R And Data Mining

📒Data Mining With R ✍ Luis Torgo

Data Mining With R Book PDF
✏Book Title : Data Mining with R
✏Author : Luis Torgo
✏Publisher : CRC Press
✏Release Date : 2016-11-30
✏Pages : 426
✏ISBN : 9781315399096
✏Available Language : English, Spanish, And French

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✏Data Mining with R Book Summary : Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies, and they facilitate the "do-it-yourself" approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the "world" of R and data mining. About the Author Luís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA.

📒R Data Mining ✍ Andrea Cirillo

R Data Mining Book PDF
✏Book Title : R Data Mining
✏Author : Andrea Cirillo
✏Publisher : Packt Publishing Ltd
✏Release Date : 2017-11-29
✏Pages : 442
✏ISBN : 9781787129238
✏Available Language : English, Spanish, And French

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✏R Data Mining Book Summary : Mine valuable insights from your data using popular tools and techniques in R About This Book Understand the basics of data mining and why R is a perfect tool for it. Manipulate your data using popular R packages such as ggplot2, dplyr, and so on to gather valuable business insights from it. Apply effective data mining models to perform regression and classification tasks. Who This Book Is For If you are a budding data scientist, or a data analyst with a basic knowledge of R, and want to get into the intricacies of data mining in a practical manner, this is the book for you. No previous experience of data mining is required. What You Will Learn Master relevant packages such as dplyr, ggplot2 and so on for data mining Learn how to effectively organize a data mining project through the CRISP-DM methodology Implement data cleaning and validation tasks to get your data ready for data mining activities Execute Exploratory Data Analysis both the numerical and the graphical way Develop simple and multiple regression models along with logistic regression Apply basic ensemble learning techniques to join together results from different data mining models Perform text mining analysis from unstructured pdf files and textual data Produce reports to effectively communicate objectives, methods, and insights of your analyses In Detail R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R. It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data. Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets. Style and approach This book takes a practical, step-by-step approach to explain the concepts of data mining. Practical use-cases involving real-world datasets are used throughout the book to clearly explain theoretical concepts.

📒R And Data Mining ✍ Yanchang Zhao

R And Data Mining Book PDF
✏Book Title : R and Data Mining
✏Author : Yanchang Zhao
✏Publisher : Academic Press
✏Release Date : 2012-12-31
✏Pages : 256
✏ISBN : 9780123972712
✏Available Language : English, Spanish, And French

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✏R and Data Mining Book Summary : R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more. Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation. With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. Presents an introduction into using R for data mining applications, covering most popular data mining techniques Provides code examples and data so that readers can easily learn the techniques Features case studies in real-world applications to help readers apply the techniques in their work

📒Data Mining With Rattle And R ✍ Graham Williams

Data Mining With Rattle And R Book PDF
✏Book Title : Data Mining with Rattle and R
✏Author : Graham Williams
✏Publisher : Springer Science & Business Media
✏Release Date : 2011-08-04
✏Pages : 374
✏ISBN : 9781441998903
✏Available Language : English, Spanish, And French

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✏Data Mining with Rattle and R Book Summary : Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.

📒Data Mining Algorithms ✍ Pawel Cichosz

Data Mining Algorithms Book PDF
✏Book Title : Data Mining Algorithms
✏Author : Pawel Cichosz
✏Publisher : John Wiley & Sons
✏Release Date : 2015-01-27
✏Pages : 720
✏ISBN : 9781118332580
✏Available Language : English, Spanish, And French

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✏Data Mining Algorithms Book Summary : "This book narrows down the scope of data mining by adopting a heavily modeling-oriented perspective"--

Data Mining Applications With R Book PDF
✏Book Title : Data Mining Applications with R
✏Author : Yanchang Zhao
✏Publisher : Academic Press
✏Release Date : 2013-11-26
✏Pages : 514
✏ISBN : 9780124115200
✏Available Language : English, Spanish, And French

