R Graphics Essentials for Great Data Visualization 1st pdf下载 [问题点数:0分]

Bbs1
本版专家分:0
结帖率 70.98%
R Graphics Essentials for Great Data Visualization 1st pdf
Data <em>visualization</em> is one of the most important part of <em>data</em> science. Many books and courses present a catalogue of <em>graphics</em> but they don't teach you which charts to use according to the type of the <em>data</em>. In this book, we start by presenting the key graphic systems and packages available in R, including R base graphs, lattice and ggplot2 plotting systems. Next, we provide more than 200 practical examples to create <em>great</em> <em>graphics</em> for the right <em>data</em> using either the ggplot2 package and extensions or the traditional R <em>graphics</em>. With this book, you 'll learn: - How to quickly create beautiful <em>graphics</em> using ggplot2 packages - How to properly customize and annotate the plots - Type of <em>graphics</em> for visualizing categorical and continuous variables - How to add automatically p-values to box plots, bar plots and alternatives - How to add marginal density plots and correlation coefficients to scatter plots - Key methods for analyzing and visualizing multivariate <em>data</em> - R functions and packages for plotting time series <em>data</em> - How to combine multiple plots on one page to create production-quality figures.
R Graphics Essentials for Great Data Visualization
R Graphics Essentials for Great Data Visualization: +200 Practical Examples You Want to Know for Data Science By 作者: Mr Alboukadel Kassambara ISBN-10 书号: 1979748101 ISBN-13 书号: 9781979748100 Edition 版本: 1 出版日期: 2017-11-14 pages 页数: 257 1 About the author 2 R Basics for Data Visualization 3 Plot One Variable 4Plot Grouped Data 5 Plot Two Continuous Variables 6 Plot Multivariate Continuous Data 7 Visualizing Multivariate Categorical Data 8 Plot Time Series Data 9 Facets:Multi-Panels GGPlot 10 Arrange Multiple GGPlot on One Page 11 Customize GGPlot References
R_Graphics,_Second_Edition.pdf
R_Graphics,_Second_Edition.<em>pdf</em>
ggplot2 elegant graphics for data analysis
r 三个绘图包之一,默认绘图精美,可以参考coursera上的课程一起学习。
Great Principles of Computing 无水印原版pdf
Great Principles of Computing 英文无水印原版<em>pdf</em> <em>pdf</em>所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络,如有侵权,请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书
298本R语言书籍免费送!!!
本人作为R语言爱好者,广泛搜集了关于R语言的书籍,具体包括R语言基础知识、统计学(含生物统计)、数据挖掘、计量经济学(时间序列)等,范围广,资料全面,多为可编辑的<em>pdf</em>版本,不少都是各学科领域R语言经典的书籍。为此,本人免费上传以下298书籍,希望能够为R语言爱好者节省搜集书籍的时间,相互交流,共同学习。rnrnR 语言书籍目录:rnrn第 1-15 册(QQ 群:538911602)rnrn1 
VTK: The visualization toolkit: An object-oriented approach to 3D graphics
The <em>visualization</em> toolkit: an object-oriented approach to 3D <em>graphics</em>, 3rd edition <em>pdf</em>重新处理得到的文字清晰的版本.
Unreal Engine 4 AI Programming Essential.pdf
电子书非扫描版,看标题应该是介绍虚幻4里面的ai开发。。
R Graphics Cookbook
R Graphics Cookbook是关于R语言作图的一本教程,主要是使用ggplot2来做图的,由浅入深,值得学习一下
Applied Data Visualization with R and ggplot2
Develop informative and aesthetic <em>visualization</em>s that enable effective <em>data</em> analysis in less time Applied Data Visualization with R and ggplot2 introduces you to the world of <em>data</em> <em>visualization</em> by taking you through the basic features of ggplot2. To start with, you’ll learn how to set up the R environment, followed by getting insights into the grammar of <em>graphics</em> and geometric objects before you explore the plotting techniques. You’ll discover what layers, scales, coordinates, and themes are, and study how you can use them to transform your <em>data</em> into aesthetical graphs. Once you’ve grasped the basics, you’ll move on to studying simple plots such as histograms and advanced plots such as superimposing and density plots. You’ll also get to grips with plotting trends, correlations, and statistical summaries. By the end of this book, you’ll have created <em>data</em> <em>visualization</em>s that will impress your clients.
