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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課程信息
對員工進行熱門技能培訓能否為您的公司帶來益處?
體驗 Coursera 企業版您將學到的內容有
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
對員工進行熱門技能培訓能否為您的公司帶來益處?
體驗 Coursera 企業版提供方
授課大綱 - 您將從這門課程中學到什麼
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審閱
- 5 stars74.15%
- 4 stars21.25%
- 3 stars3.40%
- 2 stars0.74%
- 1 star0.43%
來自探索性数据分析的熱門評論
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.
I did learn more about putting together a set of graphs that help to explore the data. I did see how subsetting and aggregating data helps to give a better understanding of the data.
When it comes to hierarchical and K-means clustering, the theory wasn't explained clearly. When do we use U and V for what purpose? How does D come in? I'm left confused after this.
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!
常見問題
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