@statsRus starts to lay the groundwork for your answer in another question https://datascience.stackexchange.com/questions/1/what-characterises-the-difference-between-data-science-and-statisticsWhat characterises the difference between data science and statistics?:
- Data collection: web scraping and online surveys
- Data manipulation: recoding messy data and extracting meaning from linguistic and social network data
- Data scale: working with extremely large data sets
- Data mining: finding patterns in large, complex data sets, with an emphasis on algorithmic techniques
- Data communication: helping turn "machine-readable" data into "human-readable" information via visualization
Definition
data-mining can be seen as one item (or set of skills and applications) in the toolkit of the data scientist. I like how he separates the definition of mining from collection in a sort of trade-specific jargon.
However, I think that data-mining would be synonymous with data-collection in a US-English colloquial definition.
As to where to go to become proficient? I think that question is too broad as it is currently stated and would receive answers that are primarily opinion based. Perhaps if you could refine your question, it might be easier to see what you are asking.
