Download Hands-On Exploratory Data Analysis with R PDF

Hands-On Exploratory Data Analysis with R

Author :
Publisher : Packt Publishing Ltd
Release Date :
ISBN 13 : 1789802083
Pages : 266 pages
Rating : 4.0/5 (82 downloads)

Download Hands-On Exploratory Data Analysis with R PDF Format Full Free by Radhika Datar and published by Packt Publishing Ltd. This book was released on 2019-05-31 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills Key FeaturesSpeed up your data analysis projects using powerful R packages and techniquesCreate multiple hands-on data analysis projects using real-world dataDiscover and practice graphical exploratory analysis techniques across domainsBook Description Hands-On Exploratory Data Analysis with R will help you build not just a foundation but also expertise in the elementary ways to analyze data. You will learn how to understand your data and summarize its main characteristics. You'll also uncover the structure of your data, and you'll learn graphical and numerical techniques using the R language. This book covers the entire exploratory data analysis (EDA) process—data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the book, you will learn how to set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using tools such as DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems. By the end of this book, you will be able to successfully carry out a preliminary investigation on any dataset, identify hidden insights, and present your results in a business context. What you will learnLearn powerful R techniques to speed up your data analysis projectsImport, clean, and explore data using powerful R packagesPractice graphical exploratory analysis techniquesCreate informative data analysis reports using ggplot2Identify and clean missing and erroneous dataExplore data analysis techniques to analyze multi-factor datasetsWho this book is for Hands-On Exploratory Data Analysis with R is for data enthusiasts who want to build a strong foundation for data analysis. If you are a data analyst, data engineer, software engineer, or product manager, this book will sharpen your skills in the complete workflow of exploratory data analysis.


Download Exploratory Data Analysis with R PDF

Exploratory Data Analysis with R

Author :
Publisher : Lulu.com
Release Date :
ISBN 13 : 1365060063
Pages : 208 pages
Rating : 4.0/5 (6 downloads)

Download Exploratory Data Analysis with R PDF Format Full Free by Roger Peng and published by Lulu.com. This book was released on 2012-04-19 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the date you have. We will cover in detail the plotting systems in R as well as some of the basic principles of contructing informative data graphics. We will also cover some of the common multivariate statistical techniques uses to visualize high-dimensional data. Some of the topics we cover are making exploratory graphs, principles of analytic graphics, plotting systems and graphics devices in R, the base and ggplot2 plotting systems in R, clustering methods, and dimension reduction techniques. (Quelle: buchcover).


Download Exploratory Data Analysis Using R PDF

Exploratory Data Analysis Using R

Author :
Publisher : CRC Press
Release Date :
ISBN 13 : 0429847041
Pages : 601 pages
Rating : 4.0/5 (847 downloads)

Download Exploratory Data Analysis Using R PDF Format Full Free by Ronald K. Pearson and published by CRC Press. This book was released on 2018-05-04 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available. About the Author: Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).


Download Exploratory Data Analysis Using R PDF

Exploratory Data Analysis Using R

Author :
Publisher : CRC Press
Release Date :
ISBN 13 : 0429847033
Pages : 548 pages
Rating : 4.0/5 (847 downloads)

Download Exploratory Data Analysis Using R PDF Format Full Free by Ronald K. Pearson and published by CRC Press. This book was released on 2018-05-04 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available. About the Author: Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).


Download Managerial Perspectives on Intelligent Big Data Analytics PDF

Managerial Perspectives on Intelligent Big Data Analytics

Author :
Publisher : IGI Global
Release Date :
ISBN 13 : 1522572783
Pages : 335 pages
Rating : 4.7/5 (572 downloads)

Download Managerial Perspectives on Intelligent Big Data Analytics PDF Format Full Free by Sun, Zhaohao and published by IGI Global. This book was released on 2019-02-22 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data, analytics, and artificial intelligence are revolutionizing work, management, and lifestyles and are becoming disruptive technologies for healthcare, e-commerce, and web services. However, many fundamental, technological, and managerial issues for developing and applying intelligent big data analytics in these fields have yet to be addressed. Managerial Perspectives on Intelligent Big Data Analytics is a collection of innovative research that discusses the integration and application of artificial intelligence, business intelligence, digital transformation, and intelligent big data analytics from a perspective of computing, service, and management. While highlighting topics including e-commerce, machine learning, and fuzzy logic, this book is ideally designed for students, government officials, data scientists, managers, consultants, analysts, IT specialists, academicians, researchers, and industry professionals in fields that include big data, artificial intelligence, computing, and commerce.


