| Course code | ENGG5103 |
| Course title | Techniques for Data Mining 數據採集技術 |
| Course description | Data mining provides useful tools for the analysis, understanding and extraction of useful information from huge databases. These techniques are used in business, finance, medicine and engineering. This course will introduce the techniques used in data mining. Topics will include clustering, classification, estimation, forecasting, statistical analysis and visualization tools. 數據採集(data mining)技術是針對巨型數據庫有用資料之提取與分析提供的有用工具。這些技術通用於商業、財務、醫藥與工程。本科將介紹此方法有關的包括聚類法、分類法、估值法、預測模型、統計分析與可視化的技術方法 |
| Unit(s) | 3 |
| Course level | Postgraduate |
| Exclusion | CMSC5724 or CSCI5180 |
| Semester | 1 or 2 |
| 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 end of the course of studies, students will have acquired the ability to 1. describe the whole data mining process, 2. identify the objectives and tasks of data mining problems, 3. select and apply the appropriate techniques to solve data mining problems |
| Assessment (for reference only) |
Essay test or exam: 60% Others: 40% |
| Recommended Reading List | 1. Tan, Steinbach and Kumar, Introduction to Data Mining. Addison Wesley, 2006 |
| 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); | TP |
| 2. design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs (K/S); |
T |
| 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); |
|
| 5. succeed in research or industry related to computer science (K/S/V); |
T |
| 6. have solid knowledge in computer science and engineering, including programming and languages, algorithms, theory, databases, etc. (K/S); | T |
| 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); |
T |
| Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes; T = Teach; P = Practice; M = Measured | |