標(biāo)題: Titlebook: Beginning Data Science in R 4; Data Analysis, Visua Thomas Mailund Book 2022Latest edition Thomas Mailund 2022 R.programming.statistics.dat [打印本頁(yè)] 作者: Daguerreotype 時(shí)間: 2025-3-21 18:32
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書(shū)目名稱Beginning Data Science in R 4影響因子(影響力)學(xué)科排名
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書(shū)目名稱Beginning Data Science in R 4網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
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書(shū)目名稱Beginning Data Science in R 4讀者反饋學(xué)科排名
作者: Mobile 時(shí)間: 2025-3-21 22:23
Mandayam A. Srinivasan,Robert H. LaMotte later, when we have a little more experience using R. The good news is, though, that to use R for data analysis, we rarely need to do much programming. At least, if you do the right kind of programming, you won’t need much.作者: 乳汁 時(shí)間: 2025-3-22 03:51 作者: 編輯才信任 時(shí)間: 2025-3-22 08:24 作者: 打擊 時(shí)間: 2025-3-22 12:16 作者: 憤世嫉俗者 時(shí)間: 2025-3-22 15:58 作者: Consequence 時(shí)間: 2025-3-22 20:50
Introduction to R Programming, later, when we have a little more experience using R. The good news is, though, that to use R for data analysis, we rarely need to do much programming. At least, if you do the right kind of programming, you won’t need much.作者: Mortal 時(shí)間: 2025-3-23 00:39 作者: 夜晚 時(shí)間: 2025-3-23 03:29 作者: FRAUD 時(shí)間: 2025-3-23 08:10 作者: 美食家 時(shí)間: 2025-3-23 10:18 作者: 地名表 時(shí)間: 2025-3-23 16:17
Book 2022Latest editionthe R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R.?.Beginning Data Science in R 4, Second Edition.?details how data science is a combination of statistics, computational science, and 作者: metropolitan 時(shí)間: 2025-3-23 20:32
Introduction to R Programming,onal programming and object-oriented programming features, and learning the complete language is far beyond the scope of this chapter. We return to it later, when we have a little more experience using R. The good news is, though, that to use R for data analysis, we rarely need to do much programmin作者: Irrigate 時(shí)間: 2025-3-23 23:20
Reproducible Analysis,yses, 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. This is the ty作者: 夜晚 時(shí)間: 2025-3-24 03:59 作者: PHONE 時(shí)間: 2025-3-24 08:11
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 unknown structures (if we have datasets th作者: 故意釣到白楊 時(shí)間: 2025-3-24 12:01 作者: 充氣球 時(shí)間: 2025-3-24 17:42 作者: Hypomania 時(shí)間: 2025-3-24 20:14 作者: prolate 時(shí)間: 2025-3-25 01:10 作者: 誹謗 時(shí)間: 2025-3-25 06:30
Project 2: Bayesian Linear Regression, could imagine we could build an R package for, and the goal is not to develop all the bells and whistles of Bayesian linear regression. We will just build enough to see the various aspects that go into building a real R package.作者: acrimony 時(shí)間: 2025-3-25 07:32 作者: 固定某物 時(shí)間: 2025-3-25 13:09 作者: nocturnal 時(shí)間: 2025-3-25 18:30
Definition of a Measure in Hubert Space,th a couple of chosen parameters, but to build robust software, you will 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 have made any changes to it.作者: ALLEY 時(shí)間: 2025-3-25 22:50
Rechtliche Grundlagen der Integration could imagine we could build an R package for, and the goal is not to develop all the bells and whistles of Bayesian linear regression. We will just build enough to see the various aspects that go into building a real R package.作者: Antarctic 時(shí)間: 2025-3-26 02:50 作者: NIB 時(shí)間: 2025-3-26 04:53 作者: fodlder 時(shí)間: 2025-3-26 09:31
J. A. Campos-Ortega,N. J. StrausfeldThis chapter and the next concern the mathematical modelling of data that is the essential core of data science. We can call this statistics, or we can call it machine learning. At its heart, it is the same thing. It is all about extracting information out of data.作者: 拉開(kāi)這車床 時(shí)間: 2025-3-26 14:36
N. J. Strausfeld,J. A. Campos-OrtegaTo see a data analysis in action, I will use an analysis that my student, Dan S?ndergaard, did the first year I held my data science class. I liked his analysis so much that I wanted to include it in the book. I am redoing his analysis in the following with his permission.作者: 哭得清醒了 時(shí)間: 2025-3-26 19:52
J. A. Becerra,J. Santos,R.J. DuroIn this chapter, we explore working with vectors and lists a little further. 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: 非實(shí)體 時(shí)間: 2025-3-27 00:02 作者: mastoid-bone 時(shí)間: 2025-3-27 04:11 作者: 采納 時(shí)間: 2025-3-27 06:00 作者: 退潮 時(shí)間: 2025-3-27 12:57
Visualizing Data,Nothing tells a story about your data as powerfully as good plots. Graphics captures your data much better than summary statistics and often shows you features that you would not be able to glean from summaries alone.作者: LATER 時(shí)間: 2025-3-27 17:37
Working with Large Data Sets,The concept of Big Data refers to enormous data sets, sets of sizes where you need data warehouses to store it, 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 often terabytes or exabytes.作者: 你敢命令 時(shí)間: 2025-3-27 20:02 作者: Parameter 時(shí)間: 2025-3-27 22:20
