Central Connecticut State University,Master of Science Data Mining

The Master of Science in Data Mining course that is offered by Central Connecticut State University will provide its students the ability to approach data mining as a process, by demonstrating competency in the use of CRISP-DM, the Cross-Industry Standard Process f or Data Mining, including the business understanding phase, the data understanding phase, the expl or at or y data analysis phase, the modeling phase, the evaluation phase, and the deployment phase. Students will be proficient with leading data mining software, including WEKA, Clementine by SPSS, and the R language as well. Students of Central Connecticut State University taking up Data Mining course will be able to understand and apply a wide range of clustering, estimation, prediction, and classification algorithms, including k-means clustering, BIRCH clustering, Kohonen clustering, classification and regression trees, the C4.5 algorithm, logistic Regression, k-nearest neighbor, multiple regression, and neural networks. Aside from this, they will also understand and apply the most current data mining techniques and applications, such as text mining, mining genomics data, and other current issues.For incoming students, they must hold a bachelors degree from a regionally accredited institution of higher education. The undergraduate record must demonstrate clear evidence of ability to undertake and pursue studies in a graduate field successfully. Applicants to the Graduate Certificate program in Data Mining program are expected to have completed, or be in the process of completing, a second semester course in undergraduate or graduate statistics. Students may be admitted on condition that they complete these prerequisite courses with a grade of B or better. Elective Courses are Web Mining, Data Mining for Genomics and Proteomics, Text Mining and Current Issues in Data Mining.