關於此課程：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.
製作方： Johns Hopkins University
教學方： Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
教學方： Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
教學方： Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
| 基本信息 | 課程 4（共 10 門，Data Science Specialization ） |
| 語言 | English, 字幕：Chinese (Simplified) |
| 如何通過 | 通過所有計分作業以完成課程。 |
| 用戶評分 |
授課大綱
第 1 週
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 個視頻, 6 個閱讀材料
- Leitura: Welcome to Exploratory Data Analysis
- Leitura: Syllabus
- Leitura: Pre-Course Survey
- Leitura: Exploratory Data Analysis with R Book
- Leitura: The Art of Data Science
- 視頻: Installing R on Windows (3.2.1)
- 視頻: Installing R on a Mac (3.2.1)
- 視頻: Installing R Studio (Mac)
- 視頻: Setting Your Working Directory (Windows)
- 視頻: Setting Your Working Directory (Mac)
- 視頻: Principles of Analytic Graphics
- 視頻: Exploratory Graphs (part 1)
- 視頻: Exploratory Graphs (part 2)
- 視頻: Plotting Systems in R
- 視頻: Base Plotting System (part 1)
- 視頻: Base Plotting System (part 2)
- 視頻: Base Plotting Demonstration
- 視頻: Graphics Devices in R (part 1)
- 視頻: Graphics Devices in R (part 2)
- Leitura: Practical R Exercises in swirl Part 1
- Programação que não vale nota: swirl Lesson 1: Principles of Analytic Graphs
- Programação que não vale nota: swirl Lesson 2: Exploratory Graphs
- Programação que não vale nota: swirl Lesson 3: Graphics Devices in R
- Programação que não vale nota: swirl Lesson 4: Plotting Systems
- Programação que não vale nota: swirl Lesson 5: Base Plotting System
已評分: Week 1 Quiz
已評分: Course Project 1
第 2 週
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 個視頻, 1 個閱讀材料
- 視頻: Lattice Plotting System (part 2)
- 視頻: ggplot2 (part 1)
- 視頻: ggplot2 (part 2)
- 視頻: ggplot2 (part 3)
- 視頻: ggplot2 (part 4)
- 視頻: ggplot2 (part 5)
- Leitura: Practical R Exercises in swirl Part 2
- Programação que não vale nota: swirl Lesson 1: Lattice Plotting System
- Programação que não vale nota: swirl Lesson 2: Working with Colors
- Programação que não vale nota: swirl Lesson 3: GGPlot2 Part1
- Programação que não vale nota: swirl Lesson 4: GGPlot2 Part2
- Programação que não vale nota: swirl Lesson 5: GGPlot2 Extras
已評分: Week 2 Quiz
第 3 週
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 個視頻, 1 個閱讀材料
- 視頻: Hierarchical Clustering (part 2)
- 視頻: Hierarchical Clustering (part 3)
- 視頻: K-Means Clustering (part 1)
- 視頻: K-Means Clustering (part 2)
- 視頻: Dimension Reduction (part 1)
- 視頻: Dimension Reduction (part 2)
- 視頻: Dimension Reduction (part 3)
- 視頻: Working with Color in R Plots (part 1)
- 視頻: Working with Color in R Plots (part 2)
- 視頻: Working with Color in R Plots (part 3)
- 視頻: Working with Color in R Plots (part 4)
- Leitura: Practical R Exercises in swirl Part 3
- Programação que não vale nota: swirl Lesson 1: Hierarchical Clustering
- Programação que não vale nota: swirl Lesson 2: K Means Clustering
- Programação que não vale nota: swirl Lesson 3: Dimension Reduction
- Programação que não vale nota: swirl Lesson 4: Clustering Example
第 4 週
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 個視頻, 2 個閱讀材料
- 視頻: Air Pollution Case Study
- Leitura: Practical R Exercises in swirl Part 4
- Programação que não vale nota: swirl Lesson 1: CaseStudy
- Leitura: Post-Course Survey
已評分: Course Project 2
常見問題解答
運作方式
Trabalho
Cada curso é como um livro didático interativo, com vídeos pré-gravados, testes e projetos.
Ajuda dos seus colegas
Conecte-se com milhares de outros aprendizes, debata ideias, discuta sobre os materiais do curso e obtenha ajuda para dominar conceitos.
Certificados
Obtenha reconhecimento oficial pelo seu trabalho e compartilhe seu sucesso com amigos, colegas e empregadores.
製作方
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.
價格
| 購買課程 | |
|---|---|
| 訪問課程材料 | 可用 |
| 訪問評分的材料 | 可用 |
| 收到最終成績 | 可用 |
| 獲得可共享的課程證書 | 可用 |
評分和審閱
Very good class. Of the four I've taken so far, this one has given me the most confidence that I could be successful as a data scientist/analyst.
Best Course i have taken ... THANKS Roger
A good start for data analysis, this course covers the basics of plotting with the three most common packages (base R, lattice, and ggplot2).I liked the assignment which difficulty is nicely measured (it is not just applying the videos concept, you have to look around the web to find tools and documentation about what functions to use).On a less positive aspects:- I am not sure this course was the best place to introduce kmean and PCA sections...- a lot of content is outdated (wrong links, old R command parameters, ...), look likes a quick freshup update would not do harm given the number of people that keeps registering...
AG
Fun and full of information :)