ggplot2_ Elegant Graphics for Data Analysis_英文原版2016年版
Springer 出版社 2016年版 ggplot2 电子书 Elegant Graphics for Data Analysis R语言可视化神包ggplot2的最新介绍,由 use R 提供。
Visualization Analysis and Design[Tamara Munzner]
Tamara Munzner所著Visualization Analysis and Design(VAD)是可视化方向的经典书籍之一,理论基础比较翔实
Python Data Science Essentials -3rd
ython数据科学概要 第3版 版: 第3版 国际标准书号: 978-1789537864 发表于: 2018年10月20日 页数: 472页 作者:Alberto Boschetti, Luca Massaron 使用流行的数据科学工具从您的数据中获得有用的见解 完全扩展和升级,最新版本的Python Data Science Essentials将帮助您使用最常见的Python库在数据科学操作中取得成功。本书提供了对Python核心的最新见解,包括最新版本的Jupyter Notebook,NumPy,pandas和scikit-learn。 本书涵盖了详细的示例和大型混合数据集,可帮助您掌握数据收集,数据调整和分析,可视化和报告活动的基本统计技术。您还将了解高级数据科学主题,如机器学习算法,分布式计算,调优预测模型和自然语言处理。此外,您还将学习深度学习和渐变增强解决方案,如XGBoost,LightGBM和CatBoost。 在本书的最后,您将全面了解主要的机器学习算法,图形分析技术以及所有可视化和部署工具,以便更轻松地向数据科学专家和企业的受众展示您的结果。 你将学到什么 在Windows,Mac和Linux上设置数据科学工具箱 使用scikit-learn库提供的核心机器学习方法 操纵,修复和探索数据以解决数据科学问题 学习先进的探索和操作技术来解决数据操作问题 优化机器学习模型以优化性能 浏览和聚类图表,利用数据中的互连和链接
ggplot2 Elegant Graphics for Data Analysis 2nd 原版pdf by Wickham
Welcome to the second edition of “ggplot2: elegant <em>graphics</em> for <em>data</em> analysis”. I’m so excited to have an updated book that shows off all the latest and <em>great</em>est ggplot2 features, as well as the <em>great</em> things that have been happening in R and in the ggplot2 community the last 5 years. The ggplot2 community is vibrant: the ggplot2 mailing list has over 7,000 members and there is a very active Stack Overflow community, with nearly 10,000 questions tagged with ggplot2. While most of my development effort is no longer going into ggplot2 (more on that below), there’s never been a better time to learn it and use it.
R数据可视化手册.pdf
《R数据可视化手册》侧重于解决具体问题,是R数据可视化的实战秘籍。《R数据可视化手册》中绝大多数的绘图案例都是以强大、灵活制图而著称的R包ggplot2实现的,充分展现了ggplot2生动、翔实的一面。从如何画点图、线图、柱状图,到如何添加注解、修改坐标轴和图例,再到分面的使用和颜色的选取等,本书都有清晰的讲解。虽然本书的大多数技巧使用的是ggplot2,但是并不仅仅局限于ggplot2的介绍。作者的理念是用合适的工具来完成合适的绘图任务,读者也可以学到许多其他有用的绘图函数和工具,来适应各种复杂的需求。 《R数据可视化手册》是学习R中丰富的数据可视化方法的权威手册,非常适合对R 语言有基本的了解的读者阅读。
Data_Visualization_with_R
《Data_Visualization_with_R》介绍各种图形的绘制技巧,内容新而全面,非常实用
Machine Learning Essentials: Practical Guide in R Book preview
Discovering knowledge from big multivariate <em>data</em>, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring <em>data</em> sets, as well as, for building predictive models. The main parts of the book include: Unsupervised learning methods, to explore and discover knowledge from a large multivariate <em>data</em> set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the <em>data</em> contain multiple correlated predictor variables. Model validation and evaluation techniques for measuring the performance of a predictive model. Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in <em>data</em> mining for students and researchers. Key features: Covers machine learning algorithm and implementation Key mathematical concepts are presented Short, self-contained chapters with practical examples. At the end of each chapter, we present R lab sections in which we systematically work through applications of the various methods discussed in that chapter.
Mastering Python Data Visualization.pdf(高清带书签)
Mastering Python Data Visualization
Computer Graphics Programming in OpenGL with C++
This book provides step-by-step instruction on modern 3D <em>graphics</em> shader programming in OpenGL with C++, along with its theoretical foundations. It is appropriate both for computer science <em>graphics</em> courses and for professionals interested in mastering 3D <em>graphics</em> skills. It has been designed in a 4-color, “teach-yourself” format with numerous examples and detailed explanations. Every shader stage is explored, starting with the basics of modeling, lighting, textures, etc., up through advanced techniques such as tessellation, soft shadows, and generating realistic materials and environments.
Data Visualisation with R_100 Examples-Springer(2017).pdf
This book is a translation of the German book “Datendesign mit R” that was published 2014 by Open Source Press. Due to the encouraging strong interest in the German edition Springer Verlag offered to publish an English translation. First of all I would like to thank Ralf Gerstner from Springer for this and for his helpful suggestions for improvement, as well as Annika Brun for translating most of the text, Colin Marsh for copy editing, and Katja Diederichs for converting all scripts from German to English. Last year I benefited a lot from a communication with Antony Unwin. His book “Graphical Data Analysis with R” can be seen as complementary to my own: while this one focusses on presentation of <em>graphics</em>, you will benefit from his book if you are interested in exploring <em>data</em> graphically.
MATLAB Graphics and Data Visualization Cookbook
1.Collection of <em>data</em> <em>visualization</em> recipes with functionalized versions of common tasks for easy integration into your <em>data</em> analysis workflow 2.Recipes cross-referenced with MATLAB product pages and MATLAB Central File Exchange resources for improved coverage 3.Includes hand created indices to find exactly what you need; such as application driven, or functionality driven solutions
D3.js 4.x Data Visualization - Third Edition2017
Welcome to D3.js 4.x Data Visualization, Third Edition. Over the course of this book, you’ll learn the basics of one of the world’s most ubiquitous and powerful <em>data</em> <em>visualization</em> libraries, but we don’t stop there. By the end of our time together, you’ll have all the skills you need to become a total D3 ninja, and will be able to do everything from build <em>visualization</em>s from scratch straight through to using it on the server and writing automated tests. If you haven’t leveled up your JavaScript skills in a while, you’re in for a treat--this book endeavors to use the latest features currently being added to the language, all the while explaining why they’re cool and how they differ from "old school" JavaScript.
