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.
課程信息
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您將學到的內容有
Apply cluster analysis techniques to locate patterns in data
Make graphical displays of very high dimensional data
Understand analytic graphics and the base plotting system in R
Use advanced graphing systems such as the Lattice system
您將獲得的技能
Cluster AnalysisGgplot2R ProgrammingExploratory Data Analysis
教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 20 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.
15 個視頻 （總計 109 分鐘）, 6 個閱讀材料, 7 個測驗
15 個視頻
Introduction1分鐘
Installing R on Windows (3.2.1)3分鐘
Installing R on a Mac (3.2.1)1分鐘
Installing R Studio (Mac)3分鐘
Setting Your Working Directory (Windows)7分鐘
Setting Your Working Directory (Mac)7分鐘
Principles of Analytic Graphics12分鐘
Exploratory Graphs (part 1)9分鐘
Exploratory Graphs (part 2) 5分鐘
Plotting Systems in R9分鐘
Base Plotting System (part 1)11分鐘
Base Plotting System (part 2)6分鐘
Base Plotting Demonstration16分鐘
Graphics Devices in R (part 1)5分鐘
Graphics Devices in R (part 2)7分鐘
6 個閱讀材料
Welcome to Exploratory Data Analysis10分鐘
Syllabus10分鐘
Pre-Course Survey10分鐘
Exploratory Data Analysis with R Book10分鐘
The Art of Data Science10分鐘
Practical R Exercises in swirl Part 110分鐘
1 個練習
Week 1 Quiz20分鐘
週
2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.
7 個視頻 （總計 61 分鐘）, 1 個閱讀材料, 6 個測驗
7 個視頻
Lattice Plotting System (part 2)6分鐘
ggplot2 (part 1)6分鐘
ggplot2 (part 2)13分鐘
ggplot2 (part 3)9分鐘
ggplot2 (part 4)10分鐘
ggplot2 (part 5)8分鐘
1 個閱讀材料
Practical R Exercises in swirl Part 210分鐘
1 個練習
Week 2 Quiz20分鐘
週
3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.
12 個視頻 （總計 77 分鐘）, 1 個閱讀材料, 4 個測驗
12 個視頻
Hierarchical Clustering (part 2)5分鐘
Hierarchical Clustering (part 3)7分鐘
K-Means Clustering (part 1)5分鐘
K-Means Clustering (part 2)4分鐘
Dimension Reduction (part 1)7分鐘
Dimension Reduction (part 2)9分鐘
Dimension Reduction (part 3)6分鐘
Working with Color in R Plots (part 1)4分鐘
Working with Color in R Plots (part 2)7分鐘
Working with Color in R Plots (part 3)6分鐘
Working with Color in R Plots (part 4)3分鐘
1 個閱讀材料
Practical R Exercises in swirl Part 310分鐘
週
4完成時間為 6 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.
2 個視頻 （總計 55 分鐘）, 2 個閱讀材料, 2 個測驗
2 個閱讀材料
Practical R Exercises in swirl Part 410分鐘
Post-Course Survey10分鐘
4.7
685 個審閱38%
完成這些課程後已開始新的職業生涯
38%
通過此課程獲得實實在在的工作福利
14%
加薪或升職
來自探索性数据分析的熱門評論
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!
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.
講師
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
關於 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.
關於 数据科学 專項課程
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
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