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.
ジョンズ・ホプキンズ大学（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.
- 5 stars74.13%
- 4 stars21.26%
- 3 stars3.41%
- 2 stars0.75%
- 1 star0.43%
I did learn more about putting together a set of graphs that help to explore the data. I did see how subsetting and aggregating data helps to give a better understanding of the data.
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.
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
Awesome course that expands on your R knowledge. Only nitpick is that some of the links don't work and the videos need an overhaul as there seem to be little to no updates since 2015/2016.