Data Mining, M.S.

This program is offered by the Mathematical Sciences Department

Program Description

State-of-the-art data modeling methodologies prepare graduates for careers in fraud detection, credit card scoring, and personal profile marketing. Students use sophisticated statistical techniques and software to find significant patterns and trends in large data sets. Emphasizes hands-on data mining. Courses available online.

Learning Outcomes

Students in the program will be expected to:

  1. Approach data analysis using a scientific approach, that is, through a systematic process that avoids expensive mistakes by assessing and accounting for the true costs of making various errors.
  2. Apply data science using a systematic process, by implementing an adaptive, iterative, and phased framework to the process, including the research understanding phase, the data understanding phase, the exploratory data analysis phase, the modeling phase, the evaluation phase, and the deployment phase;
  3. Demonstrate proficiency with leading open-source analytics coding software such as R and Python, as well as commercial platforms, such as IBM/SPSS Modeler;
  4. understand and apply a wide range of clustering, estimation, prediction, and classification algorithms including k-means clustering, classification and regression trees, logistic regression, k-nearest neighbor, multiple regression, and neural networks; and
  5. learn more specialized techniques in bioinformatics, text analytics, algorithms, and other current issues.