Exploratory Data Analysis

About this course: 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.

Created by:  Johns Hopkins University

• Taught by:  Roger D. Peng, PhD, Associate Professor, Biostatistics

  Bloomberg School of Public Health

• Taught by:  Jeff Leek, PhD, Associate Professor, Biostatistics

  Bloomberg School of Public Health

• Taught by:  Brian Caffo, PhD, Professor, Biostatistics

  Bloomberg School of Public Health

Basic Info Course 4 of 10 in the Data Science Specialization Language English, Subtitles: Chinese (Simplified) How To Pass Pass all graded assignments to complete the course. User Ratings 4.6 stars Average User Rating 4.6See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Syllabus


WEEK 1


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, 6 readings expand

1. Reading: Welcome to Exploratory Data Analysis
2. Reading: Syllabus
3. Reading: Pre-Course Survey
4. Video: Introduction
5. Reading: Exploratory Data Analysis with R Book
6. Reading: The Art of Data Science
7. Video: Installing R on Windows (3.2.1)
8. Video: Installing R on a Mac (3.2.1)
9. Video: Installing R Studio (Mac)
10. Video: Setting Your Working Directory (Windows)
11. Video: Setting Your Working Directory (Mac)
12. Video: Principles of Analytic Graphics
13. Video: Exploratory Graphs (part 1)
14. Video: Exploratory Graphs (part 2)
15. Video: Plotting Systems in R
16. Video: Base Plotting System (part 1)
17. Video: Base Plotting System (part 2)
18. Video: Base Plotting Demonstration
19. Video: Graphics Devices in R (part 1)
20. Video: Graphics Devices in R (part 2)
21. Reading: Practical R Exercises in swirl Part 1
22. Ungraded Programming: swirl Lesson 1: Principles of Analytic Graphs
23. Ungraded Programming: swirl Lesson 2: Exploratory Graphs
24. Ungraded Programming: swirl Lesson 3: Graphics Devices in R
25. Ungraded Programming: swirl Lesson 4: Plotting Systems
26. Ungraded Programming: swirl Lesson 5: Base Plotting System

Graded: Week 1 Quiz
Graded: Course Project 1

WEEK 2


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, 1 reading expand

1. Video: Lattice Plotting System (part 1)
2. Video: Lattice Plotting System (part 2)
3. Video: ggplot2 (part 1)
4. Video: ggplot2 (part 2)
5. Video: ggplot2 (part 3)
6. Video: ggplot2 (part 4)
7. Video: ggplot2 (part 5)
8. Reading: Practical R Exercises in swirl Part 2
9. Ungraded Programming: swirl Lesson 1: Lattice Plotting System
10. Ungraded Programming: swirl Lesson 2: Working with Colors
11. Ungraded Programming: swirl Lesson 3: GGPlot2 Part1
12. Ungraded Programming: swirl Lesson 4: GGPlot2 Part2
13. Ungraded Programming: swirl Lesson 5: GGPlot2 Extras

Graded: Week 2 Quiz

WEEK 3


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, 1 reading expand

1. Video: Hierarchical Clustering (part 1)
2. Video: Hierarchical Clustering (part 2)
3. Video: Hierarchical Clustering (part 3)
4. Video: K-Means Clustering (part 1)
5. Video: K-Means Clustering (part 2)
6. Video: Dimension Reduction (part 1)
7. Video: Dimension Reduction (part 2)
8. Video: Dimension Reduction (part 3)
9. Video: Working with Color in R Plots (part 1)
10. Video: Working with Color in R Plots (part 2)
11. Video: Working with Color in R Plots (part 3)
12. Video: Working with Color in R Plots (part 4)
13. Reading: Practical R Exercises in swirl Part 3
14. Ungraded Programming: swirl Lesson 1: Hierarchical Clustering
15. Ungraded Programming: swirl Lesson 2: K Means Clustering
16. Ungraded Programming: swirl Lesson 3: Dimension Reduction
17. Ungraded Programming: swirl Lesson 4: Clustering Example

WEEK 4


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, 2 readings expand

1. Video: Clustering Case Study
2. Video: Air Pollution Case Study
3. Reading: Practical R Exercises in swirl Part 4
4. Ungraded Programming: swirl Lesson 1: CaseStudy
5. Reading: Post-Course Survey

Graded: Course Project 2