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.
Ce cours fait partie de la Spécialisation Science des données
Exploration analytique de données
proposé par
À propos de ce cours
85,569 consultations récentes
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
Cluster AnalysisGgplot2R ProgrammingExploratory Data Analysis
Les étudiants prenant part à ce Course sont
- Biostatisticians
- Data Analysts
- Data Scientists
- Risk Managers
- User Experience Researchers
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
120 heures pour terminer
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 vidéos (Total 109 min), 6 lectures, 7 quiz
15 vidéos
Introduction1 min
Installing R on Windows (3.2.1)3 min
Installing R on a Mac (3.2.1)1 min
Installing R Studio (Mac)3 min
Setting Your Working Directory (Windows)7 min
Setting Your Working Directory (Mac)7 min
Principles of Analytic Graphics12 min
Exploratory Graphs (part 1)9 min
Exploratory Graphs (part 2) 5 min
Plotting Systems in R9 min
Base Plotting System (part 1)11 min
Base Plotting System (part 2)6 min
Base Plotting Demonstration16 min
Graphics Devices in R (part 1)5 min
Graphics Devices in R (part 2)7 min
6 lectures
Welcome to Exploratory Data Analysis10 min
Syllabus10 min
Pre-Course Survey10 min
Exploratory Data Analysis with R Book10 min
The Art of Data Science10 min
Practical R Exercises in swirl Part 110 min
1 exercice pour s'entraîner
Week 1 Quiz20 min
Semaine
217 heures pour terminer
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 vidéos (Total 61 min), 1 lecture, 6 quiz
7 vidéos
Lattice Plotting System (part 2)6 min
ggplot2 (part 1)6 min
ggplot2 (part 2)13 min
ggplot2 (part 3)9 min
ggplot2 (part 4)10 min
ggplot2 (part 5)8 min
1 lecture
Practical R Exercises in swirl Part 210 min
1 exercice pour s'entraîner
Week 2 Quiz20 min
Semaine
313 heures pour terminer
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 vidéos (Total 77 min), 1 lecture, 4 quiz
12 vidéos
Hierarchical Clustering (part 2)5 min
Hierarchical Clustering (part 3)7 min
K-Means Clustering (part 1)5 min
K-Means Clustering (part 2)4 min
Dimension Reduction (part 1)7 min
Dimension Reduction (part 2)9 min
Dimension Reduction (part 3)6 min
Working with Color in R Plots (part 1)4 min
Working with Color in R Plots (part 2)7 min
Working with Color in R Plots (part 3)6 min
Working with Color in R Plots (part 4)3 min
1 lecture
Practical R Exercises in swirl Part 310 min
Semaine
46 heures pour terminer
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 vidéos (Total 55 min), 2 lectures, 2 quiz
2 lectures
Practical R Exercises in swirl Part 410 min
Post-Course Survey10 min
4.7
685 avis38%
a commencé une nouvelle carrière après avoir terminé ces cours
38%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
14%
a obtenu une augmentation de salaire ou une promotion
Principaux examens pour Exploration analytique de données
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.
Enseignants
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
À propos de Université Johns-Hopkins
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.
À propos du Spécialisation Science des données
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
Foire Aux Questions
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