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.
oferecido por
Universidade Johns Hopkins
Informações sobre o curso
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Resultados de carreira do aprendiz
38%
comecei uma nova carreira após concluir estes cursos
38%
consegui um benefício significativo de carreira com este curso
15%
recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Aprox. 56 horas para completar
Inglês
Legendas: Inglês, Coreano, Vietnamita, Chinês (simplificado)
O que você vai aprender
Understand analytic graphics and the base plotting system in R
Use advanced graphing systems such as the Lattice system
Make graphical displays of very high dimensional data
Apply cluster analysis techniques to locate patterns in data
Habilidades que você terá
Cluster AnalysisGgplot2R ProgrammingExploratory Data Analysis
Resultados de carreira do aprendiz
38%
comecei uma nova carreira após concluir estes cursos
38%
consegui um benefício significativo de carreira com este curso
15%
recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Aprox. 56 horas para completar
Inglês
Legendas: Inglês, Coreano, Vietnamita, Chinês (simplificado)
oferecido por
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.
Programa - O que você aprenderá com este curso
20 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.
20 horas para concluir
15 vídeos (Total 109 mín.), 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
17 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.
17 horas para concluir
7 vídeos (Total 61 mín.), 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
13 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.
13 horas para concluir
12 vídeos (Total 77 mín.), 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
6 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.
6 horas para concluir
2 vídeos (Total 55 mín.), 2 leituras, 2 testes
2 leituras
Practical R Exercises in swirl Part 410min
Post-Course Survey10min
Avaliações
Principais avaliações do ANÁLISE EXPLORATÓRIA DE DADOS
por YSep 23, 2017
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!
por CCJul 28, 2016
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.
por BSJun 20, 2017
The course on Exploratory Data Analysis was highly enjoyable. I used to do a lot of this sort of thing in my job, but now spend more of my time managing people. It is fun to get "hands-on" again.
por IAJan 17, 2016
Very nice course, plotting data to explore and understand various features and their relationship is the key in any research domain, and this course teaches the skill required to achieve this.
por RSFeb 13, 2017
Nice course, but too much focus on "R" as a tool.... Industries don't use R as much... The course must be made more generic and independent of R - understand it is not easy to do but ....
por MAJul 3, 2018
Excellent explanation and adding very good skills on the way of data science specialization.For some slides they should be updated to have working URLs , some seems old and absolute now
por MEAug 9, 2018
Loved it! It took me longer than expected due to work and family issues, but I went so many times to the materials and even use some ggplot2 for work that ended being quite fulfilling.
por MKDec 28, 2019
Great intro to plotting and related tools in R. Will say that the coverage of heatmaps and PCA felt a little out of left field, with very little intuition. However, overall quite good.
por YWJun 5, 2017
This was incredibly useful because it gives you a feel for the datasets and tools with which to explore them. I really wasn't aware of the base and lattice plotting systems until now.
por LSOct 31, 2019
Good introduction. The swirl exercises kind of reproduce the lectures though- felt like it might not have been the most efficient use of time to go over the exact same example again.
por CBJan 11, 2017
I did learn more about putting together a set of graphs that help to explore the data. I did see how subsetting and aggregating data helps to give a better understanding of the data.
por NLMar 8, 2017
When it comes to hierarchical and K-means clustering, the theory wasn't explained clearly. When do we use U and V for what purpose? How does D come in? I'm left confused after this.
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