標(biāo)題: Titlebook: Beginning Data Science in R; Data Analysis, Visua Thomas Mailund Book 20171st edition Thomas Mailund 2017 R.programming.statistics.data sci [打印本頁(yè)] 作者: Encounter 時(shí)間: 2025-3-21 16:23
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書(shū)目名稱(chēng)Beginning Data Science in R讀者反饋
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作者: 聾子 時(shí)間: 2025-3-21 21:48
Information Processing in The Nervous Systemnal programming and object oriented programming features. Learning the language is far beyond the scope of this chapter and is something we return to later. The good news, though, is that to use R for data analysis, you rarely need to do much programming. At least, if you do the right kind of progra作者: Mobile 時(shí)間: 2025-3-22 01:56
https://doi.org/10.1007/978-3-662-25549-0ses, written in various scripts, perhaps saving some intermediate results along the way or maybe always working on the raw data. You create some plots or tables of relevant summaries of the data, and then you go and write a report about the results in a text editor or word processor. It is the typic作者: chapel 時(shí)間: 2025-3-22 05:07
Information Processing in The Nervous Systeme statistical models or machine learning algorithms we want to analyze them with. The first stages of data analysis are almost always figuring out how to load the data into R and then figuring out how to transform it into a shape you can readily analyze. The code in this chapter, and all the followi作者: byline 時(shí)間: 2025-3-22 10:17
Information Processing in The Nervous Systemking prediction models. Sometimes we are just trying to find out what structure is actually in the data we analyze. There can be several reasons for this. Sometimes unknown structures can tell us more about the data. Sometimes we want to explicitly avoid an unknown structure (if we have datasets tha作者: 留戀 時(shí)間: 2025-3-22 15:59 作者: 小臼 時(shí)間: 2025-3-22 17:08
https://doi.org/10.1007/978-3-642-87086-6p of the quick introduction you got in the last chapter. Except, perhaps, for the functional programming toward the end, we will not cover anything that is conceptually more complex that we did in the previous chapter. It is just a few more technical details we will dig into.作者: cataract 時(shí)間: 2025-3-22 22:33
Ad Aertsen,Valentino Braitenbergth a couple of chosen parameters, but to build robust software you need to approach testing more rigorously. And to prevent bugs from creeping into your code over time, you should test often. Ideally, you should check all your code anytime you make any changes to it.作者: prosperity 時(shí)間: 2025-3-23 05:09 作者: vector 時(shí)間: 2025-3-23 07:14
Information Processing in The Nervous SystemNothing really tells a story about your data as powerfully as good plots. Graphics capture your data much better than summary statistics and often show you features that you would not be able to glean from summaries alone.作者: 先鋒派 時(shí)間: 2025-3-23 10:20 作者: ELUC 時(shí)間: 2025-3-23 14:06
Ignatius G. Mattingly,Alvin M. LibermanThis chapter and the next concern the mathematical modeling of data that is the essential core of data science. We can call this statistics, or we can call it machine learning. At its core, it is the same thing. It is all about extracting information out of data.作者: 沉思的魚(yú) 時(shí)間: 2025-3-23 19:22
Information Processing in The Nervous SystemThis chapter looks at R’s flavor of object oriented programming. Actually, R has three different systems for object oriented programming: S3, S4, and RC. We will only look at S3, which is the simplest and (I believe) the most widely used.作者: 注意到 時(shí)間: 2025-3-24 00:23 作者: 逗留 時(shí)間: 2025-3-24 04:30 作者: 騙子 時(shí)間: 2025-3-24 08:37 作者: myriad 時(shí)間: 2025-3-24 11:19
Working with Large Datasets,The concept of Big Data refers to very large datasets, sets of sizes where you need data warehouses to store the data, where you typically need sophisticated algorithms to handle the data, and distributed computations to get anywhere with it. At the very least, we talk many gigabytes of data but also are often dealing with terabytes or exabytes.作者: 留戀 時(shí)間: 2025-3-24 17:19 作者: Nefarious 時(shí)間: 2025-3-24 22:45
Object Oriented Programming,This chapter looks at R’s flavor of object oriented programming. Actually, R has three different systems for object oriented programming: S3, S4, and RC. We will only look at S3, which is the simplest and (I believe) the most widely used.作者: Cloudburst 時(shí)間: 2025-3-25 02:42
Building an R Package,You now know how to write functions and create classes in R, but neither functions nor classes is the unit you use for collecting and distributing R code. That unit is the package.作者: 潰爛 時(shí)間: 2025-3-25 05:47
Profiling and Optimizing,In this last chapter, we briefly consider what to do when you find that your code is running too slow, and, in particular, how to figure out why it is running too slow.作者: nonchalance 時(shí)間: 2025-3-25 08:23
