Get an introduction to functional data structures using R and write more effective code and gain performance for your programs. This book teaches you workarounds because data in functional languages is not mutable: for example you’ll learn how to change variable-value bindings by modifying environments, which can be exploited to emulate pointers and implement traditional data structures. You’ll also see how, by abandoning traditional data structures, you can manipulate structures by building new versions rather than modifying them. You’ll discover how these so-called functional data structures are different from the traditional data structures you might know, but are worth understanding to do serious algorithmic programming in a functional language such as R. By the end of Functional Data Structures in R, you’ll understand the choices to make in order to most effectively work with data structures when you cannot modify the data itself. These techniques are especially applicable for algorithmic development important in big data, finance, and other data science applications. What You'll Learn Carry out algorithmic programming in R Use abstract data structures Work with both immutable and persistent data Emulate pointers and implement traditional data structures in R Build new versions of traditional data structures that are known Who This Book Is For Experienced or advanced programmers with at least a comfort level with R. Some experience with data structures recommended.

Step into the world of advanced data analysis with 'R Data Analysis Projects.' In this hands-on guide, you will learn to build efficient analytics systems and pipelines using R for practical applications in finance, social media, and more. By following real-world projects, you'll enhance your data analysis skills, from implementing recommender systems to performing time-series modeling. What this Book will help me do Develop end-to-end data analysis and visualization solutions using R. Create scalable predictive analytics systems with actionable insights. Leverage RShiny to build interactive dashboards for effective communication. Master popular R packages like dplyr, ggplot2, and recommenderlab. Tackle real-world data challenges in varied domains such as finance and social networks. Author(s) Gopi Subramanian is an experienced data scientist and educator with an extensive background in statistical modeling and analytics. With years of hands-on practice and teaching, Gopi specializes in making complex concepts accessible through practical examples. His passion for R programming and real-world applications shines in his approachable style, making learning empowering and engaging. Who is it for? This book is designed for readers with a foundational understanding of R and data analysis, aiming to advance their skills to a professional level. Ideal for data analysts, R programmers, and aspiring data scientists seeking practical experience in building analytics systems. Whether you're transitioning to or deepening your expertise in R, this guide offers actionable knowledge to enhance your projects.

"Practical Data Wrangling" provides a comprehensive guide to cleaning and preparing data for analysis, focusing on techniques in Python and R. As you progress through the book, you'll learn how to handle various datasets, reshape their formats, and prepare them for insights, empowering you to derive more value from your data. What this Book will help me do Understand the data wrangling process and its importance in the data analysis pipeline. Learn how to retrieve, parse, and shape raw data into structured formats. Master packages and tools in Python and R to efficiently clean and manipulate data. Gain proficiency in using regular expressions for text data preparation. Acquire skills to analyze, merge, and transform datasets to meet analytics needs. Author(s) None Visochek has years of experience working with data and analytics, with expertise in using Python and R for solving real-world data challenges. Their teaching approach emphasizes practical examples and accessible explanations, ensuring complex concepts are easy to understand. Who is it for? This book is for data scientists, analysts, or statisticians who work with real-world data and want to optimize their data preparation process. It is ideal for professionals with basic knowledge of Python and R looking to enhance their skills in data wrangling and data preparation techniques. If you're seeking to streamline your data analysis workflow through better wrangling techniques, this book is for you.

The book takes an integrative approach to the subject of electromagnetics by supplementing quintessential "old school" information and methods with instruction in the use of new commercial software such as MATLAB.

The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing. • Features a quick-learning format with concise tutorials and actionable analytics • Provides command-by-command translations of R to Python and vice versa • Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages • Offers numerous comparative examples and applications in both programming languages • Designed for use for practitioners and students that know one language and want to learn the other • Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.

