Debe estar conectado para tomar este curso  →   ENTRAR | REGISTRATE AHORA

Exploratory Data Analysis

4 ALUMNOS MATRICULADOS
  • Imagen de perfil de Mooc
  • Imagen de perfil de Magaly
  • Imagen de perfil de Val1494
  • Imagen de perfil de enova79
¡Compártelo! Share on Facebook0Share on LinkedIn0Share on Google+0Tweet about this on Twitter


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.

Syllabus

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.

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.

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.

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.

Opiniones del Curso

N.A.

ratings
  • 5 stars0
  • 4 stars0
  • 3 stars0
  • 2 stars0
  • 1 stars0

No se han encontrado opiniones para este curso

COMUNIDAD MOOC IR AL CURSO

*Irás a la plataforma donde se impartirá el mooc y donde podrás registrarte

  • GRATIS
  • 10-04-2017FECHA INICIO
  • Johns Hopkins University
  • Roger Peng

Acerca de Mooc.es

En Mooc.es encontrarás la mayor oferta de cursos de las mejores universidades del mundo.

El conocimiento al alcance de todos.

¡Compártelo!

Share on Facebook0Share on LinkedIn0Share on Google+0Tweet about this on Twitter