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The Department of Statistics at the University of Central Florida (UCF) established a Data Mining Certificate Program in the Fall of 2000 and started its Master's degree in Data Mining in the Fall of 2001. Data Mining is the process of exploration and analysis of large quantities of observational data in order to discover meaningful patterns and models. The professional staff in the program has pioneered new techniques in data mining and has an ongoing collaboration with SAS® Institute, the world's leading data mining software provider. Data Mining is an exciting and evolving discipline, as it can lead to the discovery of previously unknown patterns in data for potential business advantage.

The award-winning Data Mining Program at the University of Central Florida offers an established educational environment complemented with ongoing industrial collaborations. Every graduate of the program has served an internship with an eminent Orlando business partner in the aerospace, entertainment, hospitality or automobile service industry. Moreover, faculty have established consulting relationships with industrial clients inspiring relevant research directions, student employment opportunities and enhanced curriculum case studies. The Data Mining Program was officially established in 2000 at UCF, but the core faculty have had extensive experience in data mining and statistical consulting projects. Recently, UCF faculty and students won two 2004 KDD Cup Prizes.

Pre-eminent companies use Data Mining techniques to translate masses of raw data into valuable information for their business advantage, thus generating the need to have trained professionals to work in this field. The job market for this emerging area is superb with excellent remuneration and stable long-term prospects.

The aim of the Data Mining Program is to train individuals to solve real world problems with Enterprise Miner® software as well as to develop SAS® programming expertise.


"Data Mining is the process of exploration and analysis of large quantities of observational data to discover meaningful patterns and models"

 

       
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