Machine Learning with R Cookbook, Second Edition, is your hands-on guide to applying machine learning principles using R. Through simple, actionable examples and detailed step-by-step recipes, this book will help you build predictive models, analyze data, and derive actionable insights. Explore core topics in data science, including regression, classification, clustering, and more. What this Book will help me do Apply the Apriori algorithm for association analysis to uncover relationships in transaction datasets. Effectively visualize data patterns and associations using a variety of plots and graphing methods. Master the application of regression techniques to address predictive modeling challenges. Leverage the power of R and Hadoop for performing big data machine learning efficiently. Conduct advanced analyses such as survival analysis and improve machine learning model performance. Author(s) Yu-Wei, Chiu (David Chiu), the author, is an experienced data scientist and R programmer who specializes in applying data science and machine learning principles to solve real-world problems. David's pragmatic and comprehensive teaching style provides readers with deep insights and practical methodologies for using R effectively in their projects. His passion for data science and expertise in R and big data make this book a reliable resource for learners. Who is it for? This book is ideal for data scientists, analysts, and professionals working with machine learning and R. It caters to intermediate users who are versed in the basics of R and want to deepen their skills. If you aim to become the go-to expert for machine learning challenges and enhance your efficiency and capability in machine learning projects, this book is for you.

The Pandas Cookbook offers a collection of practical recipes for mastering data manipulation, analysis, and visualization tasks using pandas. Through a methodological and hands-on approach, you will learn to utilize pandas for handling real-world datasets efficiently. By the end of this book, you will be able to solve complex data science problems and create insightful visual representations in Python. What this Book will help me do Understand the core functionalities of pandas 0.20 for exploring datasets effectively. Master filtering, selecting, and transforming data for targeted analysis. Leverage pandas' features for aggregating and transforming grouped data. Restructure data for analysis and create professional visualizations using integration with Seaborn and Matplotlib. Gain expertise in handling time series data and SQL-like merging operations. Author(s) Theodore Petrou, the author of the Pandas Cookbook, is a data scientist and Python expert with extensive experience teaching and using pandas in professional settings. Known for his practical approach, he meticulously explains each recipe and includes comprehensive examples and datasets in Jupyter notebooks to enhance your learning experience. Who is it for? This book is aimed at data scientists, Python developers, and analysts seeking an in-depth, practical guide to mastering data analysis with pandas. Whether you're a beginner with some knowledge of Python or an experienced analyst looking to refine your skills, this cookbook provides valuable insights and techniques for your data-driven tasks.

'Modern R Programming Cookbook' provides an array of practical recipes designed to enhance your R programming skills for statistical, data science, and graphical applications. With this book, you will deepen your understanding of R's syntax and semantics and be able to solve real-world problems using R effectively. What this Book will help me do Understand and configure R's development environment, including IDE setup and library management. Master R's core data structures and best practices for efficient data manipulation. Design and implement custom R functions, handling errors and utilizing recursion effectively. Streamline data processing tasks using modern R packages like dplyr for structured workflows. Efficiently process text and interact with databases to tackle a variety of data science projects. Author(s) Jaynal Abedin is an experienced data scientist and R programmer with a strong background in statistical modeling and data analysis. Over his career, Jaynal has worked on numerous data-intensive projects, helping individuals and organizations transform data insights into actionable strategies. He brings a clear and hands-on approach to teaching and writes with the goal of empowering readers to tackle complex problems using R. Who is it for? This book is perfect for developers looking to enhance their R programming expertise, particularly if they already have a basic understanding of R's fundamentals. Whether you're working in data science, statistics, or software development, this book provides actionable strategies for leveraging R's capabilities. If you're exploring advanced R programming or aiming to upskill in data applications, this book is an excellent resource.

