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.
Este curso faz parte do Programa de cursos integrados Ciência de Dados
Análise Exploratória de Dados
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Informações sobre o curso
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100,834O que você vai aprender
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
Habilidades que você terá
Cluster AnalysisGgplot2R ProgrammingExploratory Data Analysis
Programa - O que você aprenderá com este curso
Semana
120 horas para concluir
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 vídeos (total de (Total 109 mín.) min), 6 leituras, 7 testes
15 videos
Introduction1min
Installing R on Windows (3.2.1)3min
Installing R on a Mac (3.2.1)1min
Installing R Studio (Mac)3min
Setting Your Working Directory (Windows)7min
Setting Your Working Directory (Mac)7min
Principles of Analytic Graphics12min
Exploratory Graphs (part 1)9min
Exploratory Graphs (part 2) 5min
Plotting Systems in R9min
Base Plotting System (part 1)11min
Base Plotting System (part 2)6min
Base Plotting Demonstration16min
Graphics Devices in R (part 1)5min
Graphics Devices in R (part 2)7min
6 leituras
Welcome to Exploratory Data Analysis10min
Syllabus10min
Pre-Course Survey10min
Exploratory Data Analysis with R Book10min
The Art of Data Science10min
Practical R Exercises in swirl Part 110min
1 exercício prático
Week 1 Quiz20min
Semana
217 horas para concluir
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 vídeos (total de (Total 61 mín.) min), 1 leitura, 6 testes
7 videos
Lattice Plotting System (part 2)6min
ggplot2 (part 1)6min
ggplot2 (part 2)13min
ggplot2 (part 3)9min
ggplot2 (part 4)10min
ggplot2 (part 5)8min
1 leituras
Practical R Exercises in swirl Part 210min
1 exercício prático
Week 2 Quiz20min
Semana
313 horas para concluir
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 vídeos (total de (Total 77 mín.) min), 1 leitura, 4 testes
12 videos
Hierarchical Clustering (part 2)5min
Hierarchical Clustering (part 3)7min
K-Means Clustering (part 1)5min
K-Means Clustering (part 2)4min
Dimension Reduction (part 1)7min
Dimension Reduction (part 2)9min
Dimension Reduction (part 3)6min
Working with Color in R Plots (part 1)4min
Working with Color in R Plots (part 2)7min
Working with Color in R Plots (part 3)6min
Working with Color in R Plots (part 4)3min
1 leituras
Practical R Exercises in swirl Part 310min
Semana
46 horas para concluir
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 vídeos (total de (Total 55 mín.) min), 2 leituras, 2 testes
2 leituras
Practical R Exercises in swirl Part 410min
Post-Course Survey10min
37%
comecei uma nova carreira após concluir estes cursos
38%
consegui um benefício significativo de carreira com este curso
15%
recebi um aumento ou promoção
Melhores avaliações
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.
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!
Instrutores
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
Sobre Universidade 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.
Sobre o Programa de cursos integrados Ciência de Dados
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.
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