Introduction to R Programming,nal programming and object oriented programming features. Learning the language is far beyond the scope of this chapter and is something we return to later. The good news, though, is that to use R for data analysis, you rarely need to do much programming. At least, if you do the right kind of programming, you won’t need much.作者: BIPED 時(shí)間: 2025-3-25 14:28 作者: 鍵琴 時(shí)間: 2025-3-25 16:29 作者: achlorhydria 時(shí)間: 2025-3-25 22:34 作者: Altitude 時(shí)間: 2025-3-26 02:32 作者: 車(chē)床 時(shí)間: 2025-3-26 07:30
http://image.papertrans.cn/b/image/182295.jpg作者: 芳香一點(diǎn) 時(shí)間: 2025-3-26 08:28
Information Processing in The Nervous Systemnal programming and object oriented programming features. Learning the language is far beyond the scope of this chapter and is something we return to later. The good news, though, is that to use R for data analysis, you rarely need to do much programming. At least, if you do the right kind of programming, you won’t need much.作者: Migratory 時(shí)間: 2025-3-26 15:17 作者: esculent 時(shí)間: 2025-3-26 20:16 作者: Concrete 時(shí)間: 2025-3-26 22:00 作者: 失眠癥 時(shí)間: 2025-3-27 03:18 作者: 熱情贊揚(yáng) 時(shí)間: 2025-3-27 08:32
Reproducible Analysis,ses, written in various scripts, perhaps saving some intermediate results along the way or maybe always working on the raw data. You create some plots or tables of relevant summaries of the data, and then you go and write a report about the results in a text editor or word processor. It is the typic作者: 比賽用背帶 時(shí)間: 2025-3-27 10:36
Data Manipulation,e statistical models or machine learning algorithms we want to analyze them with. The first stages of data analysis are almost always figuring out how to load the data into R and then figuring out how to transform it into a shape you can readily analyze. The code in this chapter, and all the followi作者: 相同 時(shí)間: 2025-3-27 14:08
Unsupervised Learning,king prediction models. Sometimes we are just trying to find out what structure is actually in the data we analyze. There can be several reasons for this. Sometimes unknown structures can tell us more about the data. Sometimes we want to explicitly avoid an unknown structure (if we have datasets tha作者: Nonflammable 時(shí)間: 2025-3-27 18:06 作者: 半導(dǎo)體 時(shí)間: 2025-3-27 22:14
Advanced R Programming,p of the quick introduction you got in the last chapter. Except, perhaps, for the functional programming toward the end, we will not cover anything that is conceptually more complex that we did in the previous chapter. It is just a few more technical details we will dig into.作者: 小鹿 時(shí)間: 2025-3-28 03:15
Testing and Package Checking,th a couple of chosen parameters, but to build robust software you need to approach testing more rigorously. And to prevent bugs from creeping into your code over time, you should test often. Ideally, you should check all your code anytime you make any changes to it.作者: Constrain 時(shí)間: 2025-3-28 10:19 作者: 引水渠 時(shí)間: 2025-3-28 10:37
Book 20171st editionrn.Perform data science and analytics using statistics and the R programming language.Visualize and explore data, including working with large data sets found in big data.Build an R package.Test and check your code.Practice version control.Profile and optimize your code.Who This Book Is For.Those wi作者: Breach 時(shí)間: 2025-3-28 14:40
ng.?.What You Will Learn.Perform data science and analytics using statistics and the R programming language.Visualize and explore data, including working with large data sets found in big data.Build an R package.Test and check your code.Practice version control.Profile and optimize your code.Who This Book Is For.Those wi978-1-4842-2671-1作者: 止痛藥 時(shí)間: 2025-3-28 22:17 作者: colony 時(shí)間: 2025-3-29 01:30
cessful lecture seriesDiscover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software package作者: 谷類(lèi) 時(shí)間: 2025-3-29 04:06 作者: Binge-Drinking 時(shí)間: 2025-3-29 07:46
Information Processing in The Nervous System to load the data into R and then figuring out how to transform it into a shape you can readily analyze. The code in this chapter, and all the following, assumes that the packages magrittr and ggplot2 have been loaded (just to avoid explicitly doing so in each example).作者: Tremor 時(shí)間: 2025-3-29 11:57 作者: Oversee 時(shí)間: 2025-3-29 19:16
Single Cells versus Neuronal Assembliesntrol systems, Subversion and git. Of these, git is the most widely used, and although these things are very subjective of course, I think that it is also the better system. It is certainly the system we use here.作者: 夾克怕包裹 時(shí)間: 2025-3-29 21:16 作者: 輕推 時(shí)間: 2025-3-30 01:42 作者: colony 時(shí)間: 2025-3-30 04:48
Unsupervised Learning,his. Sometimes unknown structures can tell us more about the data. Sometimes we want to explicitly avoid an unknown structure (if we have datasets that are supposed to be similar, we don’t want to discover later that there are systematic differences). Whatever the reason, unsupervised learning concerns finding unknown structures in data.作者: 污點(diǎn) 時(shí)間: 2025-3-30 08:33
Version Control,ntrol systems, Subversion and git. Of these, git is the most widely used, and although these things are very subjective of course, I think that it is also the better system. It is certainly the system we use here.