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✏Data Mining Applications with R Book Summary : Data Mining Applications with R is a great resource for researchers and professionals to understand the wide use of R, a free software environment for statistical computing and graphics, in solving different problems in industry. R is widely used in leveraging data mining techniques across many different industries, including government, finance, insurance, medicine, scientific research and more. This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas. It is an ideal companion for data mining researchers in academia and industry looking for ways to turn this versatile software into a powerful analytic tool. R code, Data and color figures for the book are provided at the RDataMining.com website. Helps data miners to learn to use R in their specific area of work and see how R can apply in different industries Presents various case studies in real-world applications, which will help readers to apply the techniques in their work Provides code examples and sample data for readers to easily learn the techniques by running the code by themselves

📒Learning Data Mining With R ✍ Bater Makhabel

Learning Data Mining With R Book PDF
✏Book Title : Learning Data Mining with R
✏Author : Bater Makhabel
✏Publisher : Packt Publishing Ltd
✏Release Date : 2015-01-31
✏Pages : 314
✏ISBN : 9781783982110
✏Available Language : English, Spanish, And French

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✏Learning Data Mining with R Book Summary : This book is intended for the budding data scientist or quantitative analyst with only a basic exposure to R and statistics. This book assumes familiarity with only the very basics of R, such as the main data types, simple functions, and how to move data around. No prior experience with data mining packages is necessary; however, you should have a basic understanding of data mining concepts and processes.

Educational Data Mining With R And Rattle Book PDF
✏Book Title : Educational Data Mining with R and Rattle
✏Author : R. S. Kamath
✏Publisher : River Publishers
✏Release Date : 2016
✏Pages : 128
✏ISBN : 9788793379312
✏Available Language : English, Spanish, And French

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✏Educational Data Mining with R and Rattle Book Summary : Educational Data Mining (EDM) is one of the emerging fields in the pedagogy and andragogy paradigm, it concerns the techniques which research data coming from the educational domain. An archetype that is covered is that of learning by example. This is a guide for EDM implementation using R and Rattle open source data mining tools.

Exam Prep For R And Data Mining Book PDF
✏Book Title : Exam Prep for R and Data Mining
✏Author :
✏Publisher :
✏Release Date :
✏Pages :
✏ISBN :
✏Available Language : English, Spanish, And French

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✏Exam Prep for R and Data Mining Book Summary :

📒R Data Mining Projects ✍ Pradeepta Mishra

R Data Mining Projects Book PDF
✏Book Title : R Data Mining Projects
✏Author : Pradeepta Mishra
✏Publisher :
✏Release Date : 2017
✏Pages :
✏ISBN : OCLC:1137157173
✏Available Language : English, Spanish, And French

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✏R Data Mining Projects Book Summary : "The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools for data mining and analysis. It enables you to create high-level graphics and offers an interface to other languages. This means R is best suited to producing data and visual analytics through customization scripts and commands, instead of the typical statistical tools that provide tick boxes and drop-down menus for users. This video course explores data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields. We will teach you about R and its application to data mining, and give you relevant and useful information you can use to develop and improve your applications. It will help you complete complex data mining cases and guide you through handling issues you might encounter during projects."--Resource description page.

Mastering Text Mining With R Book PDF
✏Book Title : Mastering Text Mining with R
✏Author : Ashish Kumar
✏Publisher : Packt Publishing Ltd
✏Release Date : 2016-12-28
✏Pages : 258
✏ISBN : 9781782174707
✏Available Language : English, Spanish, And French

