During the past weeks I spent some time learning more about Data Science. After some research, and some useful tweets from Tom Tunguz, I could find the Revolutions blog and a post with a link to a book titled: Introduction to Data Science, from Jeffrey Stanton from Syracuse University School. I started to read the book and now that I have read the first 11 chapters I can rate the book with 5/5 stars.

The book focus is very practical. After the first 2 chapters about the concept of data and the identification of data problems, the author introduces R, the open source data analysis program. Using R the author guides you from the concept of data (From the book: The inventor of the World Wide Web, Tim Berners-Lee, is often quoted as having said, “Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom”) to more complex concepts like the law of “large numbers” and
the “central limit theorem”. Through the book you will learn how to interact with the Twitter API and use real time data to understand about Poisson distributions and other statistics and probability concepts (From Wikipedia: Statistics is closely related to probability theory, with which it is often grouped. The difference is, roughly, that probability theory starts from the given parameters of a total population to deduce probabilities that pertain to samples. Statistical inference, however, moves in the opposite direction—inductively inferring from samples to the parameters of a larger or total population). This is the complete list of chapters.

Which is some interesting material related to Data Science that you would like to share?

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