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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约翰霍普金斯大学課程信息
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100% 在線
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完成時間大約為55 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 中文（簡體）, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
您將學到的內容有
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
您將獲得的技能
- Cluster Analysis
- Ggplot2
- R Programming
- Exploratory Data Analysis
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
完成時間大約為55 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 中文（簡體）, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
提供方
约翰霍普金斯大学
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.
授課大綱 - 您將從這門課程中學到什麼
完成時間為 19 小時
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.
完成時間為 19 小時
15 個視頻 （總計 109 分鐘）, 6 個閱讀材料, 7 個測驗
完成時間為 17 小時
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.
完成時間為 17 小時
7 個視頻 （總計 61 分鐘）, 1 個閱讀材料, 6 個測驗
完成時間為 13 小時
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.
完成時間為 13 小時
12 個視頻 （總計 77 分鐘）, 1 個閱讀材料, 4 個測驗
完成時間為 5 小時
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.
完成時間為 5 小時
2 個視頻 （總計 55 分鐘）, 2 個閱讀材料, 2 個測驗
審閱
來自探索性数据分析的熱門評論
由 Y 提供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!
由 CC 提供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.
由 BS 提供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.
由 IA 提供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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