Document Type


Publication Date

Winter 2018


Mike Freiling, Daniel Sagalowicz

Course Title

Data Warehousing

Course Number

ETM 538/638


Data warehousing -- Applications to business, Data warehousing -- Planning, Data warehousing -- Statistical methods, Data warehousing -- Methodology


Data mining is widely described or defined as the discipline of: “making sense of the data”. In today’s day and age, the rise of ubiquity of information calls for more advanced and developed techniques to mine the data and come up with insights. Data mining finds applications in many different fields and industries: Whether it is in Embryology, Crops, Elections, or Business Marketing...etc. It is not a wild assumption to consider that every organization in the world has some data mining capabilities or its main activity necessitates it and they have some third party organization doing that for them. One particular area where data mining is really important is in the business world. Being able to find patterns in the data can tell whether the business survives for another couple of years or not. It can make the difference between being a fortune 500 company and bankruptcy and everybody who is interested in growth and sustainability knows that. During the whole course, we learned methodology and did assignments for practicing data mining and data warehousing. In this class project, we try to put to practice as many concepts as those learned in class and apply 3 algorithms from class (1-R, Bayesian, and Instant-based).


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