After a while I decided to continue with one of the first specializations on Coursera which I started more than year ago. This Data Science specialization contains 4th course with the same name as the topic which I was asking in opening question: Exploratory Data Analysis.
I was wondering how much effort I need to include and how much knowledge I can get. After 2 years (yes, anniversary of this blog is 18th of December 2015) with Data Science study path and more than 1 and half year with intensive usage of R language and couple of statistics and R courses. What can surprised me, right?
Nothing and something. I mean this both in positive way and will tell you why.
The content of course is simple. It seems to me it is too much, but I realized, that course is really introductory for someone who starts with R and has almost no experience with plotting and exploratory data analysis. Then explaining what is real base of exploratory analysis and R plotting systems overview is really good content. And more over to this are added topics about Clustering and Dimension Reduction which is really useful. And two case studies.
So, summarize topics what you will learn in 4 weeks about exploratory data analysis:
- Principles of exploratory data analysis
- What kind of graphs to use
- Base plotting system
- Lattice plotting system
- Ggplot2
- Hierarchical Clustering
- K-means Clustering
- How to work with colors
- And two case studies
I have passed 2 weeks so far, I did one of two assignments (I would rather see deadlines after 2nd and 4th week, not 1st and 3rd, you need to deliver 1st week too much, which could lead to the fact that many people give up), but I can say it worth.
P.S.: The code for illustrative rainbow color box plot chart image (run it couple of times to get nice result ;-)):
x <- rep(1:10, each=7) y <- rnorm(10*7) boxplot(y~x, col=rainbow(length(unique(x))))
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