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
86,918 recent views
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
Syllabus - What you will learn from this course
Week
120 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.
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
Week
217 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.
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
Week
313 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.
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
Week
46 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.
2 videos (Total 55 min), 2 readings, 2 quizzes
2 readings
Practical R Exercises in swirl Part 410m
Post-Course Survey10m
4.7
696 Reviews39%
started a new career after completing these courses
39%
got a tangible career benefit from this course
14%
got a pay increase or promotion
Top 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.
Instructors
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
About 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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.
Coursera provides universal access to the world’s best education,
partnering with top universities and organizations to offer courses online.
© 2019 Coursera Inc. All rights reserved.
