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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Johns Hopkins UniversityAbout this Course
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Approx. 55 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, English, Spanish
What you will learn
Understand analytic graphics and the base plotting system in R
Use advanced graphing systems such as the Lattice system
Make graphical displays of very high dimensional data
Apply cluster analysis techniques to locate patterns in data
Skills you will gain
- Cluster Analysis
- Ggplot2
- R Programming
- Exploratory Data Analysis
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Approx. 55 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, English, Spanish
Offered by
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.
Syllabus - What you will learn from this course
19 hours to complete
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.
19 hours to complete
15 videos (Total 109 min), 6 readings, 7 quizzes
17 hours to complete
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.
17 hours to complete
7 videos (Total 61 min), 1 reading, 6 quizzes
13 hours to complete
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.
13 hours to complete
12 videos (Total 77 min), 1 reading, 4 quizzes
5 hours to complete
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.
5 hours to complete
2 videos (Total 55 min), 2 readings, 2 quizzes
Reviews
- 5 stars74.14%
- 4 stars21.25%
- 3 stars3.41%
- 2 stars0.75%
- 1 star0.43%
TOP REVIEWS FROM EXPLORATORY DATA ANALYSIS
by MAJul 3, 2018
Excellent explanation and adding very good skills on the way of data science specialization.For some slides they should be updated to have working URLs , some seems old and absolute now
by LSOct 31, 2019
Good introduction. The swirl exercises kind of reproduce the lectures though- felt like it might not have been the most efficient use of time to go over the exact same example again.
by CCJul 28, 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.
by NLMar 8, 2017
When it comes to hierarchical and K-means clustering, the theory wasn't explained clearly. When do we use U and V for what purpose? How does D come in? I'm left confused after this.
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