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 |
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
- Reading: Welcome to Exploratory Data Analysis
- Reading: Syllabus
- Reading: Pre-Course Survey
- Video: Introduction
- Reading: Exploratory Data Analysis with R Book
- Reading: The Art of Data Science
- Video: Installing R on Windows (3.2.1)
- Video: Installing R on a Mac (3.2.1)
- Video: Installing R Studio (Mac)
- Video: Setting Your Working Directory (Windows)
- Video: Setting Your Working Directory (Mac)
- Video: Principles of Analytic Graphics
- Video: Exploratory Graphs (part 1)
- Video: Exploratory Graphs (part 2)
- Video: Plotting Systems in R
- Video: Base Plotting System (part 1)
- Video: Base Plotting System (part 2)
- Video: Base Plotting Demonstration
- Video: Graphics Devices in R (part 1)
- Video: Graphics Devices in R (part 2)
- Reading: Practical R Exercises in swirl Part 1
- Ungraded Programming: swirl Lesson 1: Principles of Analytic Graphs
- Ungraded Programming: swirl Lesson 2: Exploratory Graphs
- Ungraded Programming: swirl Lesson 3: Graphics Devices in R
- Ungraded Programming: swirl Lesson 4: Plotting Systems
- 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
- Video: Lattice Plotting System (part 1)
- Video: Lattice Plotting System (part 2)
- Video: ggplot2 (part 1)
- Video: ggplot2 (part 2)
- Video: ggplot2 (part 3)
- Video: ggplot2 (part 4)
- Video: ggplot2 (part 5)
- Reading: Practical R Exercises in swirl Part 2
- Ungraded Programming: swirl Lesson 1: Lattice Plotting System
- Ungraded Programming: swirl Lesson 2: Working with Colors
- Ungraded Programming: swirl Lesson 3: GGPlot2 Part1
- Ungraded Programming: swirl Lesson 4: GGPlot2 Part2
- 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
- Video: Hierarchical Clustering (part 1)
- Video: Hierarchical Clustering (part 2)
- Video: Hierarchical Clustering (part 3)
- Video: K-Means Clustering (part 1)
- Video: K-Means Clustering (part 2)
- Video: Dimension Reduction (part 1)
- Video: Dimension Reduction (part 2)
- Video: Dimension Reduction (part 3)
- Video: Working with Color in R Plots (part 1)
- Video: Working with Color in R Plots (part 2)
- Video: Working with Color in R Plots (part 3)
- Video: Working with Color in R Plots (part 4)
- Reading: Practical R Exercises in swirl Part 3
- Ungraded Programming: swirl Lesson 1: Hierarchical Clustering
- Ungraded Programming: swirl Lesson 2: K Means Clustering
- Ungraded Programming: swirl Lesson 3: Dimension Reduction
- 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
- Video: Clustering Case Study
- Video: Air Pollution Case Study
- Reading: Practical R Exercises in swirl Part 4
- Ungraded Programming: swirl Lesson 1: CaseStudy
- Reading: Post-Course Survey
Graded: Course Project 2
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Johns Hopkins University
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Pricing
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| Access to graded materials | Available | Not available |
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Ratings and Reviews
The peer review takes so long..................................................................................... which costs me extra money even though I have finished all the stuff 1.5days before the last day.
Course is very good.
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Excellent course.