
Provides an introduction to the fundamental principles of data science, walking the reader through the "data-analytic thinking" necessary for extracting useful knowledge and business value from collected data.
I personally got way more value and insight out of "Data Science for Business" and would highly recommend it.
Something that stood out to me was their discussion about "Big Data technologies" (Hadoop, HBase...) and how they "support" data mining techniques. So sometimes I wonder if big data technologies are more for the processing of the data, like the ETL needed for data warehouses - but since it's big, we need those special technologies to process it (and do additional data-sciencey things on it - like calculate a probability of churn, probability of loan default, etc). End results can still be those high-performance in-memory objects that we can slice and dice, just like our data warehouses, if that's how we need to see them.
This is all coming from no practical experience with Hadoop / Big Data, just research so hopefully someone clarifies :)