Johns Hopkins University

Exploratory Data Analysis

This course is part of multiple programs.

Roger D. Peng, PhD
Jeff Leek, PhD
Brian Caffo, PhD

Instructors: Roger D. Peng, PhD

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4.7

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54 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • 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

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Assessments

2 quizzes

Taught in English

Course

Gain insight into a topic and learn the fundamentals

4.7

(6,063 reviews)

|

94%

54 hours (approximately)
Flexible schedule
Learn at your own pace

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There are 4 modules in this course

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.

What's included

15 videos6 readings1 quiz5 programming assignments1 peer review

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.

What's included

7 videos1 reading1 quiz5 programming assignments

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.

What's included

12 videos1 reading4 programming assignments

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.

What's included

2 videos2 readings1 programming assignment1 peer review

Instructors

Instructor ratings
4.7 (205 ratings)
Roger D. Peng, PhD
Johns Hopkins University
37 Courses1,587,611 learners

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