Stat 101

In this big data age, everyone is required to have some data sense. Statistics as a science of data, becomes more and more important nowadays. Harvard’s Statistics 101 is a good starting point.

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Queen’s Butterfly

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(R)  This graph is copy righted by Queen Statistical Consulting.

Data Science: Who’s baby?

If you tell people that you are a statistician, people may immediately think about data, because it is common to think that statisticians are data miners. In fact, this is just one side of a die. Statisticians not only run data analysis, more importantly develop statistical methodologies, supervise experimental design, and do simulation study. Furthermore, they help policy makers to make decision and provide suggestions. Thus, data crunching is just part of the work statisticians do.
Data science initially was termed by two well known statisticians. They are  Chien-Fu Jeff Wu (http://www2.isye.gatech.edu/~jeffwu/) and William S. Celeveland (http://www.stat.purdue.edu/people/faculty/wsc). Here is a pretty nice presentation by Dr. Diego Kuonen, who cited the above two statisticians:

Statistics or Data Science

Among the three, which one is the kernel?

As the new professional data scientist emerges, many statisticians begin to wonder what is the difference between a data scientist and a statistician. I came across the data science certificate at Havard Extension School at
http://www.extension.harvard.edu/p…/data-science-certificate

It is clear that data scientist needs to know some statistical techniques as well as compute database knowledge. Statistics conbsists of descriptive and inferential statistics. The descriptive statistics includes visualization and summary statistics, while data science is mainly statistics via visualization, association study and making insightful discovery of the data. The main challenge is the high dimension and hence dimension reduction is the main issue.

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