Download Modern Statistics with R PDF

Modern Statistics with R

Author :
Publisher : BoD - Books on Demand
Release Date :
ISBN 13 : 9152701514
Pages : 594 pages
Rating : 4.5/5 (71 downloads)

Download Modern Statistics with R PDF Format Full Free by Måns Thulin and published by BoD - Books on Demand. This book was released on 2021-07-28 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. The aim of Modern Statistics with R is to introduce you to key parts of the modern statistical toolkit. It teaches you: - Data wrangling - importing, formatting, reshaping, merging, and filtering data in R. - Exploratory data analysis - using visualisation and multivariate techniques to explore datasets. - Statistical inference - modern methods for testing hypotheses and computing confidence intervals. - Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting. - Simulation - using simulation techniques for sample size computations and evaluations of statistical methods. - Ethics in statistics - ethical issues and good statistical practice. - R programming - writing code that is fast, readable, and free from bugs. Starting from the very basics, Modern Statistics with R helps you learn R by working with R. Topics covered range from plotting data and writing simple R code to using cross-validation for evaluating complex predictive models and using simulation for sample size determination. The book includes more than 200 exercises with fully worked solutions. Some familiarity with basic statistical concepts, such as linear regression, is assumed. No previous programming experience is needed.


Download Multivariate Analysis in the Pharmaceutical Industry PDF

Multivariate Analysis in the Pharmaceutical Industry

Author :
Publisher : Academic Press
Release Date :
ISBN 13 : 012811066X
Pages : 464 pages
Rating : 4.6/5 (11 downloads)

Download Multivariate Analysis in the Pharmaceutical Industry PDF Format Full Free by Ana Patricia Ferreira and published by Academic Press. This book was released on 2018-04-24 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate Analysis in the Pharmaceutical Industry provides industry practitioners with guidance on multivariate data methods and their applications over the lifecycle of a pharmaceutical product, from process development, to routine manufacturing, focusing on the challenges specific to each step. It includes an overview of regulatory guidance specific to the use of these methods, along with perspectives on the applications of these methods that allow for testing, monitoring and controlling products and processes. The book seeks to put multivariate analysis into a pharmaceutical context for the benefit of pharmaceutical practitioners, potential practitioners, managers and regulators. Users will find a resources that addresses an unmet need on how pharmaceutical industry professionals can extract value from data that is routinely collected on products and processes, especially as these techniques become more widely used, and ultimately, expected by regulators. Targets pharmaceutical industry practitioners and regulatory staff by addressing industry specific challenges Includes case studies from different pharmaceutical companies and across product lifecycle of to introduce readers to the breadth of applications Contains information on the current regulatory framework which will shape how multivariate analysis (MVA) is used in years to come


Download Exploratory Data Analysis with R PDF

Exploratory Data Analysis with R

Author :
Publisher :
Release Date :
ISBN 13 : 9781838555535
Pages : pages
Rating : 4.5/5 (838 downloads)

Download Exploratory Data Analysis with R PDF Format Full Free by Andrea Cirillo and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Harness the skills to analyze your data effectively with EDA and R About This Video Explore the most popular and advanced R package to place you on the cutting-edge of technology Learn what you need to do when you see your data for the first time, through to when you make it ready for modeling activities, and pitfalls to avoid along the way Includes data and code to practice every concept introduced within the course, to consolidate your learning and turn EDA into a habit In Detail The greatest number of mistakes and failures in data analysis comes from not performing adequate Exploratory Data Analysis (EDA). Lack of EDA knowledge can expose you to the great risk of drawing incorrect, and potentially harmful, conclusions from your data analysis. In this course, you will learn how EDA helps you draw conclusions to make better sense of your data and implement correct techniques. We'll begin with a brief introduction to EDA, its importance, and advantages over BI tools. Using R libraries like dplyr and ggplot2, we will generate insights and formulate relevant questions for investigation and communicate the results effectively using visualizations. You will learn how to spot missing data and errors, validate assumptions, and identify the patterns for understanding the problem. Based on this, you'll be able to select a correct ML model to use for your data. By the end of the course, you will be able to quickly get know and interpret various kinds of data sets you will be presented with, and easily understand how to handle and work with them in order to make them ready for further modeling activities. Please note that basic knowledge of R and R Studio, together with some knowledge of descriptive statistics, are key to getting the best out of this course.