R.in.Action.2nd.Edition.Data.Analysis.and.Graphics.with.R.1617291382
Summary R in Action, Second Edition presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete <em>data</em> that are difficult to analyze using traditional methods. You'll also master R's extensive graphical capabilities for exploring and presenting <em>data</em> visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Business pros and researchers thrive on <em>data</em>, and R speaks the language of <em>data</em> analysis. R is a powerful programming language for statistical computing. Unlike general-purpose tools, R provides thousands of modules for solving just about any <em>data</em>-crunching or presentation challenge you're likely to face. R runs on all important platforms and is used by thousands of major corporations and institutions worldwide. About the Book R in Action, Second Edition teaches you how to use the R language by presenting examples relevant to scientific, technical, and business developers. Focusing on practical solutions, the book offers a crash course in statistics, including elegant methods for dealing with messy and incomplete <em>data</em>. You'll also master R's extensive graphical capabilities for exploring and presenting <em>data</em> visually. And this expanded second edition includes new chapters on forecasting, <em>data</em> mining, and dynamic report writing. What's Inside Complete R language tutorial Using R to manage, analyze, and visualize <em>data</em> Techniques for debugging programs and creating packages OOP in R Over 160 graphs About the Author Dr. Rob Kabacoff is a seasoned researcher and teacher who specializes in <em>data</em> analysis. He also maintains the popular Quick-R website at statmethods.net. Table of Contents PART 1 GETTING STARTED Chapter 1. Introduction to R Chapter 2. Creating a <em>data</em>set Chapter 3. Getting started with graphs Chapter 4. Basic <em>data</em> management Chapter 5. Advanced <em>data</em> management PART 2 BASIC METHODS Chapter 1. Basic graphs Chapter 2. Basic statistics PART 3 INTERMEDIATE METHODS Chapter 1. Regression Chapter 2. Analysis of variance Chapter 3. Power analysis Chapter 4. Intermediate graphs Chapter 5. Resampling statistics and bootstrapping PART 4 ADVANCED METHODS Chapter 1. Generalized linear models Chapter 2. Principal components and factor analysis Chapter 3. Time series Chapter 4. Cluster analysis Chapter 5. Classification Chapter 6. Advanced methods for missing <em>data</em> PART 5 EXPANDING YOUR SKILLS Chapter 1. Advanced <em>graphics</em> with ggplot2 Chapter 2. Advanced programming Chapter 3. Creating a package Chapter 4. Creating dynamic reports Chapter 5. Advanced <em>graphics</em> with the lattice package available online only from manning.com/kabacoff2
Fundamentals of Computer Graphics 4th(高清版)
高清版,好东西要和大家分享,计算机图形学必看书目!
Data Mining for Business Analytics.pdf
Data Mining for Business Analytics.<em>pdf</em>
Lattice Multivariate Data Visualization with R
About this book Gives a comprehensive overview of the Lattice <em>graphics</em> system and shows how to use it effectively Includes numerous examples using <em>data</em> sets from various R packages All code and figures including color version also available online R is rapidly growing in popularity as the environment of choice for <em>data</em> analysis and <em>graphics</em> both in academia and industry. Lattice brings the proven design of Trellis <em>graphics</em> (originally developed for S by William S. Cleveland and colleagues at Bell Labs) to R, considerably expanding its capabilities in the process. Lattice is a powerful and elegant high level <em>data</em> <em>visualization</em> system that is sufficient for most everyday <em>graphics</em> needs, yet flexible enough to be easily extended to handle demands of cutting edge research. Written by the author of the lattice system, this book describes it in considerable depth, beginning with the <em>essentials</em> and systematically delving into specific low levels details as necessary. No prior experience with lattice is required to read the book, although basic familiarity with R is assumed. The book contains close to150 figures produced with lattice. Many of the examples emphasize principles of good graphical design; almost all use real <em>data</em> sets that are publicly available in various R packages. All code and figures in the book are also available online, along with supplementary material covering more advanced topics. Deepayan Sarkar won the 2004 John M. Chambers Statistical Software Award for writing lattice while he was a graduate student in Statistics at the University of Wisconsin-Madison. He is currently doing postdoctoral research in the Computational Biology program at the Fred Hutchinson Cancer Research Center, a member of the R Core Team, and an active participant on the R mailing lists. Written for: Researchers, grad students
Data Visualization with JavaScript 无水印pdf
Data Visualization with JavaScript 英文无水印<em>pdf</em> <em>pdf</em>所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络,如有侵权,请联系上传者或csdn删除 本资源转载自网络,如有侵权,请联系上传者或csdn删除
Learn Pixi.js(Apress,2015)
Create and display interactive <em>graphics</em>, build scenes and animated transitions, make cross-platform, responsive games and applications for multiple screen resolutions, and use Pixi.js's spectacular WebGL rendering effects. Learn how to create applications for desktop and touch-screen devices, and how to use the best open-source plugins to extend Pixi.js's capabilities in a myriad of exciting ways. If you've ever wondered what you need to know to start making games, or what technology you need to build high-performance mobile apps, this book will show you the way. Learn Pixi.js is your one-stop shop for everything you need to know to quickly start making spectacular cross-platform interactive games and animations.
