Computing for Science and Engineering
Learning Objective:To expose students to computational concepts, tools, and skills useful for research in computational science and engineering, beyond what is learned in a first programming course (and basic mathematics courses). A related aim is to prepare students for other courses in CS&E.
Intro course for Computational Engineering concentration. Computational concepts, tools, and skills for computational science and engineering: scripting for numerical computing, scripting for file processing, high performance computing, and software development. Project may be required. Credit in this course may not be used toward a graduate degree in Computer Science. Fall 2016 Syllabus
Topics Covered:Unix; Python scripting and C programming for numerical computation and file processing; introduction to data structures and computer science concepts; parallel programming; introduction to object-oriented programming, software engineering, and GUIs; matrix operations and interpolation.
Prerequisites:A bachelor degree in computer science or an equivalent field. Students not in the Computer Science master's program should seek department permission to register.
Familiarity with matrix algebra is expected. Some programming experience in C, C++, Java, or Fortran or extensive scripting experience; also, commensurate computer skills.
Applied / Theory:70 / 30
Web Content:Syllabus, lecture notes, homework assignments, and solutions will be available via Piazza; grades via Blackboard.
Homework:About six or seven assignment sets, much of it requiring the writing of programs. Submitted via Blackboard.
Exams:One midterm, No written final
Textbooks:Official textbook information is now listed in the Schedule of Classes. NOTE: Textbook information is subject to be changed at any time at the discretion of the faculty member. If you have questions or concerns please contact the academic department.
Tentative: None Required - Notes will be distributed with links to Web resources.