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.
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.
Applicants to the Master of Science in Data Mining program are expected to have completed two semesters of applied statistics (such as STAT 104/STAT 453, STAT 200/STAT 201, or STAT 215/STAT 216) with grades B or better, or two semesters of statistics approved by advisor with grades B or better, or permission of the Data Mining Program Director. The second semester course may be taken concurrently with STAT 521 Into to Data Mining.
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.
In addition to the materials required by the School of Graduate Studies, the following are required by the program:
- 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, (c) future professional aspirations, and (d) where and how the applicant has completed the program prerequisites. The essay will also be used to demonstrate a command of the English language.
- 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 official transcripts should be sent to the Graduate Admissions Office. Instructions for uploading the essay and prerequisites letter, and for obtaining and submitting the recommendation letters, will be found within the online graduate application.
Course and Capstone Requirements
The following courses are required of all students.
Choose any two courses from the following list:
Other appropriate graduate course, with permission of advisor
Total Credit Hours: 33
Total Credit Hours: 33