用数据讲故事Storytelling with Data 高清文字无水印 pdf
用数据讲故事Storytelling with Data 高清文字无水印 <em>pdf</em>
Practical-Guide-to-Principal-Component-Methods-in-R
Practical-Guide-to-Principal-Component-Methods-in-R是一本很好的讲解PCA等多元统计分析实战的书;作者也开发很多相关的R语言package。这本书原理和实践都有一定的涉及,并且对如何解读结果也有很好的总结。是大数据分析,生物信息学分析等领域不可多得的好书。
Apache Hive Essentials(第二版)
Apache Hive Essentials Essential techniques to help you process, and get unique insights from, big <em>data</em>
IPython+Interactive+Computing+and+Visualization+Cookbook,2nd-(2018).epub
We are becoming awash in the flood of digital <em>data</em> from scientific research, engineering, economics, politics, journalism, business, and many other domains. As a result, analyzing, visualizing, and harnessing <em>data</em> is the occupation of an increasingly large and diverse set of people. Quantitative skills such as programming, numerical computing, mathematics, statistics, and <em>data</em> mining, which form the core of <em>data</em> science, are more and more appreciated in a seemingly endless plethora of fields. Python, a widely-known programming language, is also one of the leading open platforms for <em>data</em> science. IPython is a mature Python project that provides scientist-friendly interactive access to Python. It is part of the broader Project Jupyter, which aims to provide high-quality environments for interactive computing, <em>data</em> analysis, <em>visualization</em>, and the authoring of interactive scientific documents. Jupyter is estimated to have several million users today. The prequel of this book, Learning IPython for Interactive Computing and Data Visualization Second Edition, Packt Publishing was published in 2015, two years after the first edition. It is a beginner-level introduction to <em>data</em> science and numerical computing with Python, IPython, and Jupyter. This book, the first edition of which was published in 2014, continues that journey by presenting more than 100 recipes for interactive scientific computing and <em>data</em> science. These recipes not only cover programming topics such as numerical computing, high-performance computing, parallel computing, and interactive <em>visualization</em>, but also <em>data</em> analysis topics such as statistics, <em>data</em> mining, machine learning, signal processing, graph theory, numerical optimization, and many others. This second edition is fully compatible with the latest versions of the platform and its libraries. It includes new recipes to better leverage the latest features of Python 3, and it introduces promising new projects such as JupyterLab, Altair, and Dask.
Hadley Wickham的R包开发经典教程
这是Hadley Wickham的R包开发经典教程,R包开发人员必备资料。
MATLAB Graphics
MATLAB Graphics and Data Visualization Cookbook
R_Graphics_Cookbook
R_Graphics_Cookbook R作图能力非常强大,ggplot2是基于<em>data</em> frame设计的作图包,跟lattice也很大差别,R Graphics Cookbook偏重于讲如何作图,是非常重要的一本参考书。遇到作图细节问题和R操作问题去网上搜索即可。
Qt5 Data Visualization 3D官方教程配套示例代码
--------------------------- Qt Data Visualization 5.7.0 --------------------------- Qt Data Visualization module provides multiple graph types to visualize <em>data</em> in 3D space both with C++ and Qt Quick 2. System Requirements =================== - Qt 5.2.1 or newer - OpenGL 2.1 or newer (recommended) or OpenGL ES2 (reduced feature set) - Manipulating Qt Data Visualization graphs with QML Designer requires Qt Creator 3.3 or newer Building ======== Configure the project with qmake: qmake After running qmake, build the project with make: (Linux) make (Windows with MinGw) mingw32-make (Windows with Visual Studio) nmake (OS X) make The above generates the default makefiles for your configuration, which is typically the release build if you are using precompiled binary Qt distribution. To build both debug and release, or one specifically, use one of the following qmake lines instead. For debug builds: qmake CONFIG+=debug make or qmake CONFIG+=debug_and_release make debug For release builds: qmake CONFIG+=release make or qmake CONFIG+=debug_and_release make release For both builds (Windows/OS X only): qmake CONFIG+="debug_and_release build_all" make After building, install the module to your Qt directory: make install If you want to uninstall the module: make uninstall Building as a statically linked library ======================================= The same as above applies, you will just have to add static to the CONFIG: qmake CONFIG+=static Documentation ============= The documentation can be generated with: make docs The documentation is generated into the doc folder under the build folder. Both Qt Assistant (qt<em>data</em><em>visualization</em>.qch) and in HTML format (qt<em>data</em><em>visualization</em> subfolder) documentation is generated. Please refer to the generated documentation for more information: doc/qt<em>data</em><em>visualization</em>/qt<em>data</em><em>visualization</em>-index.html Known Issues ============ - Some platforms like Android and WinRT cannot handle multiple native windows properly, so only the Qt Quick 2 versions of graphs are available in practice for those platforms. - Shadows are not supported with OpenGL ES2 (including Angle builds in Windows). - Anti-aliasing doesn't work with OpenGL ES2 (including Angle builds in Windows). - QCustom3DVolume items are not supported with OpenGL ES2 (including Angle builds in Windows). - Surfaces with non-straight rows and columns do not always render properly. - Q3DLight class (and Light3D QML item) are currently not usable for anything. - Changing most of Q3DScene properties affecting subviewports currently has no effect. - Widget based examples layout incorrectly in iOS. - Reparenting a graph to an item in another QQuickWindow is not supported. - Android builds of QML applications importing QtDataVisualization also require "QT += <em>data</em><em>visualization</em>" in the pro file. This is because Qt Data Visualization QML plugin has a dependency to Qt Data Visualization C++ library, which Qt Creator doesn't automatically add to the deployment package. - Only OpenGL ES2 emulation is available for software renderer (that is, when using QCoreApplication::setAttribute(Qt::AA_UseSoftwareOpenGL))
SPSS_Statistics_REssentials_22_win64
这是适用于spss22版本的R插件,可用于win64,亲测可用
The.Data.Science.Handbook.pdf
