Gain better insight into your data using the power of R While R is very flexible and powerful, it is unlike most of the computer programs you have used. In order to unlock its full potential, this book delves into the language, making it accessible so you can tackle even the most complex of data analysis tasks. Simple data examples are integrated throughout so you can explore the capabilities and versatility of R. Along the way, you'll also learn how to carry out a range of commonly used statistical methods, including Analysis of Variance and Linear Regression. By the end, you'll be able to effectively and efficiently analyze your data and present the results. Beginning R: \n Discusses how to implement some basic statistical methods such as the t-test, correlation, and tests of association Explains how to turn your graphs from merely adequate to simply stunning Provides you with the ability to define complex analytical situations Demonstrates ways to make and rearrange your data for easier analysis Covers how to carry out basic regression as well as complex model building and curvilinear regression Shows how to produce customized functions and simple scripts that can automate your workflow \nwrox.com Programmer Forums Join our Programmer to Programmer forums to ask and answer programming questions about this book, join discussions on the hottest topics in the industry, and connect with fellow programmers from around the world. Code Downloads Take advantage of free code samples from this book, as well as code samples from hundreds of other books, all ready to use. Read More Find articles, ebooks, sample chapters and tables of contents for hundreds of books, and more reference resources on programming topics that matter to you. Wrox Beginning guides are crafted to make learning programming languages and technologies easier than you think, providing a structured, tutorial format that guides you through all the techniques involved. Visit the Beginning R website at www.wrox.com/go/beginningr

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Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment management that explores the three major R/finance coding paradigms, emphasizes data visualization, and explains how to build a cohesive suite of functioning Shiny applications. The full source code, asset price data and live Shiny applications are available at reproduciblefinance.com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple, yet practical real-world examples. The book begins with the first step in data science: importing and wrangling data, which in the investment context means importing asset prices, converting to returns, and constructing a portfolio. The next section covers risk and tackles descriptive statistics such as standard deviation, skewness, kurtosis, and their rolling histories. The third section focuses on portfolio theory, analyzing the Sharpe Ratio, CAPM, and Fama French models. The book concludes with applications for finding individual asset contribution to risk and for running Monte Carlo simulations. For each of these tasks, the three major coding paradigms are explored and the work is wrapped into interactive Shiny dashboards.

An essential library of basic commands you can copy and paste into R The powerful and open-source statistical programming language R is rapidly growing in popularity, but it requires that you type in commands at the keyboard rather than use a mouse, so you have to learn the language of R. But there is a shortcut, and that's where this unique book comes in. A companion book to Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, this practical reference is a library of basic R commands that you can copy and paste into R to perform many types of statistical analyses. Whether you're in technology, science, medicine, business, or engineering, you can quickly turn to your topic in this handy book and find the commands you need. \nComprehensive command reference for the R programming language and a companion book to Visualize This: The FlowingData Guide to Design, Visualization, and Statistics\n Combines elements of a dictionary, glossary, and thesaurus for the R language Provides easy accessibility to the commands you need, by topic, which you can cut and paste into R as needed Covers getting, saving, examining, and manipulating data; statistical test and math; and all the things you can do with graphs Also includes a collection of utilities that you'll find useful \nSimplify the complex statistical R programming language with The Essential R Reference. .

This practical guide provides more than 150 recipes to help you 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.\nMost of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you’re ready to get started.\n\nUse R’s default graphics for quick exploration of data\nCreate a variety of bar graphs, line graphs, and scatter plots\nSummarize data distributions with histograms, density curves, box plots, and other examples\nProvide annotations to help viewers interpret data\nControl the overall appearance of graphics\nRender data groups alongside each other for easy comparison\nUse colors in plots\nCreate network graphs, heat maps, and 3D scatter plots\nStructure data for graphing\n

