| Course code | ENGG2720 |
| Course title | Complex Variables for Engineers 複變量及其工程應用 |
| Course description | A first course in complex numbers and the calculus of functions of one complex variable. Topics include complex numbers, complex differentiation, analytic functions and Cauchy–Riemann equations, elementary complex functions, complex integration, series, and residues integration. 本科教授複數和單複變量函數的微積分。內容包括：複數、複積分、解析函數及柯西–黎曼方程、基本複變函數、複積分、級數和留數積分。 |
| Unit(s) | 2 |
| Course level | Undergraduate |
| Exclusion | ENGG2420 or 2460 or ESTR2000 or 2010 or 2014 |
| Semester | 1 |
| Grading basis | Graded |
| Grade Descriptors | A/A-: EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions. B+/B/B-: GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered. C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems. D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems. F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course. |
| Learning outcomes | At the conclusion of the course, students should be able to 1. demonstrate knowledge and understanding of the basic elements of complex analysis 2. identify simple engineering problems that can be formulated and solved using complex analytic techniques |
| Assessment (for reference only) |
Essay test or exam：65% Homework or assignment：25% Others：10% |
| Recommended Reading List | 1. Erwin Kreyszig, Advanced Engineering Mathematics, Wiley, 10th Edition, 2011 2. James Ward Brown and Ruel V. Churchill, Complex Variables and Applications, McGraw-Hill, 8th Edition, 2009 |
| CSCIN programme learning outcomes | Course mapping |
| Upon completion of their studies, students will be able to: | |
| 1. identify, formulate, and solve computer science problems (K/S); | |
| 2. design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs (K/S); |
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| 3. receive the broad education necessary to understand the impact of computer science solutions in a global and societal context (K/V); | |
| 4. communicate effectively (S/V); |
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| 5. succeed in research or industry related to computer science (K/S/V); | |
| 6. have solid knowledge in computer science and engineering, including programming and languages, algorithms, theory, databases, etc. (K/S); | |
| 7. integrate well into and contribute to the local society and the global community related to computer science (K/S/V); | |
| 8. practise high standard of professional ethics (V); | |
| 9. draw on and integrate knowledge from many related areas (K/S/V); |
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| Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes; T = Teach; P = Practice; M = Measured | |