R Data Analysis Cookbook, Second Edition, is your companion for mastering various data analysis techniques using R. Combining powerful R libraries like ggplot2, this book guides you through tasks such as data mining, visualization, and even advanced concepts like time series analysis. Whether you're cleaning and formatting data or generating actionable insights, this book offers hands-on recipes to upskill efficiently. What this Book will help me do Learn how to acquire, prepare, and visualize datasets using popular R libraries. Master exploratory data analysis concepts to discover insights in any data. Get introduced to machine learning algorithms in R such as regression and classification. Develop advanced skills like social network analysis, recommendation systems, and geospatial analysis. Learn to generate dynamic reports and interactive dashboards using tools like Shiny. Author(s) Kuntal Ganguly and Viswa Viswanathan bring a wealth of experience in data science, analytics, and R programming to this book. Their passion for teaching complex topics with clarity and practical insight shines throughout. The co-authors incorporate years of technical expertise to guide readers step-by-step in solving real-world data analysis challenges. Who is it for? This book is ideal for data scientists, analysts, or enthusiasts taking their first or next steps with R. It caters to professionals with a beginner or intermediate understanding of statistics and programming who are seeking practical examples and solutions to complex data tasks. If you want actionable knowledge to address real-world analytics challenges, this book is for you.

This book, 'Statistical Application Development with R and Python', is your gateway to mastering statistical analysis and applying it effectively in real-world contexts. Through integrated R and Python code, you'll learn how to utilize data processing, explore advanced statistical models like regression and CART, and develop applications that solve complex analytical challenges. What this Book will help me do Fully understand data visualization and exploratory analysis methods to uncover insights from datasets. Master techniques such as regression models, clustering, and classification to enhance your analytical toolkit. Gain proficiency in R and Python for data processing and statistical modeling tasks. Apply CART and other machine learning tools to tackle nonlinear data challenges effectively. Equip yourself with a comprehensive approach to data exploration and decision-making for impactful results. Author(s) The author(s) of this book bring extensive experience in statistical analysis, computational modeling, and the use of R and Python for data science. They are professionals and educators passionate about making statistics accessible and practical. Their engaging writing style ensures readers not only understand but also enjoy the journey of learning statistics. Who is it for? This book is perfect for aspiring data scientists or professionals wanting to deepen their understanding of statistical analysis. Whether you're new to R or Python or looking to integrate both into your workflow, this guide provides comprehensive knowledge and practical techniques. It's suitable for beginners with no prior experience as well as seasoned users seeking to enhance their data processing and modeling skills.

Learn the art of creating machine learning models and processing data efficiently with MATLAB. In this book, you will explore various techniques such as regression analysis, clustering, classification, and neural networks, all in the MATLAB environment. Each topic is detailed with practical examples for clear understanding and immediate application. What this Book will help me do Understand the key concepts of machine learning and how they integrate with MATLAB. Learn to preprocess and transform data for effective machine learning workflows. Explore regression methods and apply them to analyze and predict trends in your data. Master classification and clustering techniques for model creation and data categorization. Gain expertise in using MATLAB Neural Network Toolbox for building neural network-based solutions. Author(s) None Kolluru and Giuseppe Ciaburro are seasoned experts in using MATLAB for data analysis and machine learning. With years of experience in research and teaching, they have meticulously curated this book to bridge concepts of theory with real-world applications. Their writing approach is clear, instructional, and focused on equipping learners with practical skills. Who is it for? This book is ideal for data analysts, aspiring data scientists, and students eager to delve into machine learning using MATLAB. Even if you're new to the field, you'll find the instructions gentle yet comprehensive to help you follow along. However, having some background in math and statistics will definitely enhance your learning experience. If you're passionate about data and its insights, this is the guide for you.

