|Course Code and Name: SEEM4630 E-Commerce Data Mining
As an introductory course on data mining, this course introduces the concepts, algorithms, techniques, and systems of data warehousing and data mining, including (1) data preprocessing, (2) design and implementation of data warehouse and OLAP systems, (3) methods for effective and scalable data mining, including frequent pattern and correlation analysis, classification and predictive modeling, and cluster analysis. Applications of data mining techniques in e-commerce and business are covered. Hands-on practice on data mining packages is given.
1. Able to perform data analysis with a data mining approach which includes data preprocessing, data integration and warehousing, data mining, result evaluation and presentation.
2. Able to formulate a real task into a data mining problem and choose an appropriate method as the solution, according to problem characteristics, data distribution, and constraints etc.
3. Able to evaluate the mining results and present them, able to interactively tune the mining process based on users' feedback.
4. Able to apply data mining to applications in engineering domains and help managerial decision making.
(P1) The ability to apply knowledge of mathematics, science, and engineering appropriate to the degree discipline (K/S)
(P2) The ability to design and conduct experiments, as well as to analyze and interpret data (K/S)
(P3) The ability to design a system, component, or process to meet desired needs within realistic constraints, such as economic, environmental, social, political, ethical, health and safety, manufacturability and sustainability (K/S)
(P4) The ability to function in multi-disciplinary teams (S/V)
(P5) The ability to identify, formulate, and solve engineering problems (K/S)
(P6) The understanding of professional and ethical responsibility (V)
(P7) The ability to communicate effectively (S)
(P8) The ability to understand the impact of engineering solutions in a global and societal context, especially the importance of health, safety and environmental considerations to both workers and the general public (V)
(P9) The ability to recognize the need for, and to engage in life-long learning (V)
(P10) The ability to stay abreast of contemporary issues (S/V)
(P11) The ability to use the techniques, skills, and modern engineering tools necessary for engineering practice appropriate to the degree discipline (K/S)
(P12) The ability to use the computer/IT tools relevant to the discipline along with an understanding of their processes and limitations (K/S/V)
(P13) The ability to apply the skills relevant to the discipline of operations research and information technology and their applications in engineering and managerial decision making, especially in financial services, logistics and supply chain management, business information systems, and service engineering and management (K/S)
K = Knowledge outcomes
S = Skills outcomes
V = Values and attitude outcomes
|Weights (in %):|
|Course Outcome(s) is/are measurable or not: Yes
If Yes, please suggest ways to measure:
This course contributes to
(P1) by teaching knowledge of mathematics, statistics and database technology. It could be measured by midterm and final.
(P2) by giving students practice in data analysis and interpretation, and mining result evaluation. It could be measured by assignment, midterm and final.
(P5) by teaching elements of it and giving students practice in problem formulation and solving. It could be measured by midterm and final.
(P11) by teaching elements of it, and giving students practice in applying them. It could be measured by assignment.
(P12) by teaching elements of it, and giving students practice in applying them. It could be measured by assignment.
(P13) by teaching elements of it, and giving students practice in applying them. It could be measured by assignment, midterm and final.