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Apr 18, 2024
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IT 270 - Data Mining for Business Analytics
Semester Hours: 3
Fall, Spring
Business analytics is the transformation of data into information and knowledge that facilitates organizational decision-making to optimize performance and gain competitive advantage. Business analytics requires use of structured data residing in company databases and employing mining techniques to discover hidden useful information from the data. Supervised and unsupervised learning will be discussed. The course builds upon data mining principles: problem definition, exploratory data analysis, dimension reduction, consideration of alternative models, calibration of models, and evaluation and deployment. Principal topics covered are: classification and regression trees, neural network, association rules (market basket analysis), and clustering. Text mining and visualization techniques will also be discussed. Students are required to complete a number of projects using specialized DM software to capture the salient data mining principles covered in the course.
Prerequisite(s)/Course Notes:
IT 203 . Open only to matriculated graduate students in the Frank G. Zarb School of Business and in other Schools at Hofstra when appropriate. See specific program requirements. (Formerly Data Mining for Business Intelligence.)
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