Computer Science

Professional Workforce Development

CS410 & CS411

CS410 Current Semester

CS411 Current Semester

Previous Projects CS410 & CS411

Computer science traces its foundation to mathematics,logic and engineering. Studies in computer science range from theory through experimental techniques to engineering methodology.The computer science curriculum exposes students to each of these disciplines and fosters an appreciation and understanding of them.Students grasp the broad theoretical basis of computer science in a context of strong laboratory components.

The Computer Science Department's curricular model increases the relevance of computer science education to the real world.The capstone Professional Workforce Development(PWD) courses,in a new theme each semester,go beyond the experimental and design approaches of typical computer science curricula by emphasizing the creativity and productivity required for business and industrial applications today.

The first course,CS 410, is presentation/collaboration intensive,with grading and evaluation of students performed by faculty and industry review boards.The topics and learning objectives include:technical research,market research,presentation skills,group collaboration,interviews,budgeting,proposal writing,presentation tools,scheduling,hardware availability research, system architectural design,requirements specification, simulation,prototyping,and cost estimation.

The second course,CS411, is writing intensive, with grading and evaluation of students performed by CS faculty,English graduate students, and industry review boards.Documents required in the writing intensive course include:descriptive papers,requirement specifications,test plans, user manuals,and grant proposals.A working laboratory prototype is developed and demonstrated for evaluation.All student projects can be accessed through this page by utilizing the links above.The products were never developed for production or for sale to the general public.


NSF Proposals and Reports

This work is funded by the National Science Foundation under grant #CDA-9214930 and Old Dominion University.