Acerca de este curso: 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.
Creada por: Universidad Johns Hopkins
Enseñado por: Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public HealthEnseñado por: Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public HealthEnseñado por: Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
| Información básica | Curso 4 de 10 en Data Science Specialization |
| Idioma | English, Subtítulos: Chinese (Simplified) |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
Programa
SEMANA 1
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 videos, 6 lecturas
- Reading: Welcome to Exploratory Data Analysis
- Reading: Syllabus
- Reading: Pre-Course Survey
- Reading: Exploratory Data Analysis with R Book
- Reading: The Art of Data Science
- Vídeo: Installing R on Windows (3.2.1)
- Vídeo: Installing R on a Mac (3.2.1)
- Vídeo: Installing R Studio (Mac)
- Vídeo: Setting Your Working Directory (Windows)
- Vídeo: Setting Your Working Directory (Mac)
- Vídeo: Principles of Analytic Graphics
- Vídeo: Exploratory Graphs (part 1)
- Vídeo: Exploratory Graphs (part 2)
- Vídeo: Plotting Systems in R
- Vídeo: Base Plotting System (part 1)
- Vídeo: Base Plotting System (part 2)
- Vídeo: Base Plotting Demonstration
- Vídeo: Graphics Devices in R (part 1)
- Vídeo: Graphics Devices in R (part 2)
- Reading: Practical R Exercises in swirl Part 1
- 未分级程序开发: swirl Lesson 1: Principles of Analytic Graphs
- 未分级程序开发: swirl Lesson 2: Exploratory Graphs
- 未分级程序开发: swirl Lesson 3: Graphics Devices in R
- 未分级程序开发: swirl Lesson 4: Plotting Systems
- 未分级程序开发: swirl Lesson 5: Base Plotting System
Calificado: Week 1 Quiz
Calificado: Course Project 1
SEMANA 2
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 videos, 1 lecturas
- Vídeo: Lattice Plotting System (part 2)
- Vídeo: ggplot2 (part 1)
- Vídeo: ggplot2 (part 2)
- Vídeo: ggplot2 (part 3)
- Vídeo: ggplot2 (part 4)
- Vídeo: ggplot2 (part 5)
- Reading: Practical R Exercises in swirl Part 2
- 未分级程序开发: swirl Lesson 1: Lattice Plotting System
- 未分级程序开发: swirl Lesson 2: Working with Colors
- 未分级程序开发: swirl Lesson 3: GGPlot2 Part1
- 未分级程序开发: swirl Lesson 4: GGPlot2 Part2
- 未分级程序开发: swirl Lesson 5: GGPlot2 Extras
Calificado: Week 2 Quiz
SEMANA 3
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 videos, 1 lecturas
- Vídeo: Hierarchical Clustering (part 2)
- Vídeo: Hierarchical Clustering (part 3)
- Vídeo: K-Means Clustering (part 1)
- Vídeo: K-Means Clustering (part 2)
- Vídeo: Dimension Reduction (part 1)
- Vídeo: Dimension Reduction (part 2)
- Vídeo: Dimension Reduction (part 3)
- Vídeo: Working with Color in R Plots (part 1)
- Vídeo: Working with Color in R Plots (part 2)
- Vídeo: Working with Color in R Plots (part 3)
- Vídeo: Working with Color in R Plots (part 4)
- Reading: Practical R Exercises in swirl Part 3
- 未分级程序开发: swirl Lesson 1: Hierarchical Clustering
- 未分级程序开发: swirl Lesson 2: K Means Clustering
- 未分级程序开发: swirl Lesson 3: Dimension Reduction
- 未分级程序开发: swirl Lesson 4: Clustering Example
SEMANA 4
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 videos, 2 lecturas
- Vídeo: Air Pollution Case Study
- Reading: Practical R Exercises in swirl Part 4
- 未分级程序开发: swirl Lesson 1: CaseStudy
- Reading: Post-Course Survey
Calificado: Course Project 2
Preguntas Frecuentes
Cómo funciona
课程作业
每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。
来自同学的帮助
与其他成千上万的学生相联系，对想法进行辩论，讨论课程材料，并寻求帮助来掌握概念。
证书
获得正式认证的作业，并与朋友、同事和雇主分享您的成功。
Creadores
Universidad 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.
Tarifa
| Comprar curso | |
|---|---|
| Accede a los materiales del curso | Disponible |
| Accede a los materiales con calificación | Disponible |
| Recibe una calificación final | Disponible |
| Obtén un Certificado de curso para compartir | Disponible |
Calificaciones y revisiones
This has been a challenging course for me, for whatever reasons. I have devoted a great deal of time in reading Dr. Peng's books as well as reviewing work product of other students to get a better grasp of the logic and methodology. I have enjoyed this course more than any of the preceding courses. And, the struggle I believe will be worth the effort and facilitate my completion of the data science specialization program.
Great course with great materials. Easy to understand and to learn.
awesome course to learn exploratory data analysis using R programming language.
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Seems this would type of course in an online learning MOOC would be better if it was more direct hands on "how to" and less focused on explanatory fluff (academic style) .