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|OPIM 563 - Applied Decision Theory|
Small Data is an integral part of Business Analytics and it refers to two important concepts in practice: I. When solving a consulting problem, it is very common to have a small sized data set (say less than 30) in order to solve practical business situations. This data set may not be normal or fulfilled any data assumption, could be quantitative or qualitative. The data could be categories such a political parties, ranking such as customer preferences or continuous such as the time a customer wait in line at a credit card 800 numbers. II. Small data is also a new concept in Business Intelligence which appeared in late 2012, it is the opposite of Big Data and it is an innovative way to understand your customers’ actions. It refers to all tidbit of information created by customers by means of customers’ preferences, wearables devices and self-tracking. Think of all the digital tidbits consumers leave in their paths as they go through the day. Credit card payments, location fixes, newsletter signups, Facebook likes, tweets and Web searches. Small data are derived from our individual digital traces. Small data is generally small sample sized and not very normal distributed. Small data drive personalization. The aim of this class is to introduce advanced quantitative techniques for Business Analytics that will help you to make decisions in the best way to analyze real business data for small data.
1.500 Credit hours
1.500 Lecture hours
0.000 Lab hours
Levels: MN or MC Graduate, Juris Doctor
Schedule Types: Lecture
Operations & Information Mgmt Department
Must be enrolled in one of the following Degrees:
Master of Business Admin.
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