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Titlebook: Beginning Data Science in R; Data Analysis, Visua Thomas Mailund Book 20171st edition Thomas Mailund 2017 R.programming.statistics.data sci

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发表于 2025-3-21 16:23:34 | 显示全部楼层 |阅读模式
期刊全称Beginning Data Science in R
期刊简称Data Analysis, Visua
影响因子2023Thomas Mailund
视频video
发行地址Gives you everything you need to know to get started in data science and R programming.A unique book by a data science expert.Based on a successful lecture series
图书封面Titlebook: Beginning Data Science in R; Data Analysis, Visua Thomas Mailund Book 20171st edition Thomas Mailund 2017 R.programming.statistics.data sci
影响因子Discover 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 packages for R..Beginning Data Science in R. details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. .This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. .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 wi
Pindex Book 20171st edition
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发表于 2025-3-21 21:48:51 | 显示全部楼层
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
发表于 2025-3-22 01:56:07 | 显示全部楼层
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
发表于 2025-3-22 05:07:12 | 显示全部楼层
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
发表于 2025-3-22 10:17:28 | 显示全部楼层
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
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发表于 2025-3-22 17:08:51 | 显示全部楼层
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.
发表于 2025-3-22 22:33:35 | 显示全部楼层
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.
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发表于 2025-3-23 07:14:56 | 显示全部楼层
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.
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