Graduate
Student Learning Outcomes
Computer and Information Science
Graduate Certificates
The CIS department offers graduate certificates in Biocomputing, Computer Security, Software Engineering, Databases and Data Mining, and Biometrics. After graduation, a student should be able to:
- Demonstrate a sound understanding of computing principles in the chosen area of study (Biocomputing, Biometrics, Computer Security, Databases and Data Mining, Software Engineering).
- As evident from appropriate grades earned to satisfy the core course requirement for a specific certificate program
- Demonstrate an ability to work in a group.
- As evident from successfully developing moderately intense collaborative projects (e.g., semester projects in courses)
- Demonstrate an ability to solve moderately complex problems in the chosen area of study.
- As evident from successful completion of elective courses in Computer Science or related fields, as required by the Certificate program(s)
Master of Science in Computer and Information Science (M.S.)
After graduation, a student should be able to:
- Demonstrate a sound understanding of general fundamental computing concepts (e.g., algorithms, programming languages, operating systems, etc.).
- As evident from appropriate grades earned to satisfy the core course requirements
- Demonstrate a relatively in-depth understanding of a subarea.
- As evident from successfully completing a series of courses in a sub-area (e.g., databases)
- Demonstrate an ability to successfully work in a group and/or demonstrate an ability to successfully carry out moderately complex software projects.
- As evident from successfully developing moderately intense collaborative projects (e.g., semester projects in courses) and/or
- As evident from software development assignments/projects in courses (e.g., projects in networking course)
Additional Expectation from M.S. students choosing Thesis or Project Option:
- Demonstrate an ability to systematically carry out scientific research (empirical and/or theoretical) on a moderately complex problem.
Master of Science in Computational Data Science (M.S.)
After graduation, a student should be able to:
- Synthesize data analysis principles across the statistical and computer sciences in topics such as pattern analysis, prediction, and big data processing.
- Construct data science algorithms, including derivation and programming implementation in a variety of languages and platforms (C++, Python, Java, SAS, R, Matlab).
- Be able to assess new programming language trends in industry, by gaining solid background in computing and algorithmic thinking.
- Differentiate the processes from "raw data to outcome", which spans from considering the domain-specific constraints and charactertistics (e.g., static vs. sequence, sparsity, dimensionality, etc.) to efficient method implementation, as software with desired specifications.
- Integrate advanced knowledge in a broad range of related topics, such as survival analysis in Computer Science.
- Assess different solutions to specific data-specific problems.
- Summarize state-of-the-art data science methods and applications in scientific project reports and software documentation.
Doctor of Philosophy in Computer and Information Science (Ph.D.)
In addition to the above M.S. outcomes, Ph.D. students will:
- Demonstrate an ability to develop original solutions and their validation that extend the state-of-art in a chosen specialization to significant research problem(s) as evident from publications in highly-ranked conferences/journals.
