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.
Offered By
Johns Hopkins University
About this Course
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Learner Career Outcomes
38%
started a new career after completing these courses
38%
got a tangible career benefit from this course
15%
got a pay increase or promotion
What you will learn
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
Skills you will gain
Cluster AnalysisGgplot2R ProgrammingExploratory Data Analysis
Learner Career Outcomes
38%
started a new career after completing these courses
38%
got a tangible career benefit from this course
15%
got a pay increase or promotion
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Approx. 15 hours to complete
English
Subtitles: English, Korean, Vietnamese, Chinese (Simplified)
Syllabus - What you will learn from this course
20 hours to complete
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.
20 hours to complete
15 videos (Total 109 min), 6 readings, 7 quizzes
15 videos
Installing R on Windows (3.2.1)3m
Installing R on a Mac (3.2.1)1m
Installing R Studio (Mac)3m
Setting Your Working Directory (Windows)7m
Setting Your Working Directory (Mac)7m
Principles of Analytic Graphics12m
Exploratory Graphs (part 1)9m
Exploratory Graphs (part 2) 5m
Plotting Systems in R9m
Base Plotting System (part 1)11m
Base Plotting System (part 2)6m
Base Plotting Demonstration16m
Graphics Devices in R (part 1)5m
Graphics Devices in R (part 2)7m
6 readings
Welcome to Exploratory Data Analysis10m
Syllabus10m
Pre-Course Survey10m
Exploratory Data Analysis with R Book10m
The Art of Data Science10m
Practical R Exercises in swirl Part 110m
1 practice exercise
Week 1 Quiz20m
17 hours to complete
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 hours to complete
7 videos (Total 61 min), 1 reading, 6 quizzes
7 videos
Lattice Plotting System (part 2)6m
ggplot2 (part 1)6m
ggplot2 (part 2)13m
ggplot2 (part 3)9m
ggplot2 (part 4)10m
ggplot2 (part 5)8m
1 reading
Practical R Exercises in swirl Part 210m
1 practice exercise
Week 2 Quiz20m
13 hours to complete
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 hours to complete
12 videos (Total 77 min), 1 reading, 4 quizzes
12 videos
Hierarchical Clustering (part 2)5m
Hierarchical Clustering (part 3)7m
K-Means Clustering (part 1)5m
K-Means Clustering (part 2)4m
Dimension Reduction (part 1)7m
Dimension Reduction (part 2)9m
Dimension Reduction (part 3)6m
Working with Color in R Plots (part 1)4m
Working with Color in R Plots (part 2)7m
Working with Color in R Plots (part 3)6m
Working with Color in R Plots (part 4)3m
1 reading
Practical R Exercises in swirl Part 310m
6 hours to complete
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.
6 hours to complete
2 videos (Total 55 min), 2 readings, 2 quizzes
2 readings
Practical R Exercises in swirl Part 410m
Post-Course Survey10m
4.7
725 ReviewsTop reviews from Exploratory Data Analysis
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.
About Johns Hopkins University
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