Project 1: Hitting the Bottle,To see a data analysis in action, I will use an analysis that my student, Dan S?ndergaard, did the first year I held my data science class. I liked his analysis so much that I wanted to include it in the book. I am redoing his analysis in the following with his permission.作者: 商品 時(shí)間: 2025-3-28 04:23
Working with Vectors and Lists,In this chapter, we explore working with vectors and lists a little further. 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: pellagra 時(shí)間: 2025-3-28 08:47
Object-Oriented Programming,In this chapter, we look 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 (is my impression) the most widely used.作者: NADIR 時(shí)間: 2025-3-28 10:24
Building an R Package,Now we know how to write functions and create classes in R, but neither functions nor classes are the unit we use for collecting and distributing R code. That unit is the package. It is packages you load and import into your namespace when you write..作者: inveigh 時(shí)間: 2025-3-28 17:30
Profiling and Optimizing,In this last chapter, we will 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: Processes 時(shí)間: 2025-3-28 22:12
https://doi.org/10.1007/978-1-4842-8155-0R; programming; statistics; data science; big data; machine learning; deep learning; ai; cloud; analytics; cod作者: 后來(lái) 時(shí)間: 2025-3-28 23:14
Mandayam A. Srinivasan,Robert H. LaMotteonal programming and object-oriented programming features, and learning the complete language is far beyond the scope of this chapter. We return to it later, when we have a little more experience using R. The good news is, though, that to use R for data analysis, we rarely need to do much programmin作者: 憤怒事實(shí) 時(shí)間: 2025-3-29 04:09 作者: 不可接觸 時(shí)間: 2025-3-29 11:00 作者: 吸引人的花招 時(shí)間: 2025-3-29 13:10
N. J. Strausfeld,J. A. Campos-Ortegaking 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 unknown structures (if we have datasets th作者: 小步走路 時(shí)間: 2025-3-29 17:42
Molecular Computing by Signaling Pathways, book. In the first chapter, you took a tutorial introduction to R programming, but we left out a lot of details. This chapter will cover many of those details, while the next two chapters will cover more advanced aspects of R programming: functional programming and object-oriented programming.作者: 卷發(fā) 時(shí)間: 2025-3-29 21:46
J. A. Becerra,J. Santos,R.J. Duros for a language to be a functional programming language, and there have been many language wars over whether any given feature is “pure” or not. I won’t go into such discussions, but some features, I think everyone would agree, are needed. You should be able to define higher-order functions, you sh作者: Flagging 時(shí)間: 2025-3-30 00:33 作者: 符合國(guó)情 時(shí)間: 2025-3-30 05:06
Absolute Continuity of Measures,ment since it often allows several developers to make changes to the software and merge it with changes from other developers. RStudio supports two version control systems, Subversion and git. Of these, git is the most widely used, and although these things are very subjective of course, I think tha作者: 人工制品 時(shí)間: 2025-3-30 08:37
Rechtliche Grundlagen der Integration could imagine we could build an R package for, and the goal is not to develop all the bells and whistles of Bayesian linear regression. We will just build enough to see the various aspects that go into building a real R package.作者: 營(yíng)養(yǎng) 時(shí)間: 2025-3-30 14:34 作者: absorbed 時(shí)間: 2025-3-30 17:02
http://image.papertrans.cn/b/image/182296.jpg作者: 的事物 時(shí)間: 2025-3-30 20:41
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.作者: grounded 時(shí)間: 2025-3-31 01:28
Deeper into R Programming, book. In the first chapter, you took a tutorial introduction to R programming, but we left out a lot of details. This chapter will cover many of those details, while the next two chapters will cover more advanced aspects of R programming: functional programming and object-oriented programming.作者: 有限 時(shí)間: 2025-3-31 08:41
Testing and Package Checking,th a couple of chosen parameters, but to build robust software, you will 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 have made any changes to it.作者: Binge-Drinking 時(shí)間: 2025-3-31 12:42
Project 2: Bayesian Linear Regression, could imagine we could build an R package for, and the goal is not to develop all the bells and whistles of Bayesian linear regression. We will just build enough to see the various aspects that go into building a real R package.作者: Decline 時(shí)間: 2025-3-31 14:12
ble at github.com/Apress/beg-data-science-r4..What You Will Learn.Perform data science and analytics using statistics and the R programming language.Visualize and explore data, including working with large data978-1-4842-8154-3978-1-4842-8155-0作者: ORBIT 時(shí)間: 2025-3-31 19:12
