Master of Science in Data Mining

Program Rationale:

  • 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.

Learning Outcomes for Program Graduates:
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 WEKA, Clementine by SPSS, 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.

Admission Requirements

Students must hold a Bachelor's degree from a regionally accredited institution of higher education. The undergraduate record must demonstrate clear evidence of ability to undertake and pursue studies successfully in a graduate field.

A minimum undergraduate GPA of 3.00 on a 4.00 scale (where A is 4.00), or is equivalent, and good standing (3.00 GPA) in all post-baccalaureate course work is required. Conditional admission may be granted to candidates with undergraduate GPAs as low as 2.40, conditioned on a student receiving no grades lower than a B in the first three core courses in the program.

The following materials are required, in addition to the materials required by the School of Graduate Studies.

  • 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.

Students may be admitted on condition that they complete these prerequisite courses with a grade of B or better. First-semester courses in statistics are regularly offered by CCSU both online and in the classroom.

  • Two letters of recommendation, one from each the academic and work environment (or two from academia if the candidate has not been employed).

The application and all transcripts should be sent to the Graduate Admissions Office. The deadline for submitting applications for the fall semester is May 1. The other materials, including the formal application essay, the prerequisites letter, and the two letters of recommendation, should be sent to:

Dr. Daniel T. Larose
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. No attachments to e-mails or other electronically transmitted material will be considered in admissions decisions.

Course and Capstone Requirements (33 credits):
Core Courses (27 credits)
The following courses are required of all students.
STAT 520 Multivariate Analysis for Data Mining (new course) 4
STAT 521 Introduction to Data Mining 4
STAT 522 Clustering and Affinity Analysis 4
STAT 523 Predictive Analytics 4
STAT 526 Data Mining for Genomics and Proteomics 4
STAT 527 Text Mining 4
STAT 599 Thesis 3
Elective Courses (6 credits)
Choose any two courses from the following list:
CS 570 Topics in Artificial Intelligence: Machine Learning 3
CS 580 Topics in Database Systemsand Applications: Data Mining 3
STAT 455 Experimental Design 3
STAT 456 Fundamentals of SAS 3
STAT 465 Nonparametric Statistics 3
STAT 525 Web Mining 3
STAT 529 Current Issues in Data Mining 3
STAT 551 Applied Stochastic Processes 3
Other appropriate graduate course, with permission of advisor
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