This course covers the essential exploratory techniques for summarizing data. 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 data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.
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ジョンズ・ホプキンズ大学（Johns Hopkins University）このコースについて
70,150 最近の表示
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100%オンライン
自分のスケジュールですぐに学習を始めてください。
約55時間で修了
英語
字幕：アラビア語, フランス語, ポルトガル語（ヨーロッパ）, 中国語（簡体）, イタリア語, ベトナム語, 韓国語, ドイツ語, ロシア語, 英語, スペイン語
学習内容
Understand analytic graphics and the base plotting system in R
Use advanced graphing systems such as the Lattice system
Make graphical displays of very high dimensional data
Apply cluster analysis techniques to locate patterns in data
習得するスキル
Cluster AnalysisGgplot2R ProgrammingExploratory Data Analysis
柔軟性のある期限
スケジュールに従って期限をリセットします。
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
約55時間で修了
英語
字幕：アラビア語, フランス語, ポルトガル語（ヨーロッパ）, 中国語（簡体）, イタリア語, ベトナム語, 韓国語, ドイツ語, ロシア語, 英語, スペイン語
提供：
ジョンズ・ホプキンズ大学（Johns Hopkins University）
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
シラバス - 本コースの学習内容
19時間で修了
Week 1
This week covers the basics of analytic graphics and the base plotting system in R. We've also included some background material to help you install R if you haven't done so already.
19時間で修了
15件のビデオ （合計109分）, 6 学習用教材, 7 個のテスト
17時間で修了
Week 2
Welcome to Week 2 of Exploratory Data Analysis. This week covers some of the more advanced graphing systems available in R: the Lattice system and the ggplot2 system. While the base graphics system provides many important tools for visualizing data, it was part of the original R system and lacks many features that may be desirable in a plotting system, particularly when visualizing high dimensional data. The Lattice and ggplot2 systems also simplify the laying out of plots making it a much less tedious process.
17時間で修了
7件のビデオ （合計61分）, 1 学習用教材, 6 個のテスト
13時間で修了
Week 3
Welcome to Week 3 of Exploratory Data Analysis. This week covers some of the workhorse statistical methods for exploratory analysis. These methods include clustering and dimension reduction techniques that allow you to make graphical displays of very high dimensional data (many many variables). We also cover novel ways to specify colors in R so that you can use color as an important and useful dimension when making data graphics. All of this material is covered in chapters 9-12 of my book Exploratory Data Analysis with R.
13時間で修了
12件のビデオ （合計77分）, 1 学習用教材, 4 個のテスト
5時間で修了
Week 4
This week, we'll look at two case studies in exploratory data analysis. The first involves the use of cluster analysis techniques, and the second is a more involved analysis of some air pollution data. How one goes about doing EDA is often personal, but I'm providing these videos to give you a sense of how you might proceed with a specific type of dataset.
5時間で修了
2件のビデオ （合計55分）, 2 学習用教材, 2 個のテスト
レビュー
探索的データ解析 からの人気レビュー
Yにより2017年9月23日
Very good course! It provide me the foundation in learning how to plot and interpret data. This will definitely strengthen my "R programming" to generate publication type figure for my genomics data!
CCにより2016年7月28日
This is the second course I have taken from Roger Peng and both were outstanding. I have a strong math background, but not much of a background in stats, but this course was very approachable for me.
BSにより2017年6月20日
The course on Exploratory Data Analysis was highly enjoyable. I used to do a lot of this sort of thing in my job, but now spend more of my time managing people. It is fun to get "hands-on" again.
IAにより2016年1月17日
Very nice course, plotting data to explore and understand various features and their relationship is the key in any research domain, and this course teaches the skill required to achieve this.
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