Informações sobre o curso
4.7
3,951 ratings
590 reviews
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....
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Sugerido: 5 hours/week

Aprox. 15 horas restantes
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English

Legendas: English, Chinese (Simplified)

O que você vai aprender

  • Check
    Apply cluster analysis techniques to locate patterns in data
  • Check
    Make graphical displays of very high dimensional data
  • Check
    Understand analytic graphics and the base plotting system in R
  • Check
    Use advanced graphing systems such as the Lattice system

Habilidades que você terá

Cluster AnalysisGgplot2R ProgrammingExploratory Data Analysis
Globe

cursos 100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Calendar

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Clock

Sugerido: 5 hours/week

Aprox. 15 horas restantes
Comment Dots

English

Legendas: English, Chinese (Simplified)

Programa - O que você aprenderá com este curso

1

Seção
Clock
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. ...
Reading
15 vídeos (Total de 109 min), 6 leituras, 7 testes
Video15 videos
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
Reading6 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
Quiz1 exercício prático
Week 1 Quiz20min

2

Seção
Clock
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....
Reading
7 vídeos (Total de 61 min), 1 leitura, 6 testes
Video7 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
Reading1 leituras
Practical R Exercises in swirl Part 210min
Quiz1 exercício prático
Week 2 Quiz20min

3

Seção
Clock
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....
Reading
12 vídeos (Total de 77 min), 1 leitura, 4 testes
Video12 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
Reading1 leituras
Practical R Exercises in swirl Part 310min

4

Seção
Clock
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. ...
Reading
2 vídeos (Total de 55 min), 2 leituras, 2 testes
Video2 videos
Air Pollution Case Study40min
Reading2 leituras
Practical R Exercises in swirl Part 410min
Post-Course Survey10min
4.7
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Melhores avaliações

por CCJul 29th 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 YSep 24th 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!

Instrutores

Roger D. Peng, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health

Jeff Leek, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health

Brian Caffo, PhD

Professor, Biostatistics
Bloomberg School of Public Health

Sobre 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....

Sobre o Programa de cursos integrados Data Science

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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Perguntas Frequentes – FAQ

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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