A comprehensive overview of <em>data</em> science covering the analytics, programming, and business skills necessary to master the discipline Finding a good <em>data</em> scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good <em>data</em> science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in <em>data</em> science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a <em>data</em> scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in <em>data</em> science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of <em>data</em> and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of <em>data</em> science skills, is given its own chapter, and all topics are explained in the context of solving real-world <em>data</em> problems. The book also features: Extensive sample code and tutorials using Python™ along with its technical libraries Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity A wide variety of case studies from industry Practical advice on the realities of being a <em>data</em> scientist today, including the overall workflow, where time is spent, the types of <em>data</em>sets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for <em>data</em> analysis methodology and big <em>data</em> software tools. The book is appropriate for people who want to practice <em>data</em> science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern <em>data</em> science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is Principal Data Scientist at Maana, Inc. where he applies Big Data tools to solve industrial problems. He has a BS in Physics from Stanford University, an MS in Applied Mathematics from the University of Washington, and an MS in Computer Science from Carnegie Mellon University. Table of Contents Chapter 1 Introduction: Becoming a Unicorn Part I The Stuff You’ll Always Use Chapter 2 The Data Science Road Map Chapter 3 Programming Languages Chapter 4 Data Munging: String Manipulation, Regular Expressions, and Data Cleaning Chapter 5 Visualizations and Simple Metrics Chapter 6 Machine Learning Overview Chapter 7 Interlude: Feature Extraction Ideas Chapter 8 Machine Learning Classification Chapter 9 Technical Communication and Documentation Part II Stuff You Still Need to Know Chapter 10 Unsupervised Learning: Clustering and Dimensionality Reduction Chapter 11 Regression Chapter 12 Data Encodings and File Formats Chapter 13 Big Data Chapter 14 Databases Chapter 15 Software Engineering Best Practices Chapter 16 Natural Language Processing Chapter 17 Time Series Analysis Chapter 18 Probability Chapter 19 Statistics Chapter 20 Programming Language Concepts Chapter 21 Performance and Computer Memory Part III Specialized or Advanced Topics Chapter 22 Computer Memory and Data Structures Chapter 23 Maximum Likelihood Estimation and Optimization Chapter 24 Advanced Classifiers Chapter 25 Stochastic Modeling Chapter 25a Parting Words: Your Future as a Data Scientist
Data Visualization: Principles and Practice
This book explores the study of processing and visually representing <em>data</em> sets. Data <em>visualization</em> is closely related to information <em>graphics</em>, information <em>visualization</em>, scientific <em>visualization</em>, and statistical <em>graphics</em>.
1929年大崩盘.The.Great.Crash.1929.pdf
The.Great.Crash.1929.<em>pdf</em>
ggplot2-3本神书打包下载
ggplot2_The Elements for Elegant Data Visulization in R.<em>pdf</em> ggplot2 - Elegant Graphics for Data Analysis.<em>pdf</em> R数据可视化手册 全部高清版PDF,打包<em>下载</em>
Fundamentals of Computer Graphics, 4th Edition 英文高清完整.pdf下载
Drawing on an impressive roster of experts in the field, Fundamentals of Computer Graphics, Fourth Edition offers an ideal resource for computer course curricula as well as a user-friendly personal or professional reference. Focusing on geometric intuition, the book gives the necessary information for understanding how images get onto the screen by using the complementary approaches of ray tracing and rasterization. It covers topics common to an introductory course, such as sampling theory, texture mapping, spatial <em>data</em> structure, and splines. It also includes a number of contributed chapters from authors known for their expertise and clear way of explaining concepts. Highlights of the Fourth Edition Include: Updated coverage of existing topics Major updates and improvements to several chapters, including texture mapping, <em>graphics</em> hardware, signal processing, and <em>data</em> structures A text now printed entirely in four-color to enhance illustrative figures of concepts The fourth edition of Fundamentals of Computer Graphics continues to provide an outstanding and comprehensive introduction to basic computer graphic technology and theory. It retains an informal and intuitive style while improving precision, consistency, and completeness of material, allowing aspiring and experienced <em>graphics</em> programmers to better understand and apply foundational principles to the development of efficient code in creating film, game, or web designs. Table of Contents Chapter 1: Introduction Chapter 2: Miscellaneous Math Chapter 3: Raster Images Chapter 4: Ray Tracing Chapter 5: Linear Algebra Chapter 6: Transformation Matrices Chapter 7: Viewing Chapter 8: The Graphics Pipeline Chapter 9: Signal Processing Chapter 10: Surface Shading Chapter 11: Texture Mapping Chapter 12: Data Structures for Graphics Chapter 13: More Ray Tracing Chapter 14: Sampling Chapter 15: Curves Chapter 16: Computer Animation Chapter 17: Using Graphics Hardware Chapter 18: Light Chapter 19: Color Chapter 20: Visual Perception Chapter 21: Tone Reproduction Chapter 22: Implicit Modeling Chapter 23: Global Illumination Chapter 24: Reflection Models Chapter 25: Computer Graphics in Games Chapter 26: Visualization Title: Fundamentals of Computer Graphics, 4th Edition Author: Peter Shirley, Steve Marschner Length: 748 pages Edition: 4 Language: English Publisher: A K Peters/CRC Press Publication Date: 2015-12-18 ISBN-10: 1482229390 ISBN-13: 9781482229394
SAS Programming I Essentials
SAS Programming I Essentials
Data mining concepts and techniques中文版.pdf
机器学习和数据挖掘方面的两本 世界名著 之一 本书从应用出发,原理非常完整。
R Graphics Cookbook 1st 原版PDF by Chang
I started using R several years ago to analyze <em>data</em> I had collected for my research in graduate school. My motivation at first was to escape from the restrictive environments and canned analyses offered by statistical programs like SPSS. And even better, because it’s freely available, I didn’t need to convince someone to buy me a copy of the software— very important for a poor graduate student! As I delved deeper into R, I discovered that it could also create excellent <em>data</em> <em>graphics</em>.