Download Exploratory Data Analysis Using R PDF

Exploratory Data Analysis Using R

Author :
Publisher :
Release Date :
ISBN 13 :
Pages : pages
Rating : 4./5 ( downloads)

Download Exploratory Data Analysis Using R PDF Format Full Free by Abhishek Chhibber and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This video series covers Exploratory Data Analysis (EDA) using R. It is intended for beginners, who have basic programming knowledge in any language, and want to learn R for data analysis. The series starts with setting up the software environment and understanding the basic syntax of R, and then graduates to importing, wrangling, and analyzing data by using specific packages such as Dplyr and Tidyr. We explore EDA through univariate, bivariate, and multivariate analysis, as well as analyze charts using R's Ggplot2 package."--Resource description page.


Download Foundations of Intelligent Systems PDF

Foundations of Intelligent Systems

Author :
Publisher : Springer
Release Date :
ISBN 13 : 3030018512
Pages : 464 pages
Rating : 4.5/5 (18 downloads)

Download Foundations of Intelligent Systems PDF Format Full Free by Michelangelo Ceci and published by Springer. This book was released on 2018-10-22 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 24th International Symposium on Foundations of Intelligent Systems, ISMIS 2018, held in Limassol, Cyprus, in October 2018. The 32 full, 8 short, and 4 application papers presented in this volume were carefully reviewed and selected from 59 submissions. The papers deal with topics such as bioinformatics and health informatics, graph mining, image analysis, intelligent systems, mining complex patterns, novelty detection and class imbalance, social data analysis, spatio-temporal analysis, and topic modeling and opinion mining. In addition, three special sessions were organized, namely: Special Session on Granular and Soft Clustering for Data Science, Special Session on Intelligent Methodologies for Traffic Data Analysis and Mining, and Special Session on Advanced Methods in Machine Learning for Modeling Complex Data.


Download Interactive and Dynamic Graphics for Data Analysis PDF

Interactive and Dynamic Graphics for Data Analysis

Author :
Publisher : Springer Science & Business Media
Release Date :
ISBN 13 : 0387717625
Pages : 188 pages
Rating : 4.6/5 (717 downloads)

Download Interactive and Dynamic Graphics for Data Analysis PDF Format Full Free by Dianne Cook and published by Springer Science & Business Media. This book was released on 2007-09-05 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapter topics include clustering, supervised classification, and working with missing values. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The book is augmented by a wealth of online material.


Download Data Science Using Python and R PDF

Data Science Using Python and R

Author :
Publisher : Wiley
Release Date :
ISBN 13 : 1119526817
Pages : 240 pages
Rating : 4.8/5 (526 downloads)

Download Data Science Using Python and R PDF Format Full Free by Chantal D. Larose and published by Wiley. This book was released on 2019-04-09 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


Download Using R to Unlock the Value of Big Data: Big Data Analytics with Oracle R Enterprise and Oracle R Connector for Hadoop PDF

Using R to Unlock the Value of Big Data: Big Data Analytics with Oracle R Enterprise and Oracle R Connector for Hadoop

Author :
Publisher : McGraw Hill Professional
Release Date :
ISBN 13 : 0071824383
Pages : 77 pages
Rating : 4.3/5 (824 downloads)

Download Using R to Unlock the Value of Big Data: Big Data Analytics with Oracle R Enterprise and Oracle R Connector for Hadoop PDF Format Full Free by Mark Hornick and published by McGraw Hill Professional. This book was released on 2013-06-27 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Oracle Press Guide to Big Data Analytics using R Cowritten by members of the Big Data team at Oracle, this Oracle Press book focuses on analyzing data with R while making it scalable using Oracle’s R technologies. Using R to Unlock the Value of Big Data provides an introduction to open source R and describes issues with traditional R and database interaction. The book then offers in-depth coverage of Oracle’s strategic R offerings: Oracle R Enterprise, Oracle R Distribution, ROracle, and Oracle R Connector for Hadoop. You can practice your new skills using the end-of-chapter exercises.