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✏Mastering Text Mining with R Book Summary : Master text-taming techniques and build effective text-processing applications with R About This Book Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide Gain in-depth understanding of the text mining process with lucid implementation in the R language Example-rich guide that lets you gain high-quality information from text data Who This Book Is For If you are an R programmer, analyst, or data scientist who wants to gain experience in performing text data mining and analytics with R, then this book is for you. Exposure to working with statistical methods and language processing would be helpful. What You Will Learn Get acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining process Access and manipulate data from different sources such as JSON and HTTP Process text using regular expressions Get to know the different approaches of tagging texts, such as POS tagging, to get started with text analysis Explore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in R Discover the underlying themes or topics that are present in an unstructured collection of documents, using common topic models such as Latent Dirichlet Allocation (LDA) Build a baseline sentence completing application Perform entity extraction and named entity recognition using R In Detail Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages. Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media. Style and approach This book takes a hands-on, example-driven approach to the text mining process with lucid implementation in R.

📒Social Media Mining With R ✍ Nathan Danneman

Social Media Mining With R Book PDF
✏Book Title : Social Media Mining with R
✏Author : Nathan Danneman
✏Publisher : Packt Publishing Ltd
✏Release Date : 2014-03-25
✏Pages : 122
✏ISBN : 9781783281787
✏Available Language : English, Spanish, And French

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✏Social Media Mining with R Book Summary : A concise, hands-on guide with many practical examples and a detailed treatise on inference and social science research that will help you in mining data in the real world. Whether you are an undergraduate who wishes to get hands-on experience working with social data from the Web, a practitioner wishing to expand your competencies and learn unsupervised sentiment analysis, or you are simply interested in social data analysis, this book will prove to be an essential asset. No previous experience with R or statistics is required, though having knowledge of both will enrich your experience.

Data Mining And Business Analytics With R Book PDF
✏Book Title : Data Mining and Business Analytics with R
✏Author : Johannes Ledolter
✏Publisher : John Wiley & Sons
✏Release Date : 2013-05-28
✏Pages : 368
✏ISBN : 9781118572153
✏Available Language : English, Spanish, And French

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✏Data Mining and Business Analytics with R Book Summary : Collecting, analyzing, and extracting valuable information froma large amount of data requires easily accessible, robust,computational and analytical tools. Data Mining and BusinessAnalytics with R utilizes the open source software R for theanalysis, exploration, and simplification of large high-dimensionaldata sets. As a result, readers are provided with the neededguidance to model and interpret complicated data and become adeptat building powerful models for prediction and classification. Highlighting both underlying concepts and practicalcomputational skills, Data Mining and Business Analytics withR begins with coverage of standard linear regression and theimportance of parsimony in statistical modeling. The book includesimportant topics such as penalty-based variable selection (LASSO);logistic regression; regression and classification trees;clustering; principal components and partial least squares; and theanalysis of text and network data. In addition, the bookpresents: • A thorough discussion and extensive demonstration of thetheory behind the most useful data mining tools • Illustrations of how to use the outlined concepts inreal-world situations • Readily available additional data sets and related Rcode allowing readers to apply their own analyses to the discussedmaterials • Numerous exercises to help readers with computing skillsand deepen their understanding of the material Data Mining and Business Analytics with R is an excellentgraduate-level textbook for courses on data mining and businessanalytics. The book is also a valuable reference for practitionerswho collect and analyze data in the fields of finance, operationsmanagement, marketing, and the information sciences.

Learning Path Book PDF
✏Book Title : Learning Path
✏Author :
✏Publisher :
✏Release Date : 2017
✏Pages :
✏ISBN : OCLC:1137157315
✏Available Language : English, Spanish, And French

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✏Learning Path Book Summary : "Data mining is a growing demand on the market as the world is generating data at an increasing pace. R is a popular programming language for statistics. It can be used for day-to-day data analysis tasks. This Learning Path is the complete learning process for data-happy people. We begin with a thorough introduction to data mining and how R makes it easy with its many packages. We then move on to exploring data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields using R's vast set of algorithms. Discover the versatility of R for data mining with the collection of analysis techniques in this Learning Path."--Resource description page.