JavaScript and jQuery for Data Analysis and Visualization 无水印pdf
JavaScript and jQuery for Data Analysis and Visualization 英文无水印<em>pdf</em> <em>pdf</em>所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络,如有侵权,请联系上传者或csdn删除 本资源转载自网络,如有侵权,请联系上传者或csdn删除
ggplot2 Elegant Graphics for Data Analysis中文版,Springer出版
ggplot2 Elegant Graphics for Data Analysis 的中文版,全球首发,好东西只要3分,值不值自己看看哈!
R programing and Data Visualization
外国一个大学老师所作的关于R编程及可视化的快速入门文档,较为基础
Interactive Data Visualization for the Web(2nd) 无水印原版pdf
Interactive Data Visualization for the Web(2nd) 英文无水印原版<em>pdf</em> 第2版 <em>pdf</em>所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络,如有侵权,请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书
Multivariate Data Visualization with R
R软件包中lattice包的教程,内容为英文版,多元作图的好教材
R: Data Analysis and Visualization
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!
streaming system
streaming system <em>1st</em> Edition;streaming system <em>1st</em> Edition;
R_Graphics_Cookbook代码和数据.zip
R_Graphics_Cookbook代码和数据.zip
R Data Science Essentials
R Data Science Essentials - Raja B. Koushik
Visualization and Graphics pdf
Wolfram Mathematica Tutorial Collection Visualization and Grap Visualization and Graphics
R语言实战(中文完整版)
r in action <em>data</em> analysis and <em>graphics</em> with r
A Second Course in StatisticsRegression Analysis.pdf
Thistextisdesignedfortwotypesofstatisticscourses.Theearlychapters,combined with a selection of the case studies, are designed for use in the second half of a two-semester (two-quarter) introductory statistics sequence for undergraduates withstatisticsornonstatisticsmajors.Or,thetextcanbeusedforacourseinapplied regressionanalysis for masters or PhD students in other fields.
Computing Essentials 2017精校原版 (计算机专业英语 绝对经典)
Computing Essentials 2017精校原版 (计算机专业英语 绝对经典),<em>pdf</em>文件,压缩包112M,解压后134M
数据建模模式 - Patterns of Data Modeling
数据建模模式 - Patterns of Data Modeling 数据建模模式 - Patterns of Data Modeling
computing essentials 2017
Computing essential 原版PDF 2017年版本
IPython Interactive Computing and Visualization Cookbook-Second_Edition
IPython Interactive Computing and Visualization Cookbook-Second_Edition
data analysis and graphics using R
<em>data</em> analysis and <em>graphics</em> using R
LLVM Essentials
This book is intended for those who already know some of the concepts concerning compilers and want to quickly become familiar with LLVM’s infrastructure and the rich set of libraries that it provides. Compiler programmers, who are familiar with concepts of compilers and want to indulge in understanding, exploring, and using the LLVM infrastructure in a meaningful way in their work, will find this book useful. This book is also for programmers who are not directly involved in compiler projects but are often involved in development phases where they write thousands of lines of code. With knowledge of how compilers work, they will be able to code in an optimal way and improve performance with clean code.
The Essentials of Risk Management, 2nd Edition.pdf
The Essentials of Risk Management, 2nd Edition.<em>pdf</em>
R Machine Learning By Example
R Machine Learning By Example Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to make machine learning give them <em>data</em>driven insights to grow their businesses. With powerful <em>data</em> manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world <em>data</em> problems. This book takes you on a <em>data</em>-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems.
