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A Gentle Introduction to Scientific Computing
Scientific Computation has established itself as a stand-alone area of knowledge at the borderline between computer science and applied mathematics. Nonetheless, its interdisciplinary character cannot be denied: its methodologies are increasingly used in a wide variety of branches of science and engineering. A Gentle Introduction to Scientific Computing intends to serve a very broad audience of college students across a variety of disciplines. It aims to expose its readers to some of the basic tools and techniques used in computational science, with a view to helping them understand what happens "behind the scenes" when simple tools such as solving equations, plotting and interpolation are u...
Optimization Modelling Using R
This book covers using R for doing optimization, a key area of operations research, which has been applied to virtually every industry. The focus is on linear and mixed integer optimization. It uses an algebraic modeling approach for creating formulations that pairs naturally with an algebraic implementation in R. With the rapid rise of interest in data analytics, a data analytics platform is key. Working technology and business professionals need an awareness of the tools and language of data analysis. R reduces the barrier to entry for people to start using data analytics tools. Philosophically, the book emphasizes creating formulations before going into implementation. Algebraic represent...
Handbook of Teaching and Learning in Sociology
Showcasing advanced research from over 30 expert sociologists, this dynamic Handbook explores a wide range of cutting-edge developments in scholarship on teaching and learning in sociology. It presents instructors with a comprehensive companion on how to achieve excellence in teaching, both in individual courses and across the undergraduate sociology curriculum.
Introduction to Computational Mathematics
This is an interactive e-book for students and instructors to introduce the concepts of mathematical computation and numerical analysis earlier in the curriculum. These courses are taught most commonly at the senior undergraduate level and students have little exposure to the topics taught prior. This is meant as a self-study module or as a supplement to instruction. There are many links to other resources included.
Introduction to Computational Engineering with MATLAB®
Introduction to Computational Engineering with MATLAB® aims to teach readers how to use MATLAB programming to solve numerical engineering problems. The book focuses on computational engineering with the objective of helping engineering students improve their numerical problem-solving skills. The book cuts a middle path between undergraduate texts that simply focus on programming and advanced mathematical texts that skip over foundational concepts, feature cryptic mathematical expressions, and do not provide sufficient support for novices. Although this book covers some advanced topics, readers do not need prior computer programming experience or an advanced mathematical background. Instead,...
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, user...
Exploring Operations Research with R
Exploring Operations Research with R shows how the R programming language can be a valuable tool – and way of thinking – which can be successfully applied to the field of operations research (OR). This approach is centred on the idea of the future OR professional as someone who can combine knowledge of key OR techniques (e.g., simulation, linear programming, data science, and network science) with an understanding of R, including tools for data representation, manipulation, and analysis. The core aim of the book is to provide a self-contained introduction to R (both Base R and the tidyverse) and show how this knowledge can be applied to a range of OR challenges in the domains of public h...
Applied Mathematics with Open-Source Software
Applied Mathematics with Open-source Software: Operational Research Problems with Python and R is aimed at a broad segment of readers who wish to learn how to use open-source software to solve problems in applied mathematics. The book has an innovative structure with 4 sections of two chapters covering a large range of applied mathematical techniques: probabilistic modelling, dynamical systems, emergent behaviour and optimisation. The pairs of chapters in each section demonstrate different families of solution approaches. Each chapter starts with a problem, gives an overview of the relevant theory, shows a solution approach in R and in Python, and finally gives wider context by including a n...
R for Business Analytics
R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages. With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. T...
Advances in Human Factors, Business Management and Society
This book presents practical approaches for facilitating the achievement of excellence in the management and leadership of organizational resources. It shows how the principles of creating shared value can be applied to ensure faster learning, training, business development, and social renewal. In particular, it presents novel methods and tools for tackling the complexity of management and learning in both business organizations and society. Discussing ontologies, intelligent management systems, methods for creating knowledge and value added, it offers novel insights into time management and operations optimization, as well as advanced methods for evaluating customers’ satisfaction and con...
