This course is a follow-up of the predictive modeling course. It aims at business managers,
information professionals, financial analysts, insurance analysts, as well as
general audience who are interested in how to turn data into business intelligence
and eventually use data and analytics to create business value. It is generally assumed
that students who take the course will have basic understanding about statistics
and simple predictive modeling techniques (such as data visualization,
simple linear regression, data partition, etc).
The course will discuss the principles and ideas underlying the current practice of data mining,
as well as introduce a broad collection of useful data analytics tools (such as clustering,
logistic regression, latent class analysis, survival models, association and event analysis,
text mining, etc). However,it emphasizes hands-on learning with a focus on dealing with
real business problems from industry-strength projects. In that regard,
the course will take a case-study approach. It will not just describe/explain the end results,
but also discuss the process of formulating/refining business objectives, data selection,
data preparation, model selection and evaluation that lead to the results.
The course will use analytics software SAS (including SAS Base, SAS Enterprise Miner,
SAS Text Analytics) for hands-on experimentation with various data mining techniques.
Course includes: Data Mining Overview; Clustering Analysis; Latent Class Analysis,
Survival Models; Text Mining; Social Network Analysis