Data Mining M.S.
The Master of Science in Data Mining prepares students to find interesting and useful patterns and trends in large data sets. Students are provided with expertise in state-of-the-art data modeling methodologies to prepare them for information-age careers.
Program Learning Outcomes
Students in the program will be expected to:
• approach data mining as a process, by demonstrating competency in the use of CRISP-DM (the Cross-Industry Standard Process for Data Mining), including the business understanding phase, the data understanding phase, the exploratory data analysis phase, the modeling phase, the evaluation phase, and the deployment phase;
• be proficient with leading data mining software, including IBM/SPSS Modeler (formerly Clementine),WEKA, Perl, and the R language;• 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; and
• understand and apply the most current data mining techniques and applications, such as text mining, mining genomics data, and other current issues.
Applicants must hold a bachelor’s degree from a regionally accredited institution of higher education. The minimum required undergraduate GPA for prospective candidates for the Master of Science in data mining is 3.00. Conditional admission may be granted to candidates with undergraduate cumulative GPA that is within the range of 2.40 – 2.99.
Additional Materials Required
1. A formal application essay of 500–1000 words that focuses on (a) academic and work history, (b) reasons for pursuing the Master of Science in data mining, and (c) future professional aspirations. The essay will also be used to demonstrate a command of the English language.
2. A letter explaining whether and how the candidate has fulfilled the program prerequisite of one course in statistics.
3. Two letters of recommendation, one each from the academic and work environment (or two from academia if the candidate has not been employed). The deadline for submitting applications for the fall semester is May 1. The deadline for submitting applications for the spring semester admissions is November 1.
Applicants must submit a completed admissions application, the application fee, and official transcripts from each undergraduate and graduate institution to the Graduate Recruitment and admissions Office.
All additional materials (formal application essay, the prerequisites letter, and the two letters of recommendation) must be sent to:
Data Mining Program Coordinator
Re: MS in Data Mining Admissions Materials
Department of Mathematical Sciences
Marcus White 118
Central Connecticut State University
New Britain, CT, 06050
Note: Only hard copy materials are acceptable for additional materials submitted. No attachments to e-mails or other electronically transmitted material will be considered in admissions decisions.
Contact: Larosed@ccsu.edu www.ccsu.edu/grad
Course and Capstone Requirements (36 credits): Core Courses (27 credits)
The following courses are required of all students. (All courses three credits unless otherwise indicated.)
STAT 416 Mathematical Statistics II
STAT 521 Introduction to Data Mining (4credits)
STAT 522 Data Mining Methods (4credits)
STAT 523 Applied Data Mining (4 credits)
STAT 525 Web Mining
STAT 526 Data Mining for Genomics and Proteomics
STAT 527 Text Mining
STAT 570 Applied Multivariate Analysis
Thesis Course (3 credits)
STAT 599 Thesis
All students must elect capstone Plan A, thesis. Students must make a presentation of their thesis on the CCSU campus. Students who cannot come to campus must make a web presentation of their thesis. Elective Courses (6 credits)
Choose any two courses from the following list:
CS 570 Topics in Artificial Intelligence
CS 580 Topics in Database Systems and Applications
STAT 455 Experimental Design
STAT 529 Current Issues in Data Mining
STAT 551 Applied Stochastic Processes
STAT 567 Linear Models and Time Series
STAT 575 Mathematical Statistics III
Other appropriate graduate course, with permission of advisor
Note: New students may take the first course in the program while working on the prerequisites for the more advanced courses.
Note: No more than nine credits at the 400 level, as approved by the graduate advisor, may be counted toward the graduate planned program of study.