📒R Data Mining Blueprints ✍ Pradeepta Mishra

R Data Mining Blueprints Book PDF
✏Book Title : R Data Mining Blueprints
✏Author : Pradeepta Mishra
✏Publisher : Packt Publishing Ltd
✏Release Date : 2016-07-29
✏Pages : 260
✏ISBN : 9781783989690
✏Available Language : English, Spanish, And French

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✏R Data Mining Blueprints Book Summary : Learn about data mining with real-world datasets About This Book Diverse real-world datasets to teach data mining techniques Practical and focused on real-world data mining cases, this book covers concepts such as spatial data mining, text mining, social media mining, and web mining Real-world case studies illustrate various data mining techniques, taking you from novice to intermediate Who This Book Is For Data analysts from beginner to intermediate level who need a step-by-step helping hand in developing complex data mining projects are the ideal audience for this book. They should have prior knowledge of basic statistics and little bit of programming language experience in any tool or platform. What You Will Learn Make use of statistics and programming to learn data mining concepts and its applications Use R Programming to apply statistical models on data Create predictive models to be applied for performing classification, prediction and recommendation Use of various libraries available on R CRAN (comprehensive R archives network) in data mining Apply data management steps in handling large datasets Learn various data visualization libraries available in R for representing data Implement various dimension reduction techniques to handle large datasets Acquire knowledge about neural network concept drawn from computer science and its applications in data mining In Detail The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools for data mining and analysis. It enables you to create high-level graphics and offers an interface to other languages. This means R is best suited to produce data and visual analytics through customization scripts and commands, instead of the typical statistical tools that provide tick boxes and drop-down menus for users. This book explores data mining techniques and shows you how to apply different mining concepts to various statistical and data applications in a wide range of fields. We will teach you about R and its application to data mining, and give you relevant and useful information you can use to develop and improve your applications. It will help you complete complex data mining cases and guide you through handling issues you might encounter during projects. Style and approach This fast-paced guide will help you solve predictive modeling problems using the most popular data mining algorithms through simple, practical cases.

Advanced Data Mining Projects With R Book PDF
✏Book Title : Advanced Data Mining Projects with R
✏Author : Pradeepta Mishra
✏Publisher :
✏Release Date : 2017
✏Pages :
✏ISBN : OCLC:1137157071
✏Available Language : English, Spanish, And French

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✏Advanced Data Mining Projects with R Book Summary : "Advanced Data Mining Projects with R takes you one step ahead in understanding the most complex data mining algorithms and implementing them in the popular R language. Follow up to our course Data Mining Projects in R, this course will teach you how to build your own recommendation engine. You will also implement dimensionality reduction and use it to build a real-world project. Going ahead, you will be introduced to the concept of neural networks and learn how to apply them for predictions, classifications, and forecasting. Finally, you will implement ggplot2, plotly and aspects of geomapping to create your own data visualization projects.By the end of this course, you will be well-versed with all the advanced data mining techniques and how to implement them using R, in any real-world scenario."--Resource description page.

Data Mining For Business Intelligence Concepts Techniques And Applications In Microsoft Office Excel With Xlminer Book PDF
✏Book Title : Data Mining for Business Intelligence Concepts Techniques and Applications in Microsoft Office Excel with Xlminer
✏Author : Galit Shmueli
✏Publisher : John Wiley & Sons
✏Release Date : 2008-09
✏Pages : 298
✏ISBN : 8126517581
✏Available Language : English, Spanish, And French