Odoo 11 Development Essentials(3rd) epub
Odoo 11 Development Essentials(3rd) 英文epub 第3版 本资源转载自网络,如有侵权,请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书
Spark-The Definitive Guide Big Data Processing Made Simple
Spark-The Definitive Guide Big Data Processing Made Simple 完美true <em>pdf</em>。 Apache Spark is a unified computing engine and a set of libraries for parallel <em>data</em> processing on computer clusters. As of this writing, Spark is the most actively developed open source engine for this task, making it a standard tool for any developer or <em>data</em> scientist interested in big <em>data</em>. Spark supports multiple widely used programming languages (Python, Java, Scala, and R), includes libraries for diverse tasks ranging from SQL to streaming and machine learning, and runs anywhere from a laptop to a cluster of thousands of servers. This makes it an easy system to start with and scale-up to big <em>data</em> processing or incredibly large scale.
ggplot2 Elegant Graphics for Data Analysis
ggplot2 Elegant Graphics for Data Analysis 是一本讲解R语言绘图中ggplot2语法经典图书
R for Data Science 原版PDF by Wickham & Grolemund
Data science is an exciting discipline that allows you to turn raw <em>data</em> into understanding, insight, and knowledge. The goal of R for Data Science is to help you learn the most important tools in R that will allow you to do <em>data</em> science. After reading this book, you’ll have the tools to tackle a wide variety of <em>data</em> science challenges, using the best parts of R.
[3rd] Essentials of Programming Languages.pdf 带书签
Essentials of Programming Languages 英文原书第三版,带书签版本。
R语言数据科学(r for data science 中文版)
r for <em>data</em> science 中文翻译版~
MQTT Essentials - A Lightweight IoT Protocol
<em>pdf</em> 文件 如涉及侵权内容,您的资源将被移除 * 请勿上传小说、mp3、图片等与技术无关的内容.一旦发现将被删除 * 请勿在未经授权的情况下上传任何涉及著作权侵权的资源,除非该资源完全由您个人创作 * 点击上传资源即表示您确认该资源不违反资源分享的使用条款,并且您拥有该资源的所有版权或者上传资源的授权
use R! ggplot2 Elegant Graphics for Data Analysis Second Edition
作者: Hadley Wickham 出版社: Springer 副标题: Second Edition 出版年: 2016-6-16 页数: 260 定价: USD 48.21
Deep Learning Essentials (2018)
If you are an aspiring <em>data</em> scientist, deep learning enthusiast, or AI researcher looking to build the power of deep learning to your business applications, then this book is the perfect resource for you to start addressing AI challenges. To get the most out of this book, you must have intermediate Python skills and be familiar with machine learning concepts well in advance.
McGraw-Hill-Computing_Essentials_2017计算机专业英语,国外原版,(第一卷,共三卷)
专业英语 McGraw-Hill-Computing_Essentials_2017计算机专业英语,国外原版
Interactive Data Visualization for the Web
Interactive Data Visualization for the Web
Python Data Visualization Cookbook 2nd 2015第2版 0分
Paperback: 302 pages Publisher: Packt Publishing - ebooks Account; 2nd Revised edition edition (November 30, 2015) Language: English ISBN-10: 1784396699 ISBN-13: 978-1784396695 Over 70 recipes to get you started with popular Python libraries based on the principal concepts of <em>data</em> <em>visualization</em> About This Book Learn how to set up an optimal Python environment for <em>data</em> <em>visualization</em> Understand how to import, clean and organize your <em>data</em> Determine different approaches to <em>data</em> <em>visualization</em> and how to choose the most appropriate for your needs
[KNIME] KNIME 核心技术 英文版
☆ 资源说明:☆ [Packt Publishing] KNIME 核心技术 英文版 [Packt Publishing] KNIME Essentials E Book ☆ 图书概要:☆ Perform accurate <em>data</em> analysis using the power of KNIME Overview Learn the <em>essentials</em> of KNIME from importing <em>data</em> to <em>data</em> <em>visualization</em> and reporting Utilize a wide range of <em>data</em> processing solutions Visualize your final <em>data</em> sets using KNIME’s powerful <em>data</em> <em>visualization</em> options ☆ 出版信息:☆ [作者信息] Gabor Bakos [出版机构] Packt Publishing [出版日期] 2013年10月16日 [图书页数] 148页 [图书语言] 英语 [图书格式] PDF 格式">☆ 资源说明:☆ [Packt Publishing] KNIME 核心技术 英文版 [Packt Publishing] KNIME Essentials E Book ☆ 图书概要:☆ Perform accurate <em>data</em> analysis using the power of KNIME Overview Learn the <em>essentials</em> of KNIME from importing <em>data</em> to <em>data</em> <em>visualization</em> and reporting U [更多]
Data Visualization with python 课程的pdf
Data Visualization with python 课程的<em>pdf</em> Data Visualization with python 课程的<em>pdf</em> IBM 数据可视化
Topological Data Analysis for Scientific Visualization 无水印原版pdf
Topological Data Analysis for Scientific Visualization 英文无水印原版<em>pdf</em> <em>pdf</em>所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络,如有侵权,请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书
ggplot2 Elegant Graphics for Data Analysis (use R)
ggplot2 Elegant Graphics for Data Analysis (use R)
Network Security Essentials Applications and Standards(英文版)总复习
网络安全基础应用与标准(英文版)期末总复习
android-studio-3-development-essentials2018
Fully updated for Android Studio 3.0 and Android 8, the goal of this book is to teach the skills necessary to develop Android based applications using the Android Studio Integrated Development Environment (IDE), the Android 8 Software Development Kit (SDK) and the Java programming language. Beginning with the basics, this book provides an outline of the steps necessary to set up an Android development and testing environment. An overview of Android Studio is included covering areas such as tool windows, the code editor and the Layout Editor tool. An introduction to the architecture of Android is followed by an in-depth look at the design of Android applications and user interfaces using the Android Studio environment. More advanced topics such as <em>data</em>base management, content providers and intents are also covered, as are touch screen handling, gesture recognition, camera access and the playback and recording of both video and audio. This edition of the book also covers printing, transitions and cloud-based file storage.
