À propos de ce cours : 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.
Créé par : Johns Hopkins University
Enseigné par : Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public HealthEnseigné par : Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public HealthEnseigné par : Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
| Informations de base | Cours 4 de 10 dans Data Science Specialization |
| Langue | English, Sous-titres : Chinese (Simplified) |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 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 vidéos, 6 lectures
- Материал для самостоятельного изучения: Welcome to Exploratory Data Analysis
- Материал для самостоятельного изучения: Syllabus
- Материал для самостоятельного изучения: Pre-Course Survey
- Материал для самостоятельного изучения: Exploratory Data Analysis with R Book
- Материал для самостоятельного изучения: The Art of Data Science
- Vidéo: Installing R on Windows (3.2.1)
- Vidéo: Installing R on a Mac (3.2.1)
- Vidéo: Installing R Studio (Mac)
- Vidéo: Setting Your Working Directory (Windows)
- Vidéo: Setting Your Working Directory (Mac)
- Vidéo: Principles of Analytic Graphics
- Vidéo: Exploratory Graphs (part 1)
- Vidéo: Exploratory Graphs (part 2)
- Vidéo: Plotting Systems in R
- Vidéo: Base Plotting System (part 1)
- Vidéo: Base Plotting System (part 2)
- Vidéo: Base Plotting Demonstration
- Vidéo: Graphics Devices in R (part 1)
- Vidéo: Graphics Devices in R (part 2)
- Материал для самостоятельного изучения: Practical R Exercises in swirl Part 1
- Неоцениваемое задание по программированию: swirl Lesson 1: Principles of Analytic Graphs
- Неоцениваемое задание по программированию: swirl Lesson 2: Exploratory Graphs
- Неоцениваемое задание по программированию: swirl Lesson 3: Graphics Devices in R
- Неоцениваемое задание по программированию: swirl Lesson 4: Plotting Systems
- Неоцениваемое задание по программированию: swirl Lesson 5: Base Plotting System
Noté: Week 1 Quiz
Noté: Course Project 1
SEMAINE 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 vidéos, 1 lecture
- Vidéo: Lattice Plotting System (part 2)
- Vidéo: ggplot2 (part 1)
- Vidéo: ggplot2 (part 2)
- Vidéo: ggplot2 (part 3)
- Vidéo: ggplot2 (part 4)
- Vidéo: ggplot2 (part 5)
- Материал для самостоятельного изучения: Practical R Exercises in swirl Part 2
- Неоцениваемое задание по программированию: swirl Lesson 1: Lattice Plotting System
- Неоцениваемое задание по программированию: swirl Lesson 2: Working with Colors
- Неоцениваемое задание по программированию: swirl Lesson 3: GGPlot2 Part1
- Неоцениваемое задание по программированию: swirl Lesson 4: GGPlot2 Part2
- Неоцениваемое задание по программированию: swirl Lesson 5: GGPlot2 Extras
Noté: Week 2 Quiz
SEMAINE 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 vidéos, 1 lecture
- Vidéo: Hierarchical Clustering (part 2)
- Vidéo: Hierarchical Clustering (part 3)
- Vidéo: K-Means Clustering (part 1)
- Vidéo: K-Means Clustering (part 2)
- Vidéo: Dimension Reduction (part 1)
- Vidéo: Dimension Reduction (part 2)
- Vidéo: Dimension Reduction (part 3)
- Vidéo: Working with Color in R Plots (part 1)
- Vidéo: Working with Color in R Plots (part 2)
- Vidéo: Working with Color in R Plots (part 3)
- Vidéo: Working with Color in R Plots (part 4)
- Материал для самостоятельного изучения: Practical R Exercises in swirl Part 3
- Неоцениваемое задание по программированию: swirl Lesson 1: Hierarchical Clustering
- Неоцениваемое задание по программированию: swirl Lesson 2: K Means Clustering
- Неоцениваемое задание по программированию: swirl Lesson 3: Dimension Reduction
- Неоцениваемое задание по программированию: swirl Lesson 4: Clustering Example
SEMAINE 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 vidéos, 2 lectures
- Vidéo: Air Pollution Case Study
- Материал для самостоятельного изучения: Practical R Exercises in swirl Part 4
- Неоцениваемое задание по программированию: swirl Lesson 1: CaseStudy
- Материал для самостоятельного изучения: Post-Course Survey
Noté: Course Project 2
FAQ
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Créateurs
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.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
More instructions on SVD/PCA would be welcome
Good beginners course with helpful tools to take a first glance to your data
Loved it , the K means was pretty difficult for me but others was a breeze as i had experince in ggplots before the course
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The exploratory data analysis is a very important part of the elaboration of a data product because this period helps to understand the most important variables and the elements to construct models and visualize an early result.