This handy reference book detailing the intricacies of R updates the popular first edition by adding R version 3.4 and 3.5 features. Starting with the basic structure of R, the book takes you on a journey through the terminology used in R and the syntax required to make R work. You will find looking up the correct form for an expression quick and easy. Some of the new material includes information on RStudio, S4 syntax, working with character strings, and an example using the Twitter API. With a copy of the R Quick Syntax Reference in hand, you will find that you are able to use the multitude of functions available in R and are even able to write your own functions to explore and analyze data. What You Will Learn Discover the modes and classes of R objects and how to use them Use both packaged and user-created functions in R Import/export data and create new data objects in R Create descriptive functions and manipulate objects in R Take advantage of flow control and conditional statements Work with packages such as base, stats, and graphics Who This Book Is For Those with programming experience, either new to R, or those with at least some exposure to R but who are new to the latest version.

Machine Learning with R Quick Start Guide takes you through the foundations of machine learning using the R programming language. Starting with the basics, this book introduces key algorithms and methodologies, offering hands-on examples and applicable machine learning solutions that allow you to extract insights and create predictive models. What this Book will help me do Understand the basics of machine learning and apply them using R 3.5. Learn to clean, prepare, and visualize data with R to ensure robust data analysis. Develop and work with predictive models using various machine learning techniques. Discover advanced topics like Natural Language Processing and neural network training. Implement end-to-end pipeline solutions, from data collection to predictive analytics, in R. Author(s) None Sanz, the author of Machine Learning with R Quick Start Guide, is an expert in data science with years of experience in the field of machine learning and R programming. Known for their accessible and detailed teaching style, the author focuses on providing practical knowledge to empower readers in the real world. Who is it for? This book is ideal for graduate students and professionals, including aspiring data scientists and data analysts, looking to start their journey in machine learning. Readers are expected to have some familiarity with the R programming language but no prior machine learning experience is necessary. With this book, the audience will gain the ability to confidently navigate machine learning concepts and practices.

The "R Statistics Cookbook" offers a comprehensive guide to solving statistical problems using R 3.5. Through over 100 practical recipes, you'll learn to perform essential statistical analyses, such as t-tests and regression, while mastering techniques for data modeling, nonparametric methods, and machine learning. This resource is tailored for tackling statistics-centric challenges across industries. What this Book will help me do Confidently use R 3.5 to perform statistical analyses that meet your data needs. Apply various hypothesis testing methods, such as t-tests and ANOVA, effectively. Model and forecast data using time series analysis and mixed-effects modeling. Implement regression techniques, including Bayesian regression, for actionable insights. Leverage robust statistics and the caret package for machine learning applications in R. Author(s) None Juretig, a professional statistician and experienced educator, has an extensive background in applying statistical methods to real-world problems using R. Their writing combines deep technical knowledge with an approachable teaching style, making complex statistical concepts accessible to learners of varying levels. Who is it for? If you're a statistician, data scientist, researcher, or analyst with proficiency in R programming and foundational knowledge of linear algebra, this book is crafted for you. It caters to professionals looking to solidify their statistical knowledge while exploring practical, real-world applications. Whether seeking to apply advanced methods or refine your statistical approaches, this guide provides actionable insights.

This book provides a comprehensive study in digital image interpolation with theoretical, analytical and Matlab® implementation. It includes all historically and practically important interpolation algorithms, accompanied with Matlab® source code on a website, which will assist readers to learn and understand the implementation details of each presented interpolation algorithm. Furthermore, sections in fundamental signal processing theories and image quality models are also included. The authors intend for the book to help readers develop a thorough consideration of the design of image interpolation algorithms and applications for their future research in the field of digital image processing. Introduces a wide range of traditional and advanced image interpolation methods concisely and provides thorough treatment of theoretical foundations Discusses in detail the assumptions and limitations of presented algorithms Investigates a variety of interpolation and implementation methods including transform domain, edge-directed, wavelet and scale-space, and fractal based methods Features simulation results for comparative analysis, summaries and computational and analytical exercises at the end of each chapter Digital Image Interpolation in Matlab® is an excellent guide for researchers and engineers working in digital imaging and digital video technologies. Graduate students studying digital image processing will also benefit from this practical reference text.

Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study. Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You’ll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language. What You’ll Learn Conduct advanced analyses in R including: generalized linear models, generalized additive models, mixedeffects models, machine learning, and parallel processing Carry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysis Handle machine learning using R including parallel processing, dimension reduction, and feature selection and classification Address missing data using multiple imputation in R Work on factor analysis, generalized linear mixed models, and modeling intraindividual variability Who This Book Is For Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are givenproven code to reduce time to result(s).

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. What you'll learn: How to write code for machine learning, adaptive control and estimation using MATLAB How these three areas complement each other How these three areas are needed for robust machine learning applications How to use MATLAB graphics and visualization tools for machine learning How to code real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers, data scientists and students wanting a comprehensive and code cookbook rich in examples on machine learning using MATLAB.

Beyond Spreadsheets with R shows you how to take raw data and transform it for use in computations, tables, graphs, and more. You’ll build on simple programming techniques like loops and conditionals to create your own custom functions. You’ll come away with a toolkit of strategies for analyzing and visualizing data of all sorts using R and RStudio. About the Technology Spreadsheets are powerful tools for many tasks, but if you need to interpret, interrogate, and present data, they can feel like the wrong tools for the task. That’s when R programming is the way to go. The R programming language provides a comfortable environment to properly handle all types of data. And within the open source RStudio development suite, you have at your fingertips easy-to-use ways to simplify complex manipulations and create reproducible processes for analysis and reporting. About the Book With Beyond Spreadsheets with R you’ll learn how to go from raw data to meaningful insights using R and RStudio. Each carefully crafted chapter covers a unique way to wrangle data, from understanding individual values to interacting with complex collections of data, including data you scrape from the web. You’ll build on simple programming techniques like loops and conditionals to create your own custom functions. You’ll come away with a toolkit of strategies for analyzing and visualizing data of all sorts. What's Inside How to start programming with R and RStudio Understanding and implementing important R structures and operators Installing and working with R packages Tidying, refining, and plotting your data About the Reader If you’re comfortable writing formulas in Excel, you’re ready for this book. About the Author Dr Jonathan Carroll is a data science consultant providing R programming services. He holds a PhD in theoretical physics. We interviewed Jonathan as a part of our Six Questions series. Check it out here. Quotes A useful guide to facilitate graduating from spreadsheets to more serious data wrangling with R. - John D. Lewis, DDN An excellent book to help you understand how stored data can be used. - Hilde Van Gysel, Trebol Engineering A great introduction to a data science programming language. Makes you want to learn more! - Jenice Tom, CVS Health Handy to have when your data spreads beyond a spreadsheet. - Danil Mironov, Luxoft Poland

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. What You'll Learn Work with vectors and matrices using NumPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Review statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its related ecosystem for numerical computing.

This graduate text covers a variety of mathematical and statistical tools for the analysis of big data coming from biology, medicine and economics. Neural networks, Markov chains, tools from statistical physics and wavelet analysis are used to develop efficient computational algorithms, which are then used for the processing of real-life data using Matlab.

This third edition of Paul Murrell’s classic book on using R for graphics represents a major update, with a complete overhaul in focus and scope. It focuses primarily on the two core graphics packages in R - graphics and grid - and has a new section on integrating graphics.

Extensively updated to reflect the evolution of statistics and computing, the second edition of the bestselling R Graphics comes complete with new packages and new examples. Paul Murrell, widely known as the leading expert on R graphics, has developed an in-depth resource that helps both neophyte and seasoned users master the intricacies of R graph

This O’Reilly cookbook provides more than 150 recipes to help scientists, engineers, programmers, and data analysts generate high-quality graphs quickly—without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project and includes a discussion of how and why the recipe works. Most of the recipes in this second edition use the updated version of the ggplot2 package, a powerful and flexible way to make graphs in R. You’ll also find expanded content about the visual design of graphics. If you have at least a basic understanding of the R language, you’re ready to get started with this easy-to-use reference. Use R’s default graphics for quick exploration of data Create a variety of bar graphs, line graphs, and scatter plots Summarize data distributions with histograms, density curves, box plots, and more Provide annotations to help viewers interpret data Control the overall appearance of graphics Explore options for using colors in plots Create network graphs, heat maps, and 3D scatter plots Get your data into shape using packages from the tidyverse