Data Visualization Principles and Practice, Second Edition
Data Visualization Principles and Practice, Second Edition
Hands-On Data Visualization with Bokeh
bokeh是python中一款基于网页的画图工具库,画出的图像以html格式保存。
Intel(R) HD Graphics 3000_9.17.10.3347
Intel(R) HD Graphics 3000_9.17.10.3347 驅動程式 ACRE-ASPIRE系列
Python Data Visualization Cookbook
1.Learn how to set up an optimal Python environment for <em>data</em> <em>visualization</em> 2.Understand the topics such as importing <em>data</em> for <em>visualization</em> and formatting <em>data</em> for <em>visualization</em> 3.Understand the underlying <em>data</em> and how to use the right <em>visualization</em>s
Level Up! The Guide to Great Video Game Design
Level Up covers the entire video game creation process Readers will learn how to develop marketable ideas, learn what perils and pitfalls await them during a game's pre-production, production and post-production stages Video games continue to be a growth market, with sustained growth since 2006 despite the economic downturn. 2008 was a record year for game sales. Expert author Scott Rogers is a veteran of the industry and has designed hits including God of War, the SpongeBob Squarepants game series, and the venerable Pac Man World. His recent talks at Game Developer Expo was featured on sites including BoingBoing, Digg, and Slashdot. Demand for game designers and developers has tripled since 2006 (indeed.com) It has a unique approach with Scott's tongue in cheek style, and "more practice, less theory" approach will appeal to today's readers who want to get started quickly.
ggplot2.Elegant.Graphics.for.Data.Analysis.2nd.Edition
This new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a <em>data</em> <em>visualization</em> package for R that helps users create <em>data</em> <em>graphics</em>, including those that are multi-layered, with ease. With ggplot2, it's easy to: produce handsome, publication-quality plots with automatic legends created from the plot specification superimpose multiple layers (points, lines, maps, tiles, box plots) from different <em>data</em> sources with automatically adjusted common scales add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression save any ggplot2 plot (or part thereof) for later modification or reuse create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots approach a graph from a visual perspective, thinking about how each component of the <em>data</em> is represented on the final plot This book will be useful to everyone who has struggled with displaying <em>data</em> in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing <em>data</em> into R). ggplot2 is a mini-language specifically tailored for producing <em>graphics</em>, and you'll learn everything you need in the book. After reading this book you'll be able to produce <em>graphics</em> customized precisely for your problems, and you'll find it easy to get <em>graphics</em> out of your head and on to the screen or page. Table of Contents Part I Getting Started Chapter 1 Introduction Chapter 2 Getting Started with ggplot2 Chapter 3 Toolbox Part II The Grammar Chapter 4 Mastering the Grammar Chapter 5 Build a Plot Layer by Layer Chapter 6 Scales, Axes and Legends Chapter 7 Positioning Chapter 8 Themes Part III Data Analysis Chapter 9 Data Analysis Chapter 10 Data Transformation Chapter 11 Modelling for Visualisation Chapter 12 Programming with ggplot2
the great gatsby pdf 扫描
the <em>great</em> gatsby <em>pdf</em> 扫描
Learn You a Haskell for Great Good.pdf
It's all in the name: Learn You a Haskell for Great Good! is a hilarious, illustrated guide to this complex functional language.
AWS Technical Essentials
AWS Technical Essentials Students Book AWS Technical Essentials Students BookAWS Technical Essentials Students BookAWS Technical Essentials Students Book
Deep Learning with R
读了用到项目实践中非常有收获!François Chollet的完整版!
R Graph Essentials
R Graph Essentials 优化<em>pdf</em>版
Android Studio 3.2 Development Essentials - Android 9 Edition Book-Preview
Android Studio 3.2 Development Essentials - Android 9 Edition Book-Preview共126页
Jqurey Easy UI 1.1.1 我修改后的下载
Jquery Easy Ui 是基于jquery框架的 常用ui 由于项目的需要我修改了calendar插件 将英文变为了中文,并将透明变为了不透明 而且还修改了 日期的输出格式 相关下载链接:[url=//download.csdn.net/download/xingshikk/2577274?utm_source=bbsseo]//download.csdn.net/download/xingshikk/2577274?utm_source=bbsseo[/url]
AutoCAD2004与AutoLISP二次开发技术2004(四 完)下载
用autolisp开发autocad的经典教程 相关下载链接:[url=//download.csdn.net/download/qhdbonu/2755765?utm_source=bbsseo]//download.csdn.net/download/qhdbonu/2755765?utm_source=bbsseo[/url]
同步电机机调速系统-图书下载
介绍同步电机调速控制原理,重点在于矢量控制在同步电机控制中的应用。 相关下载链接:[url=//download.csdn.net/download/chinascwangyi/3040033?utm_source=bbsseo]//download.csdn.net/download/chinascwangyi/3040033?utm_source=bbsseo[/url]
文章热词 机器学习教程 Objective-C培训 交互设计视频教程 颜色模型 设计制作学习
相关热词 mysql关联查询两次本表 native底部 react extjs glyph 图标 区块链1st价格 区块链教程pdf下载
我们是很有底线的