Solve challenging data science problems by mastering cutting-edge machine learning techniques in PythonAbout This Book\nResolve complex machine learning problems and explore deep learning\nLearn to use Python code for implementing a range of machine learning algorithms and techniques\nA practical tutorial that tackles real-world computing problems through a rigorous and effective approach\nWho This Book Is ForThis title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you’ve ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you!\nPrior experience of Python and grounding in some of the core concepts of machine learning would be helpful.What You Will Learn\nCompete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms\nApply your new found skills to solve real problems, through clearly-explained code for every technique and test\nAutomate large sets of complex data and overcome time-consuming practical challenges\nImprove the accuracy of models and your existing input data using powerful feature engineering techniques\nUse multiple learning techniques together to improve the consistency of results\nUnderstand the hidden structure of datasets using a range of unsupervised techniques\nGain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach\nImprove the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together\nIn DetailDesigned to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data.\nThe machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce.\nThis book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano.\nBy this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering.Style and approachThis book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.

Get more from your data through creating practical machine learning systems with Python About This Book\nBuild your own Python-based machine learning systems tailored to solve any problem\nDiscover how Python offers a multiple context solution for create machine learning systems\nPractical scenarios using the key Python machine learning libraries to successfully implement in your projects\nWho This Book Is ForThis book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems.What You Will Learn\nBuild a classification system that can be applied to text, images, or sounds Use NumPy, SciPy, scikit-learn a“ scientific Python open source libraries for scientific computing and machine learning Explore the mahotas library for image processing and computer vision Build a topic model for the whole of Wikipedia Employ Amazon Web Services to run analysis on the cloud Debug machine learning problems Get to grips with recommendations using basket analysis Recommend products to users based on past purchases In DetailUsing machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python is a wonderful language to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation. With its excellent collection of open source machine learning libraries you can focus on the task at hand while being able to quickly try out many ideas.\nThis book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and introducing libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling, creating recommendation systems. Later on, the book covers advanced topics such as topic modeling, basket analysis, and cloud computing. These will extend your abilities and enable you to create large complex systems.\nWith this book, you gain the tools and understanding required to build your own systems, tailored to solve your real-world data analysis problems.

This Fascinating Book Demonstrates How You Can Build Web Applications To Mine The Enormous Amount Of Data Created By People On The Internet. With The Sophisticated Algorithms In This Book, You Can Write Smart Programs To Access Interesting Datasets From Other Web Sites, Collect Data From Users Of Your Own Applications, And Analyze And Understand The Data Once You've Found It.

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This Book\nLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization\nLearn effective strategies and best practices to improve and optimize machine learning systems and algorithms\nAsk – and answer – tough questions of your data with robust statistical models, built for a range of datasets\nWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.What You Will Learn\nExplore how to use different machine learning models to ask different questions of your data\nLearn how to build neural networks using Pylearn 2 and Theano\nFind out how to write clean and elegant Python code that will optimize the strength of your algorithms\nDiscover how to embed your machine learning model in a web application for increased accessibility\nPredict continuous target outcomes using regression analysis\nUncover hidden patterns and structures in data with clustering\nOrganize data using effective pre-processing techniques\nGet to grips with sentiment analysis to delve deeper into textual and social media data\nIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.\nPython Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills.

Explore the world of data science through Python and learn how to make sense of dataAbout This Book\nMaster data science methods using Python and its libraries\nCreate data visualizations and mine for patterns\nAdvanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning\nWho This Book Is ForIf you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed.What You Will Learn\nManage data and perform linear algebra in Python\nDerive inferences from the analysis by performing inferential statistics\nSolve data science problems in Python\nCreate high-end visualizations using Python\nEvaluate and apply the linear regression technique to estimate the relationships among variables.\nBuild recommendation engines with the various collaborative filtering algorithms\nApply the ensemble methods to improve your predictions\nWork with big data technologies to handle data at scale\nIn DetailData science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving.\nThis comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science.\nBeginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods.\nFinally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics.Style and approachThis book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.