Not at all, actually.

Cathy O'Neil and Rachel Schutt's book, Doing Data Science (http://shop.oreilly.com/product/0636920028529.do) covers this almost immediately. Because the term developed sort of organically as a cross-discipline approach to solving certain challenging problems, students in their classes were often a mix of scientists, statisticians, and software/database engineers like yourself.

So, you may not consider yourself "a data scientist", and that's fine, but there's certainly a role for your specialization in data science, and that doesn't at all indicate trouble in the field. On the contrary, it's exciting that there's a marriage of these specializations underway.

If I had the time, I'd write a much more in-depth reply along the same lines to many of the criticisms in the article. The lack of a clear definition for "data science" or "data scientist" has caused some confusion, but at the same time, there is new technology available and new approaches to working with large-scale datasets that weren't available before, and that does represent new skillsets.

Think it was this book, http://shop.oreilly.com/product/0636920028529.do

As already mentioned machine learning is a lot of statistics. There are some really good machine learning and statistics books on O Reilly Publishers. These two are a great start:

http://shop.oreilly.com/product/0636920028529.do

http://shop.oreilly.com/product/0636920034094.do?code=WKMATH...

Then last but certainly not least head on over to Kaggle.com.

They have introductory competitions which are awesome, as they are practical and you actually get involved.

Good luck!