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✏Data Mining for Business Intelligence Concepts Techniques and Applications in Microsoft Office Excel with Xlminer Book Summary : Market_Desc: As a textbook or supplement for courses in data mining, data warehousing, business intelligence, and/or decision support systems at the upper undergraduate or beginning graduate (MS, Ph.D., or MBA) levels in departments of mathematics and statistics, computer science, information technology, engineering, or business; as a reference guide for professionals in related fields. Special Features: · The book s greatest strength lies in its presentation of hands-on, business-oriented applications, complete with real data sets and cases.· The chapters have been written with flexibility in mind so the user and/or instructor can navigate throughout the book as he or she chooses.· The excellent mix between mathematical rigor and readability make the book ideal for multiple readerships.· The software system-of-choice, XLMinerTM, is a familiar and easy-to-use tool for business analysts, consultants, and students since it is based on the popular Excel® spreadsheet concept. It provides a comprehensive set of data mining models and algorithms that includes statistical, machine learning and database methods - at no additional cost to the purchaser!· There are plentiful exercises and examples to motivate learning and understanding. About The Book: This book arose out of a data mining course at MIT s Sloan School of Management. Preparation for the course revealed that there are a number of excellent books on the business context of data mining, but their coverage of the statistical and machine learning algorithms and theoretical underpinnings is not sufficiently detailed to provide a practical guide for users who possess the raw skills and tools to analyze data. This book is intended for the business student (and practitioner) of data mining techniques, and the goal is threefold: (1) to provide both a theoretical and practical understanding of the key methods of classification, prediction, reduction and exploration that are at the heart of data mining; (2) to provide a business decision-making context for these methods; and (3) using real business cases and data, to illustrate the application and interpretation of these methods. The book employs the use of an Excel® add-in, XLMinerTM, at no cost to registered instructors, in order to illustrate and interpret the various data sets that are presented throughout. Real-life business cases are also presented so that readers can implement algorithms with a very low learning hurdle.

R Data Analysis And Visualization Book PDF
✏Book Title : R Data Analysis and Visualization
✏Author : Tony Fischetti
✏Publisher : Packt Publishing Ltd
✏Release Date : 2016-06-24
✏Pages : 1783
✏ISBN : 9781786460486
✏Available Language : English, Spanish, And French

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✏R Data Analysis and Visualization Book Summary : Master the art of building analytical models using R About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Build and customize publication-quality visualizations of powerful and stunning R graphs Develop key skills and techniques with R to create and customize data mining algorithms Use R to optimize your trading strategy and build up your own risk management system Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R Who This Book Is For This course is for data scientist or quantitative analyst who are looking at learning R and take advantage of its powerful analytical design framework. It's a seamless journey in becoming a full-stack R developer. What You Will Learn Describe and visualize the behavior of data and relationships between data Gain a thorough understanding of statistical reasoning and sampling Handle missing data gracefully using multiple imputation Create diverse types of bar charts using the default R functions Familiarize yourself with algorithms written in R for spatial data mining, text mining, and so on Understand relationships between market factors and their impact on your portfolio Harness the power of R to build machine learning algorithms with real-world data science applications Learn specialized machine learning techniques for text mining, big data, and more In Detail The R learning path created for you has five connected modules, which are a mini-course in their own right. As you complete each one, you'll have gained key skills and be ready for the material in the next module! This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility. The second place to explore is R Graphs, which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. You'll learn how to produce, customize, and publish advanced visualizations using this popular and powerful framework. With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions. Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, and so on. Style and approach Learn data analysis, data visualization techniques, data mining, and machine learning all using R and also learn to build models in quantitative finance using this powerful language.

📒Data Science Using Python And R ✍ Chantal D. Larose

Data Science Using Python And R Book PDF
✏Book Title : Data Science Using Python and R
✏Author : Chantal D. Larose
✏Publisher : Wiley
✏Release Date : 2019-04-09
✏Pages : 240
✏ISBN : 9781119526810
✏Available Language : English, Spanish, And French

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✏Data Science Using Python and R Book Summary : Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.

Exam Prep For Data Mining With R Learning With Case  Book PDF
✏Book Title : Exam Prep for Data Mining with R Learning with Case
✏Author :
✏Publisher :
✏Release Date :
✏Pages :
✏ISBN :
✏Available Language : English, Spanish, And French

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✏Exam Prep for Data Mining with R Learning with Case Book Summary :

Data Mining For Scientific And Engineering Applications Book PDF
✏Book Title : Data Mining for Scientific and Engineering Applications
✏Author : R.L. Grossman
✏Publisher : Springer Science & Business Media
✏Release Date : 2001-10-31
✏Pages : 605
✏ISBN : 1402000332
✏Available Language : English, Spanish, And French

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✏Data Mining for Scientific and Engineering Applications Book Summary : Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.

