Student outcomes for this program will be the following:
Demonstrated ability to apply mathematical and statistical principles to the analysis of data
Demonstrated ability to develop and implement data analysis strategies base on theoretical principles, ethical considerations, and detailed knowledge of the underlying data
Demonstrated ability to identify and classify relevant variables to develop appropriate machine learning and statistical models for effective actionable insight into the underlying data
An additional criterion specific to graduate programs is that students demonstrate an ability to apply masters level knowledge to the specialized area of data science. Students will be required to maintain a reasonably high level of performance while enrolled in the program. Students must achieve a B- or better in all required classes for the MS degree and graduate with a GPA of 3.2 or higher.
Several different methods of evaluation will be used to assess the program, the first being a survey of the students that are currently in the program. The purpose of this survey is to see if the desired goals and the expectations of the students are being met. The second will consist of surveys of both graduates from the program and the employers of those graduates. The graduates will be evaluated to determine whether the program met the needs of their current employer or other past employers and how the program could be improved. The employers will be asked whether or not the program is meeting their needs for data scientists. The project and thesis courses will also be used as an assessment tool to determine if students have gained the knowledge and skills they were to have acquired in their graduate courses. The results from these three sources of information will be analyzed and used to implement necessary changes every other year in a process of continuous improvement.