It is easy to confuse Analytics and Data Mining. Data Mining is a term that was quite popular a little while ago but has kind of lost the attention it once held. Some experts believe that the term "Analytics" has replaced "Data Mining"- its the same thing under the hood. That is not quite true. They are close but different. The table below lists out the differences between the two.
|Discovery||Leveraging human judgment is key; automated discovery is a tool to accomplish this goal||Automated discovery is key; leveraging human judgment is a tool to accomplish this goal|
|Reduction & Holism||Stronger emphasis on understanding systems as wholes, in their full complexity||Stronger emphasis on reducing to components and analyzing individual components and relationships between them|
|Origins||has stronger origins in semantic web, "intelligent curriculum," outcome prediction, and systemic interventions||has strong origins in software and modelling|
|Adaptation & Personalisation||Greater focus on informing and empowering end users||Greater focus on automated adaptation (eg by the computer with no human in the loop)|
|Techniques & Methods||Social network analysis, sentiment analysis, influence analytics, concept analysis, sense-making models||Classification, clustering, Bayesian modelling, relationship mining, discovery with models, visualization|
- Siemens & Baker (2012). Learning Analytics and Educational Data Mining: Towards Communication and Collaboration. Learning Analytics and Knowledge 2012. Available in .pdf format at http://users.wpi.edu/~rsbaker/LAKs%20reformatting%20v2.pdf