Download Exploratory Multivariate Analysis by Example Using R PDF

Exploratory Multivariate Analysis by Example Using R

Author :
Publisher : CRC Press
Release Date :
ISBN 13 : 1315301865
Pages : 262 pages
Rating : 4.8/5 (31 downloads)

Download Exploratory Multivariate Analysis by Example Using R PDF Format Full Free by Francois Husson and published by CRC Press. This book was released on 2017-04-25 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) a


Download A Course in Statistics with R PDF

A Course in Statistics with R

Author :
Publisher : John Wiley & Sons
Release Date :
ISBN 13 : 1119152720
Pages : 693 pages
Rating : 4.7/5 (152 downloads)

Download A Course in Statistics with R PDF Format Full Free by Prabhanjan N. Tattar and published by John Wiley & Sons. This book was released on 2016-05-02 with total page 693 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models. Key features: Integrates R basics with statistical concepts Provides graphical presentations inclusive of mathematical expressions Aids understanding of limit theorems of probability with and without the simulation approach Presents detailed algorithmic development of statistical models from scratch Includes practical applications with over 50 data sets


Download Oracle R Enterprise: Harnessing the Power of R in Oracle Database PDF

Oracle R Enterprise: Harnessing the Power of R in Oracle Database

Author :
Publisher : McGraw Hill Professional
Release Date :
ISBN 13 : 1259585646
Pages : 253 pages
Rating : 4.6/5 (585 downloads)

Download Oracle R Enterprise: Harnessing the Power of R in Oracle Database PDF Format Full Free by Brendan Tierney and published by McGraw Hill Professional. This book was released on 2016-11-04 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the Big Data Capabilities of Oracle R Enterprise Effectively manage your enterprise’s big data and keep complex processes running smoothly using the hands-on information contained in this Oracle Press guide. Oracle R Enterprise: Harnessing the Power of R in Oracle Database shows, step-by-step, how to create and execute large-scale predictive analytics and maintain superior performance. Discover how to explore and prepare your data, accurately model business processes, generate sophisticated graphics, and write and deploy powerful scripts. You will also find out how to effectively incorporate Oracle R Enterprise features in APEX applications, OBIEE dashboards, and Apache Hadoop systems. Learn to: • Install, configure, and administer Oracle R Enterprise • Establish connections and move data to the database • Create Oracle R Enterprise packages and functions • Use the R language to work with data in Oracle Database • Build models using ODM, ORE, and other algorithms • Develop and deploy R scripts and use the R script repository • Execute embedded R scripts and employ ORE SQL API functions • Map and manipulate data using Oracle R Advanced Analytics for Hadoop • Use ORE in Oracle Data Miner, OBIEE, and other applications


Download Statistical Data Analysis Explained PDF

Statistical Data Analysis Explained

Author :
Publisher : John Wiley & Sons
Release Date :
ISBN 13 : 1119965284
Pages : 362 pages
Rating : 4.2/5 (965 downloads)

Download Statistical Data Analysis Explained PDF Format Full Free by Clemens Reimann and published by John Wiley & Sons. This book was released on 2011-08-31 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Few books on statistical data analysis in the natural sciences are written at a level that a non-statistician will easily understand. This is a book written in colloquial language, avoiding mathematical formulae as much as possible, trying to explain statistical methods using examples and graphics instead. To use the book efficiently, readers should have some computer experience. The book starts with the simplest of statistical concepts and carries readers forward to a deeper and more extensive understanding of the use of statistics in environmental sciences. The book concerns the application of statistical and other computer methods to the management, analysis and display of spatial data. These data are characterised by including locations (geographic coordinates), which leads to the necessity of using maps to display the data and the results of the statistical methods. Although the book uses examples from applied geochemistry, and a large geochemical survey in particular, the principles and ideas equally well apply to other natural sciences, e.g., environmental sciences, pedology, hydrology, geography, forestry, ecology, and health sciences/epidemiology. The book is unique because it supplies direct access to software solutions (based on R, the Open Source version of the S-language for statistics) for applied environmental statistics. For all graphics and tables presented in the book, the R-scripts are provided in the form of executable R-scripts. In addition, a graphical user interface for R, called DAS+R, was developed for convenient, fast and interactive data analysis. Statistical Data Analysis Explained: Applied Environmental Statistics with R provides, on an accompanying website, the software to undertake all the procedures discussed, and the data employed for their description in the book.