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.
Offered By
Johns Hopkins UniversityAbout this Course
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Approx. 54 hours to complete
English
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessWhat 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. 54 hours to complete
English
Could your company benefit from training employees on in-demand skills?
Try Coursera for Business
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Syllabus - What you will learn from this course
19 hours to complete
Week 1
19 hours to complete
15 videos (Total 109 min), 6 readings, 7 quizzes
17 hours to complete
Week 2
17 hours to complete
7 videos (Total 61 min), 1 reading, 6 quizzes
13 hours to complete
Week 3
13 hours to complete
12 videos (Total 77 min), 1 reading, 4 quizzes
5 hours to complete
Week 4
5 hours to complete
2 videos (Total 55 min), 2 readings, 2 quizzes
Reviews
- 5 stars74.15%
- 4 stars21.25%
- 3 stars3.40%
- 2 stars0.74%
- 1 star0.43%
TOP REVIEWS FROM EXPLORATORY DATA ANALYSIS
by SLMay 21, 2018
Week 3 - clustering concepts appear hard to comprehend initially. This week should first start with a practical example/use of clustering and then move on to technical
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 RSFeb 13, 2017
Nice course, but too much focus on "R" as a tool.... Industries don't use R as much... The course must be made more generic and independent of R - understand it is not easy to do but ....
by CBDec 10, 2016
The course is interesting and the content is relevant. I do think that there are some issues with project 2 though. I did provide feedback on that to the course administrators.
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