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. 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 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 MTMar 26, 2016
This is a great introductory course on the topic and on R language.
You will get acquainted with basic R functions which are most useful for initial statistical analysis.
by YSep 23, 2017
Very good course! It provide me the foundation in learning how to plot and interpret data. This will definitely strengthen my "R programming" to generate publication type figure for my genomics data!
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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