Found 3 comments on HN
thaumaturgy · 2019-02-14 · Original thread
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.

markatkinson · 2015-05-08 · Original thread
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!

Get dozens of book recommendations delivered straight to your inbox every Thursday.