USF Masters in Data Analytics Class

From data to information --Unstructured, scrappy data

statistics and machine learning, 

. “They can take a data set and model it mathematically and understand the math required to build those models; they can actually do that, which means they have the engineering skills…and finally they are someone who can find insights and tell stories from their data. That means asking the right questions, and that is usually the hardest piece.”

  

Computational Analytics I (2)
Data mining, including classification and association. Rules, trees, and classifiers. Clustering. Data cleaning. Use of relational and non-relational (NoSQL) data stores.

Business Analytics I (2)
Application of basic analytical methods to business problems. Topics include market basket analysis, management science, optimization and satisficing techniques, survey design. 

Computational Analytics II (2)
Topics include: advanced data mining, text mining, modeling of problems for hadoop/MapReduce, network analysis, managing large data sets.