An essential guide to two burgeoning topics in machine learning – classification trees and ensemble learning Ensemble Classification Methods with Applications in R introduces the concepts and principles of ensemble classifiers methods and includes a review of the most commonly used techniques. This important resource shows how ensemble classification has become an extension of the individual classifiers. The text puts the emphasis on two areas of machine learning: classification trees and ensemble learning. The authors explore ensemble classification methods’ basic characteristics and explain the types of problems that can emerge in its application. Written by a team of noted experts in the field, the text is divided into two main sections. The first section outlines the theoretical underpinnings of the topic and the second section is designed to include examples of practical applications. The book contains a wealth of illustrative cases of business failure prediction, zoology, ecology and others. This vital guide: Offers an important text that has been tested both in the classroom and at tutorials at conferences Contains authoritative information written by leading experts in the field Presents a comprehensive text that can be applied to courses in machine learning, data mining and artificial intelligence Combines in one volume two of the most intriguing topics in machine learning: ensemble learning and classification trees Written for researchers from many fields such as biostatistics, economics, environment, zoology, as well as students of data mining and machine learning, Ensemble Classification Methods with Applications in R puts the focus on two topics in machine learning: classification trees and ensemble learning.

Discover the basic telecommunications systems principles in an accessible learn-by-doing format Communication Systems Principles Using MATLAB covers a variety of systems principles in telecommunications in an accessible format without the need to master a large body of theory. The text puts the focus on topics such as radio and wireless modulation, reception and transmission, wired networks and fiber optic communications. The book also explores packet networks and TCP/IP as well as digital source and channel coding, and the fundamentals of data encryption. Since MATLAB® is widely used by telecommunications engineers, it was chosen as the vehicle to demonstrate many of the basic ideas, with code examples presented in every chapter. The text addresses digital communications with coverage of packet-switched networks. Many fundamental concepts such as routing via shortest-path are introduced with simple and concrete examples. The treatment of advanced telecommunications topics extends to OFDM for wireless modulation, and public-key exchange algorithms for data encryption. Throughout the book, the author puts the emphasis on understanding rather than memorization. The text also: Includes many useful take-home skills that can be honed while studying each aspect of telecommunications Offers a coding and experimentation approach with many real-world examples provided Gives information on the underlying theory in order to better understand conceptual developments Suggests a valuable learn-by-doing approach to the topic Written for students of telecommunications engineering, Communication Systems Principles Using MATLAB® is the hands-on resource for mastering the basic concepts of telecommunications in a learn-by-doing format.

Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This revision is fully updated with new content on social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulation Author Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you've learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Second Edition is an invaluable reference with its examples of storing, accessing, and analyzing data. What You'll Learn Understand the core concepts of data analysis and the Python ecosystem Go in depth with pandas for reading, writing, and processing data Use tools and techniques for data visualization and image analysis Examine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorch Who This Book Is For Experienced Python developers who need to learn about Pythonic tools for data analysis

Master the essentials of programming with R and streamline your data analysis workflow with 'R Programming Fundamentals'. This book introduces key R concepts like data structures and control flow, and guides you through practical applications such as data visualization with ggplot2. By the end, you will progress to completing a full data science project for practical hands-on experience. What this Book will help me do Learn to use R's core features, including package management, data structures, and control flow. Process and clean datasets effectively within R, handling missing values and variable transformation. Master data visualization techniques with ggplot2 to create insightful plots and charts. Develop skills to import diverse datasets such as CSVs, Excel spreadsheets, and SQL databases into R. Construct a data science project end-to-end, applying skills in analysis, visualization, and reporting. Author(s) Kaelen Medeiros is a dedicated teacher with a passion for making complex concepts accessible. Bringing years of experience in data science and statistical computing, Kaelen excels at helping learners understand and leverage R for their data analysis needs. With a focus on practical learning, Kaelen has designed this book to give you the hands-on experience and foundational knowledge you need. Who is it for? This book is perfect for analysts looking to enhance their data science toolkit by learning R. It's especially suited for those with little R programming experience looking to start with foundational concepts. Whether you're an aspiring data scientist or a seasoned professional seeking a refresher, this book offers a structured approach to mastering R effectively.

