aesthetically pleasing- one advantage of R relative to other software packages is that graphs are fully customizable. We will leverage this feature to yield high-end graphical displays of results Affordability- R is free. R packages are free. There is no need to purchase site licenses or updates.

GBP 59.99
1

Statistical Process Control For Quality Improvement- Hardcover Version

Statistical Process Control For Quality Improvement- Hardcover Version

While the common practice of Quality Assurance aims to prevent bad units from being shipped beyond some allowable proportion statistical process control (SPC) ensures that bad units are not created in the first place. Its philosophy of continuous quality improvement to a great extent responsible for the success of Japanese manufacturing is rooted in a paradigm as process-oriented as physics yet produces a friendly and fulfilling work environment. The first edition of this groundbreaking text showed that the SPC paradigm of W. Edwards Deming was not at all the same as the Quality Control paradigm that has dominated American manufacturing since World War II. Statistical Process Control: The Deming Paradigm and Beyond Second Edition reveals even more of Deming's philosophy and provides more techniques for use at the managerial level. Explaining that CEOs and service industries need SPC at least as much as production managers it offers precise methods and guidelines for their use. Using the practical experience of the authors working both in America and Europe this book shows how SPC can be implemented in a variety of settings from health care to manufacturing. It also provides you with the necessary technical background through mathematical and statistical appendices. According to the authors companies with managers who have adopted the philosophy of statistical process control tend to survive. Those with managers who do not are likely to fail. In which group will your company be? | Statistical Process Control For Quality Improvement- Hardcover Version

GBP 44.99
1

Probability and Statistics for Engineering and the Sciences with Modeling using R

Probability and Statistics for Engineering and the Sciences with Modeling using R

Probability and statistics courses are more popular than ever. Regardless of your major or your profession you will most likely use concepts from probability and statistics often in your career. The primary goal behind this book is offering the flexibility for instructors to build most undergraduate courses upon it. This book is designed for either a one-semester course in either introductory probability and statistics (not calculus-based) and/or a one-semester course in a calculus-based probability and statistics course. The book focuses on engineering examples and applications while also including social sciences and more examples. Depending on the chapter flows a course can be tailored for students at all levels and background. Over many years of teaching this course the authors created problems based on real data student projects and labs. Students have suggested these enhance their experience and learning. The authors hope to share projects and labs with other instructors and students to make the course more interesting for both. R is an excellent platform to use. This book uses R with real data sets. The labs can be used for group work in class or for self-directed study. These project labs have been class-tested for many years with good results and encourage students to apply the key concepts and use of technology to analyze and present results. | Probability and Statistics for Engineering and the Sciences with Modeling using R

GBP 89.99
1

Student Solutions Manual for Gallian's Contemporary Abstract Algebra

Student Solutions Manual for Gallian's Contemporary Abstract Algebra

Whereas many partial solutions and sketches for the odd-numbered exercises appear in the book the Student Solutions Manual written by the author has comprehensive solutions for all odd-numbered exercises and large number of even-numbered exercises. This Manual also offers many alternative solutions to those appearing in the text. These will provide the student with a better understanding of the material. This is the only available student solutions manual prepared by the author of Contemporary Abstract Algebra Tenth Edition and is designed to supplement that text. Table of Contents Integers and Equivalence Relations0. Preliminaries Groups1. Introduction to Groups 2. Groups 3. Finite Groups; Subgroups 4. Cyclic Groups 5. Permutation Groups 6. Isomorphisms 7. Cosets and Lagrange's Theorem 8. External Direct Products 9. Normal Subgroups and Factor Groups 10. Group Homomorphisms 11. Fundamental Theorem of Finite Abelian Groups Rings12. Introduction to Rings 13. Integral Domains14. Ideals and Factor Rings 15. Ring Homomorphisms 16. Polynomial Rings 17. Factorization of Polynomials 18. Divisibility in Integral Domains FieldsFields19. Extension Fields 20. Algebraic Extensions21. Finite Fields 22. Geometric Constructions Special Topics23. Sylow Theorems 24. Finite Simple Groups 25. Generators and Relations 26. Symmetry Groups 27. Symmetry and Counting 28. Cayley Digraphs of Groups 29. Introduction to Algebraic Coding Theory 30. An Introduction to Galois Theory 31. Cyclotomic Extensions Biography Joseph A. Gallian earned his PhD from Notre Dame. In addition to receiving numerous national awards for his teaching and exposition he has served terms as the Second Vice President and the President of the MAA. He has served on 40 national committees chairing ten of them. He has published over 100 articles and authored six books. Numerous articles about his work have appeared in the national news outlets including the New York Times the Washington Post the Boston Globe and Newsweek among many others. | Student Solutions Manual for Gallian's Contemporary Abstract Algebra

GBP 44.99
1

C++ for Financial Mathematics

State-Space Methods for Time Series Analysis Theory Applications and Software

State-Space Methods for Time Series Analysis Theory Applications and Software

The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover it can accommodate with a reasonable effort nonstandard situations such as observation errors aggregation constraints or missing in-sample values. Exploring the advantages of this approach State-Space Methods for Time Series Analysis: Theory Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form. After discussing the formulation of the state-space model the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables. Web ResourceThe authors’ E4 MATLAB® toolbox offers all the computational procedures administrative and analytical functions and related materials for time series analysis. This flexible powerful and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work. | State-Space Methods for Time Series Analysis Theory Applications and Software

GBP 48.99
1

Python for Bioinformatics

Artificial Intelligence for Autonomous Networks