In "Advanced Analytics with R and Tableau," you will learn how to combine the statistical computing power of R with the excellent data visualization capabilities of Tableau to perform advanced analysis and present your findings effectively. This book guides you through practical examples to understand topics such as classification, clustering, and predictive analytics while creating compelling visual dashboards. What this Book will help me do Integrate advanced statistical computations in R with Tableau's visual analysis for comprehensive analytics. Master making R function calls from Tableau through practical applications such as RServe integration. Develop predictive and classification models in R, visualized wonderfully in Tableau dashboards. Understand clustering and unsupervised learning concepts, applied to real-world datasets for business insights. Leverage the combination of Tableau and R for making impactful, data-driven decisions in your organization. Author(s) Ruben Oliva Ramos, Jen Stirrup, and Roberto Rösler are accomplished professionals with extensive experience in data science and analytics. Their combined expertise brings practical insights into combining R and Tableau for advanced analytics. Advocates for hands-on learning, they emphasize clarity and actionable knowledge in their writing. Who is it for? "Advanced Analytics with R and Tableau" is ideal for business analysts, data scientists, and Tableau professionals eager to expand their capabilities into advanced analytics. Readers should be familiar with Tableau and have basic knowledge of R, though the book starts with accessible examples. If you're looking to enhance your analytics with R's statistical power seamlessly integrated into Tableau, this book is for you.

This comprehensive guide dives into predictive analytics with R, exploring the powerful functionality and vast ecosystem of packages available in this programming language. By studying this book, you will gain mastery over predictive modeling techniques and learn how to apply machine learning to real-world problems efficiently and effectively. What this Book will help me do Develop proficiency in predictive modeling processes, from data preparation to model evaluation. Gain hands-on experience with R's diverse packages for machine learning. Understand the theoretical foundations and practical applications of various predictive models. Learn advanced techniques such as deep learning implementations of word embeddings and recurrent neural networks. Acquire the ability to handle large datasets using R for scalable predictive analytics workflows. Author(s) James D. Miller and Rui Miguel Forte are experts in data science and predictive analytics with decades of combined experience in the field. They bring practical insights from their work in both academia and industry. Their clear and engaging writing style aims at making complex concepts accessible to readers by integrating theoretical knowledge with real-world applications. Who is it for? This book is ideal for budding data scientists, predictive modelers, or quantitative analysts with some basic knowledge of R and statistics. Advanced learners aiming to refine their expertise in predictive analytics and those wishing to explore the functionality of R for applied machine learning will also greatly benefit from this resource. The book is suitable for professionals and enthusiasts keen to expand their understanding of predictive modeling and learn advanced techniques.

Welcome to Scientific Python and its community. If you’re a scientist who programs with Python, this practical guide not only teaches you the fundamental parts of SciPy and libraries related to it, but also gives you a taste for beautiful, easy-to-read code that you can use in practice. You’ll learn how to write elegant code that’s clear, concise, and efficient at executing the task at hand. Throughout the book, you’ll work with examples from the wider scientific Python ecosystem, using code that illustrates principles outlined in the book. Using actual scientific data, you’ll work on real-world problems with SciPy, NumPy, Pandas, scikit-image, and other Python libraries. Explore the NumPy array, the data structure that underlies numerical scientific computation Use quantile normalization to ensure that measurements fit a specific distribution Represent separate regions in an image with a Region Adjacency Graph Convert temporal or spatial data into frequency domain data with the Fast Fourier Transform Solve sparse matrix problems, including image segmentations, with SciPy’s sparse module Perform linear algebra by using SciPy packages Explore image alignment (registration) with SciPy’s optimize module Process large datasets with Python data streaming primitives and the Toolz library

Suitable for both beginners and advanced users, this popular book makes writing statistical reports easier by integrating computing directly with reporting. Reports range from homework, projects, exams, books, blogs, and web pages to virtually any documents related to statistical graphics, computing, and data analysis. This edition includes a new chapter on R Markdown v2, changes that reflect improvements in the knitr package, and several new sections. Demos and other information about the package are available on the author’s website.