Business Analytics And Data Mining With R Book PDF
✏Book Title : Business Analytics and Data Mining with R
✏Author : Johannes Ledolter
✏Publisher :
✏Release Date : 2013
✏Pages : 351
✏ISBN : OCLC:1083607014
✏Available Language : English, Spanish, And French

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✏Business Analytics and Data Mining with R Book Summary :

An Introduction To Data Analysis In R Book PDF
✏Book Title : An Introduction to Data Analysis in R
✏Author : Alfonso Zamora Saiz
✏Publisher : Springer Nature
✏Release Date : 2020-07-27
✏Pages : 276
✏ISBN : 9783030489977
✏Available Language : English, Spanish, And French

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✏An Introduction to Data Analysis in R Book Summary : This textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R. The chapters cover topics such as the fundamentals of programming in R, data collection and preprocessing, including web scraping, data visualization, and statistical methods, including multivariate analysis, and feature exercises at the end of each section. The text requires only basic statistics skills, as it strikes a balance between statistical and mathematical understanding and implementation in R, with a special emphasis on reproducible examples and real-world applications. This textbook is primarily intended for undergraduate students of mathematics, statistics, physics, economics, finance and business who are pursuing a career in data analytics. It will be equally valuable for master students of data science and industry professionals who want to conduct data analyses.

The Elements Of Statistical Learning Book PDF
✏Book Title : The Elements of Statistical Learning
✏Author : Trevor Hastie
✏Publisher : Springer Science & Business Media
✏Release Date : 2001
✏Pages : 533
✏ISBN : 0387952845
✏Available Language : English, Spanish, And French

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✏The Elements of Statistical Learning Book Summary : This book describes the important ideas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry.

Advances In Knowledge Discovery And Data Mining Book PDF
✏Book Title : Advances in Knowledge Discovery and Data Mining
✏Author : Usama M. Fayyad
✏Publisher : Mit Press
✏Release Date : 1996
✏Pages : 611
✏ISBN : UOM:39015037286955
✏Available Language : English, Spanish, And French

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✏Advances in Knowledge Discovery and Data Mining Book Summary : Eight sections of this book span fundamental issues of knowledge discovery, classification and clustering, trend and deviation analysis, dependency derivation, integrated discovery systems, augumented database systems and application case studies. The appendices provide a list of terms used in the literature of the field of data mining and knowledge discovery in databases, and a list of online resources for the KDD researcher.

Principles Of Data Mining Book PDF
✏Book Title : Principles of Data Mining
✏Author : Max Bramer
✏Publisher : Springer Science & Business Media
✏Release Date : 2007-03-06
✏Pages : 344
✏ISBN : 1846287669
✏Available Language : English, Spanish, And French

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✏Principles of Data Mining Book Summary : This book explains the principal techniques of data mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples and explanations of the algorithms given. This will benefit readers of all levels, from those who use data mining via commercial packages, right through to academic researchers. The book aims to help the general reader develop the necessary understanding to use commercial data mining packages, and to enable advanced readers to understand or contribute to future technical advances. Includes exercises and glossary.

