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.

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Approx. 15 heures pour terminer

Recommandé : 5 hours/week

English

Sous-titres : English, Chinese (Simplified)

Ce que vous allez apprendre

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

Compétences que vous acquerrez

R ProgrammingCluster AnalysisData VisualizationData Analysis

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Approx. 15 heures pour terminer

Recommandé : 5 hours/week

English

Sous-titres : English, Chinese (Simplified)

Syllabus - What you will learn from this course

1

Section

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. ...

15 videos (Total 109 min), 6 readings, 7 quizzes

15 videos

Introduction1m

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 exercises

Week 1 Quiz20m

2

Section

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....

7 videos (Total 61 min), 1 reading, 6 quizzes

7 videos

Lattice Plotting System (part 1)6m

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 readings

Practical R Exercises in swirl Part 210m

1 practice exercises

Week 2 Quiz20m

3

Section

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....

12 videos (Total 77 min), 1 reading, 4 quizzes

12 videos

Hierarchical Clustering (part 1)7m

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 readings

Practical R Exercises in swirl Part 310m

4

Section

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. ...

2 videos (Total 55 min), 2 readings, 2 quizzes

2 videos

Clustering Case Study14m

Air Pollution Case Study40m

2 readings

Practical R Exercises in swirl Part 410m

Post-Course Survey10m

Instructors

Roger D. Peng, PhD

Associate Professor, Biostatistics

Jeff Leek, PhD

Associate Professor, Biostatistics

Brian Caffo, PhD

Professor, Biostatistics

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

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Spécialisation Data Science