I think learning statistics definitely helps. Simple things like averages, stdev, probability, etc will help you. Also a healthy knowledge of SQL goes a long way.
But more importantly, its learning how to be a detective. For example, I'm not strictly a math person. I also know Python and PHP and use it to write scripts when I need to validate a theory, often working with raw logs.
You also need business acumen. Analysts are like consultants in that they work with a variety of teams and stakeholders. Everyone wants to be sure that the decision they're making is the right one, and data can help them do it. So you need an understanding of what the core issue is, how the stakeholder wants it solved, and the engineering capability to get it done in a timely fashion.
One of my favorite books is Collective Intelligence. Its a soft-intro to machine learning, and having read that and gone through those exercises along with taking a formal class on machine learning, has helped me see the importance of statistics applied at web scale.
EDIT (to the PS):
I don't think there's a standard teaching for data science. Everyone screens for it differently. If you apply for a job as a data scientist at LinkedIn for example (I did) they'll expect you to have a fairly formal CS background and will throw you questions any good software engineer should be able to solve. But they also ask you questions like "design a news feed" or "whats a good algorithm for a spam filter and how do you score it" or "how do you make an algorithm work with little data". I think "data science" is actually learning how to apply statistics at web scale problems, so my advice would be to look at things like recommenders, spam filters, classifiers, NLP, etc.
But more importantly, its learning how to be a detective. For example, I'm not strictly a math person. I also know Python and PHP and use it to write scripts when I need to validate a theory, often working with raw logs.
You also need business acumen. Analysts are like consultants in that they work with a variety of teams and stakeholders. Everyone wants to be sure that the decision they're making is the right one, and data can help them do it. So you need an understanding of what the core issue is, how the stakeholder wants it solved, and the engineering capability to get it done in a timely fashion.
One of my favorite books is Collective Intelligence. Its a soft-intro to machine learning, and having read that and gone through those exercises along with taking a formal class on machine learning, has helped me see the importance of statistics applied at web scale.
EDIT (to the PS): I don't think there's a standard teaching for data science. Everyone screens for it differently. If you apply for a job as a data scientist at LinkedIn for example (I did) they'll expect you to have a fairly formal CS background and will throw you questions any good software engineer should be able to solve. But they also ask you questions like "design a news feed" or "whats a good algorithm for a spam filter and how do you score it" or "how do you make an algorithm work with little data". I think "data science" is actually learning how to apply statistics at web scale problems, so my advice would be to look at things like recommenders, spam filters, classifiers, NLP, etc.