Take your Python skills to the next level with 'Learning pandas,' your go-to guide for mastering data manipulation and analysis. This book walks you through the powerful tools offered by the pandas library, helping you unlock key insights from data efficiently. Whether you're handling time-series data or visualizing patterns, you'll gain the proficiency needed to make sense of complex datasets. What this Book will help me do Understand and effectively use pandas Series and DataFrame objects for data representation and manipulation. Master indexing, slicing, and combining data to perform detailed exploration and analysis. Learn to access and work with external data sources, including APIs, databases, and files, using pandas. Develop the skills to handle and analyze time-series data, managing its unique challenges. Create informative and professional data visualizations directly using pandas capabilities. Author(s) Michael Heydt is a respected author and educator in the field of Python and data analysis. With years of experience utilizing pandas in practical and professional environments, Michael offers a unique perspective that combines deep technical insight with approachable examples. His teaching philosophy emphasizes clarity, applicability, and engaging instruction, ensuring learners easily acquire valuable skills. Who is it for? This book is ideal for Python programmers looking to enhance their data analysis capabilities, as well as data analysts and scientists wanting to leverage pandas to improve their workflows. Readers are recommended to have some familiarity with Python, though prior experience with pandas is not required. If you have a keen interest in data exploration and quantitative techniques, this book is for you.

Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media. Learn how to apply the tidy text format to NLP Use sentiment analysis to mine the emotional content of text Identify a document’s most important terms with frequency measurements Explore relationships and connections between words with the ggraph and widyr packages Convert back and forth between R’s tidy and non-tidy text formats Use topic modeling to classify document collections into natural groups Examine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages

Learn how to write object-oriented programs in R and how to construct classes and class hierarchies in the three object-oriented systems available in R. This book gives an introduction to object-oriented programming in the R programming language and shows you how to use and apply R in an object-oriented manner. You will then be able to use this powerful programming style in your own statistical programming projects to write flexible and extendable software. After reading Advanced Object-Oriented Programming in R, you'll come away with a practical project that you can reuse in your own analytics coding endeavors. You'll then be able to visualize your data as objects that have state and then manipulate those objects with polymorphic or generic methods. Your projects will benefit from the high degree of flexibility provided by polymorphism, where the choice of concrete method to execute depends on the type of data being manipulated. What You'll Learn Define and use classes and generic functions using R Work with the R class hierarchies Benefit from implementation reuse Handle operator overloading Apply the S4 and R6 classes Who This Book Is For Experienced programmers and for those with at least some prior experience with R programming language.

Create data mining algorithms About This Book Develop a strong strategy to solve predictive modeling problems using the most popular data mining algorithms Real-world case studies will take you from novice to intermediate to apply data mining techniques Deploy cutting-edge sentiment analysis techniques to real-world social media data using R Who This Book Is For This Learning Path is for R developers who are looking to making a career in data analysis or data mining. Those who come across data mining problems of different complexities from web, text, numerical, political, and social media domains will find all information in this single learning path. What You Will Learn Discover how to manipulate data in R Get to know top classification algorithms written in R Explore solutions written in R based on R Hadoop projects Apply data management skills in handling large data sets Acquire knowledge about neural network concepts and their applications in data mining Create predictive models for classification, prediction, and recommendation Use various libraries on R CRAN for data mining Discover more about data potential, the pitfalls, and inferencial gotchas Gain an insight into the concepts of supervised and unsupervised learning Delve into exploratory data analysis Understand the minute details of sentiment analysis In Detail Data mining is the first step to understanding data and making sense of heaps of data. Properly mined data forms the basis of all data analysis and computing performed on it. This learning path will take you from the very basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining. You will learn how to manipulate data with R using code snippets and how to mine frequent patterns, association, and correlation while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on R Hadoop projects. Now that you are comfortable with data mining with R, you will move on to implementing your knowledge with the help of end-to-end data mining projects. You will learn how to apply different mining concepts to various statistical and data applications in a wide range of fields. At this stage, you will be able to complete complex data mining cases and handle any issues you might encounter during projects. After this, you will gain hands-on experience of generating insights from social media data. You will get detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Learning Data Mining with R by Bater Makhabel R Data Mining Blueprints by Pradeepta Mishra Social Media Mining with R by Nathan Danneman and Richard Heimann Style and approach A complete package with which will take you from the basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining. Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.

Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You'll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. What You'll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers Who This Book Is For Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.