This book focuses on why one draws graphics to display data and which graphics to draw (and uses R to do so). Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. All the datasets are available in R or one of its packages and the R code is available online. Color graphics are used throughout the book.

This book is designed for users who already know R or MATLAB® and now need to learn the other platform. The author covers essential tasks, such as working with matrices and vectors, writing functions and other programming concepts, graphics, numerical computing, and file input/output. He highlights important differences between the two platforms and explores common mistakes that are easy to make when transitioning from one platform to the other.

This practical guide shows how to use R and its add-on packages to obtain numerical solutions to complex mathematical problems commonly faced by scientists and engineers. Providing worked examples and code, the text not only addresses necessary aspects of the R programming language but also demonstrates how to produce useful graphs and statistically analyze and fit data to linear and nonlinear models. It covers Monte Carlo, stochastic, and deterministic methods and explores topics such as numerical differentiation and integration, interpolation and curve fitting, and optimization.

Introduces the basic concepts of FEM in an easy-to-use format so that students and professionals can use the method efficiently and interpret results properly Finite element method (FEM) is a powerful tool for solving engineering problems both in solid structural mechanics and fluid mechanics. This book presents all of the theoretical aspects of FEM that students of engineering will need. It eliminates overlong math equations in favour of basic concepts, and reviews of the mathematics and mechanics of materials in order to illustrate the concepts of FEM. It introduces these concepts by including examples using six different commercial programs online. The all-new, second edition of Introduction to Finite Element Analysis and Design provides many more exercise problems than the first edition. It includes a significant amount of material in modelling issues by using several practical examples from engineering applications. The book features new coverage of buckling of beams and frames and extends heat transfer analyses from 1D (in the previous edition) to 2D. It also covers 3D solid element and its application, as well as 2D. Additionally, readers will find an increase in coverage of finite element analysis of dynamic problems. There is also a companion website with examples that are concurrent with the most recent version of the commercial programs. Offers elaborate explanations of basic finite element procedures Delivers clear explanations of the capabilities and limitations of finite element analysis Includes application examples and tutorials for commercial finite element software, such as MATLAB, ANSYS, ABAQUS and NASTRAN Provides numerous examples and exercise problems Comes with a complete solution manual and results of several engineering design projects Introduction to Finite Element Analysis and Design, 2nd Edition is an excellent text for junior and senior level undergraduate students and beginning graduate students in mechanical, civil, aerospace, biomedical engineering, industrial engineering and engineering mechanics.

Dive into 'Hands-On Data Analysis with NumPy and pandas' to explore the world of Python for data analysis. This book guides you through using these powerful Python libraries to handle and manipulate data efficiently. You will learn hands-on techniques to read, sort, group, and visualize data for impactful analysis. What this Book will help me do Learn to set up a Python environment for data analysis with tools like Jupyter notebooks. Master data handling using NumPy, focusing on array creation, slicing, and operations. Understand the functionalities of pandas for managing datasets, including DataFrame operations. Discover techniques for data preparation, such as handling missing data and hierarchical indexing. Explore data visualization using pandas and create impactful plots for data insights. Author(s) The book is authored by None Miller, a seasoned Python developer and data analyst. With a strong background in leveraging Python for data processing, None focuses on creating content that is practical and accessible. The author's teaching approach emphasizes hands-on practice and understanding, making technical topics approachable and engaging. Who is it for? This book is ideal for Python developers at a beginner to intermediate level looking to venture into data analysis. If you are transitioning from general programming to data-focused work or need to enhance your skills in data manipulation and processing, this book will be a strong foundation. It requires no prior experience with data analysis, so it is accessible to many learners.

