There is often a confusion between these two terms.
This definition seems acceptable: "Data Analytics is all about automating insights into a dataset and supposes the usage of queries and data aggregation procedures. It can represent various dependencies between input variables, but also it can use Data Mining techniques and tools to discover hidden patterns in the dataset under analysis. For example, not obvious associations between user purchases can be automatically discovered."
Source: https://onthe.io/learn/en/category/analytic/What-is-the-difference-between-Data-Science,-Data-Analysis,-Big-Data,-Data-Analytics,--Data-Mining-and-Machine-Learning%3F
Very informative URL. Would you like to say something on KDD (Knowledge Discovery in Database). Does it overlap with Data Science?
It's just my opinion: the term 'data Science' is a buzz word! A wikipedia page is available to define it: https://en.wikipedia.org/wiki/Data_science.
I would say: data scientist=mix(statistician,developer).
Analytics are specifically constructed records and texts that collectively represent a larger collective or set. They can be abstracts and descriptions of individual papers that form part of conference proceedings for example; or manuscripts that constitute a particular collection. They provide more detail than is provided by the general description and record of the collective itself, which might describe the conference or collection only in broad terms. Links are usually provided from the 'parent' whole to its analytics ... and from each analytic back to its parent. The creation of analytics usually involves intellectual input to assign subject headings, syndetic (see, see-also, see-under) references and so on. Data mining might identify materials worthy of being made analytics, but mining beyond the pre-coordinate environment of formal library data, relationships, definitions and systems will result in data that , like free-text data, will need considerable re-structuring, definition and re-ordering to create analytics in any ordered context. That doesn't mean it's not worth the effort, only that effort and intellectual input will be required after mining is completed - unless you've developed an automatic system of evaluation, classification, heading assignment, thesaurus control, corporate author/acronym management and publisher and imprint management (and a host of other areas of automatic indexing technologies). Meta tag data available on the web are a far cry from the structured subject catalogues and classification systems employed by libraries - a principle reason why libraries continue to offer discrete access methods and OPACs (On-line Public Access Catalogues) with separate author, title, subject, publisher access points etc, as well as global keyword access.
Data mining (as mining term was borrowed from mining engineering) is like mining gold on earth. It aims to find something useful (gold) among vast amount of noise (worthless soil). Why mining gold is difficult? because the search space (earth) is much much bigger than the target (gold). Therefore in data mining the focus is on the largeness of the search space, while data analytic is a broader concept that can include both data mining and statistics, because it does not talk about the volume of data.
Both are same. Performing analysis on the data means data analytic and data mining is one of the intelligent step where we extract the useful pattern from the huge amount of data.
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