A New Course on R&D Project Management in Computer Science and Engineering: Subjects Taught, Rationales Behind, and Lessons Learned

Abstract

This chapter describes the essence of a course for senior level undergraduate students and for master students of computer science and engineering, and analyzes its effects. The course prepares students for their professional life after graduation, and especially, it prepares them for the challenges related to efforts to bring new paradigm-shifting ideas into the commercial world. This course was developed to complement a DataFlow course and to teach DataFlow researchers about issues of importance for promotion of their results with a commercial potential. Consequently, course examples and homework assignments were chosen to reflect issues of importance in the commercialization of the DataFlow concept. The course includes the following subjects (presented with DataFlow-related issues in mind): (a) Writing proposals for Research and Development in industry and academia, (b) Understanding the essence of the MBA/PhD degrees and preparing the GMAT/GRE analytical exam, (c) Understanding Capability Maturity Model Integration and learning how to write holistic strategic project plans, (d) Understanding Project Management and learning how to write detailed tactical project plans, (e) Writing business plans for venture capital or business angels, (f) Writing patent applications, (g) Writing survey papers for SCI (Science Citation Index) journals, (h) Writing research papers for SCI journals, (i) Making an Internet shop, (j) Making a MindGenomics campaign for the Internet shop, (k) DataMining from project history and project experiments, and (l) Preservation of project heritage and skills related to brand making. Each subject matter is covered by a homework assignment to help deepen the practical knowledge of the subject matter covered. In addition to the above described, which is accompanied with homework, the following four subjects are also covered and accompanied with in-class discussions (oriented to DataFlow research): (m) Inventivity, (n) Creativity, (o) Effectiveness, and (p) Efficiency. Consequently, the analysis part concentrates on the following issues: (a) Inventivity: How different majors react to the subject matters, (b) Creativity: How efficiently the initial knowledge gaps get bridged, (c) Effectiveness: How the experience of the teacher helps, and (d) Efficiency: How the previous experiences of students help.