"Mastering Numerical Computing with NumPy" is a comprehensive guide to becoming proficient in numerical computing using Python's NumPy library. This book will teach you how to perform advanced numerical operations, explore data statistically, and build predictive models effectively. By mastering the provided concepts and exercises, you'll be empowered in your scientific computing projects. What this Book will help me do Perform and optimize vector and matrix operations effectively using NumPy. Analyze data using exploratory data analysis techniques and predictive modeling. Implement unsupervised learning algorithms such as clustering with relevant datasets. Understand advanced benchmarks and select optimal configurations for performance. Write efficient and scalable programs utilizing advanced NumPy features. Author(s) The authors of "Mastering Numerical Computing with NumPy" include domain experts and educators with years of experience in Python programming, numerical computing, and data science. They bring a practical and detailed approach to teaching advanced topics and guide you through every step of mastering NumPy. Who is it for? This book is ideal for Python programmers, data analysts, and data science enthusiasts who aim to deepen their understanding of numerical computing. If you have basic mathematics skills and want to utilize NumPy to solve complex data problems, this book is an excellent resource. Whether you're a beginner or an intermediate user, you will find this content approachable and enriching. Advanced users will benefit from the highly specialized content and real-world examples.

Gain an accelerated introduction to domain-specific languages in R, including coverage of regular expressions. This compact, in-depth book shows you how DSLs are programming languages specialized for a particular purpose, as opposed to general purpose programming languages. Along the way, you’ll learn to specify tasks you want to do in a precise way and achieve programming goals within a domain-specific context. Domain-Specific Languages in R includes examples of DSLs including large data sets or matrix multiplication; pattern matching DSLs for application in computer vision; and DSLs for continuous time Markov chains and their applications in data science. After reading and using this book, you’ll understand how to write DSLs in R and have skills you can extrapolate to other programming languages. What You'll Learn Program with domain-specific languages using R Discover the components of DSLs Carry out large matrix expressions and multiplications Implement metaprogramming with DSLs Parse and manipulate expressions Who This Book Is For Those with prior programming experience. R knowledge is helpful but not required.

Python and R are two of the mainstream languages in data science. Fundamentally, Python is a language for programmers, whereas R is a language for statisticians. In a data science context, there is a significant degree of overlap when it comes to the capabilities of each language in the fields of regression analysis and machine learning. Your choice of language will depend highly on the environment in which you are operating. In a production environment, Python integrates with other languages much more seamlessly and is therefore the modus operandi in this context. However, R is much more common in research environments due to its more extensive selection of libraries for statistical analysis.

Graduate from Excel to MATLAB® to keep up with the evolution of finance data Foundations of Computational Finance with MATLAB® is an introductory text for both finance professionals looking to branch out from the spreadsheet, and for programmers who wish to learn more about finance. As financial data grows in volume and complexity, its very nature has changed to the extent that traditional financial calculators and spreadsheet programs are simply no longer enough. Today’s analysts need more powerful data solutions with more customization and visualization capabilities, and MATLAB provides all of this and more in an easy-to-learn skillset. This book walks you through the basics, and then shows you how to stretch your new skills to create customized solutions. Part I demonstrates MATLAB’s capabilities as they apply to traditional finance concepts, and PART II shows you how to create interactive and reusable code, link with external data sources, communicate graphically, and more. Master MATLAB’s basic operations including matrices, arrays, and flexible data structures Learn how to build your own customized solutions when the built-ins just won’t do Learn how to handle financial data and industry-specific variables including risk and uncertainty Adopt more accurate modeling practices for portfolios, options, time series, and more MATLAB is an integrated development environment that includes everything you need in one well-designed user interface. Available Toolboxes provide tested algorithms that save you hours of code, and the skills you learn using MATLAB make it easier to learn additional languages if you choose to do so. Financial firms are catching up to universities in MATLAB usage, so this is skill set that will follow you throughout your career. When you’re ready to step into the new age of finance, Foundations of Computational Finance with MATLAB provides the expert instruction you need to get started quickly.