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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Habilidades que você terá
Universidade Johns Hopkins
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 stars74,14%
- 4 stars21,25%
- 3 stars3,41%
- 2 stars0,75%
- 1 star0,43%
Principais avaliações do ANÁLISE EXPLORATÓRIA DE DADOS
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.
This is a great course. The basics are explained very clearly and very easy to understand. I highly recommend this course for those who wish to start in Data Analyst / Data Science track.
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.
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.
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