📒Mastering Social Media Mining With R ✍ Sharan Kumar Ravindran

Mastering Social Media Mining With R Book PDF
✏Book Title : Mastering Social Media Mining with R
✏Author : Sharan Kumar Ravindran
✏Publisher : Packt Publishing Ltd
✏Release Date : 2015-09-23
✏Pages : 248
✏ISBN : 9781784399672
✏Available Language : English, Spanish, And French

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✏Mastering Social Media Mining with R Book Summary : Extract valuable data from your social media sites and make better business decisions using R About This Book Explore the social media APIs in R to capture data and tame it Employ the machine learning capabilities of R to gain optimal business value A hands-on guide with real-world examples to help you take advantage of the vast opportunities that come with social media data Who This Book Is For If you have basic knowledge of R in terms of its libraries and are aware of different machine learning techniques, this book is for you. Those with experience in data analysis who are interested in mining social media data will find this book useful. What You Will Learn Access APIs of popular social media sites and extract data Perform sentiment analysis and identify trending topics Measure CTR performance for social media campaigns Implement exploratory data analysis and correlation analysis Build a logistic regression model to detect spam messages Construct clusters of pictures using the K-means algorithm and identify popular personalities and destinations Develop recommendation systems using Collaborative Filtering and the Apriori algorithm In Detail With an increase in the number of users on the web, the content generated has increased substantially, bringing in the need to gain insights into the untapped gold mine that is social media data. For computational statistics, R has an advantage over other languages in providing readily-available data extraction and transformation packages, making it easier to carry out your ETL tasks. Along with this, its data visualization packages help users get a better understanding of the underlying data distributions while its range of "standard" statistical packages simplify analysis of the data. This book will teach you how powerful business cases are solved by applying machine learning techniques on social media data. You will learn about important and recent developments in the field of social media, along with a few advanced topics such as Open Authorization (OAuth). Through practical examples, you will access data from R using APIs of various social media sites such as Twitter, Facebook, Instagram, GitHub, Foursquare, LinkedIn, Blogger, and other networks. We will provide you with detailed explanations on the implementation of various use cases using R programming. With this handy guide, you will be ready to embark on your journey as an independent social media analyst. Style and approach This easy-to-follow guide is packed with hands-on, step-by-step examples that will enable you to convert your real-world social media data into useful, practical information.

Data Mining With Rattle And R Book PDF
✏Book Title : Data Mining with Rattle and R
✏Author :
✏Publisher :
✏Release Date : 2011-08-03
✏Pages : 396
✏ISBN : 1441998918
✏Available Language : English, Spanish, And French

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✏Data Mining with Rattle and R Book Summary :

Scalable High Performance Computing For Knowledge Discovery And Data Mining Book PDF
✏Book Title : Scalable High Performance Computing for Knowledge Discovery and Data Mining
✏Author : Paul Stolorz
✏Publisher : Springer Science & Business Media
✏Release Date : 1998-01-31
✏Pages : 100
✏ISBN : 0792380975
✏Available Language : English, Spanish, And French

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✏Scalable High Performance Computing for Knowledge Discovery and Data Mining Book Summary : Scalable High Performance Computing for Knowledge Discovery and Data Mining brings together in one place important contributions and up-to-date research results in this fast moving area. Scalable High Performance Computing for Knowledge Discovery and Data Mining serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

📒Data Mining ✍ Florin Gorunescu

Data Mining Book PDF
✏Book Title : Data Mining
✏Author : Florin Gorunescu
✏Publisher : Springer Science & Business Media
✏Release Date : 2011-03-10
✏Pages : 360
✏ISBN : 3642197213
✏Available Language : English, Spanish, And French

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✏Data Mining Book Summary : The knowledge discovery process is as old as Homo sapiens. Until some time ago this process was solely based on the ‘natural personal' computer provided by Mother Nature. Fortunately, in recent decades the problem has begun to be solved based on the development of the Data mining technology, aided by the huge computational power of the 'artificial' computers. Digging intelligently in different large databases, data mining aims to extract implicit, previously unknown and potentially useful information from data, since “knowledge is power”. The goal of this book is to provide, in a friendly way, both theoretical concepts and, especially, practical techniques of this exciting field, ready to be applied in real-world situations. Accordingly, it is meant for all those who wish to learn how to explore and analysis of large quantities of data in order to discover the hidden nugget of information.