| Course Code and Name: SEEM3650/ ESTR3516 Fundamentals in Decision and Data Analytics | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Course Objectives: This course introduces the basic concepts of decision and data analytics from a statistical and probability view. Topics include linear regression, classification, sampling techniques, model selection, decision trees, principal component analysis, clustering, and their applications in decision making. |
||||||||||||||
| Course Outcomes: Upon completion of this course, students will be able to 1. understand the basic principles of data analytics tools, such as regression models, classification, decision trees, principal component analysis, and clustering. 2. apply data analytics tools to decision making problems. | Programme Outcomes: (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 |
|||||||||||||