Year 3 Modules Taken

DAO2703 – Operations and Technology Management

This module provides an introduction to the substantive knowledge which has developed over the years in the field of Operations and Technology Management. It builds around the foundational topics of operations and highlights the relevance and strategic significance of technology and the operations function in enterprises. Topics covered include production technology, process analysis and process technology, quality management, and the role of technology in process control in both manufacturing and service organizations. Use of coordination technology such as ERP by firms to match demand and supply efficiently and effectively, operations strategy, and sustainability will also be introduced.

Grade obtained: A

DBA3803 – Predictive Analytics in Business

Managerial success rests strategically on the ability to forecast the demand for the goods and services that a firm provides. Demand forecasting drives the effective planning of the supply chain: personnel requirements, capital investment, production schedules, logistics etc.This module surveys forecasting techniques and their applications. These encompass traditional qualitative (e.g. front line intelligence, Delphi method) and quantitative techniques (e.g. regression, time series) as well as emerging techniques based on neural networks. Concepts such as trends, seasonality and business cycles will be discussed. Their value in improving forecasts will be illustrated. The module makes extensive use of software including MS Excel and dedicated forecasting packages.

Grade obtained: A+

EE4002R – Research Capstone

This research capstone project provides students with an opportunity to work on a complex engineering problem with a strong element of investigative and exploratory research. It requires a confluence of knowledge, skills and capabilities in project management and communications. The project will involve a varied blend of research, design and development activities and is carried out over two semesters. The project proposal can come from a faculty member or student. It may arise during the student’s industrial attachment or as part of an on-going research project and may involve direct industrial and research institutes’ participation. Students will be assessed individually.

Grade obtained: A

EE5907 – Pattern Recognition

Pattern recognition deals with automated classification, identification, and/or characterizations of signals/data from various sources. The main objectives of this graduate module are to equip students with knowledge of common statistical pattern recognition (PR) algorithms and techniques. Course will contain project-based work involving use of PR algorithms. Upon completion of this module, students will be able to analyze a given pattern recognition problem, and determine which standard technique is applicable, or be able to modify existing algorithms to engineer new algorithms to solve the problem. Topics covered include: Decision theory, Parameter estimation, Density estimation, Non-parametric techniques, Supervised learning, Dimensionality reduction, Linear discriminant functions, Clustering, Unsupervised learning, Feature extraction and Applications.

Grade obtained: A

FIN2704X – Finance

This course helps students to understand the key concepts and tools in Finance. It provides a broad overview of the financial environment under which a firm operates. It equips the students with the conceptual and analytical skills necessary to make sound financial decisions for a firm. Topics to be covered include introduction to finance, financial statement analysis, long-term financial planning, time value of money, risk and return analysis, capital budgeting methods and applications, common stock valuation, bond valuation, short term management and financing.

Grade obtained: A+

EG3611A – Industrial Attachment

This internship module is for B.Eng. degree with a compulsory 20-week internship. The type of internship varies according to the programmes. Internships integrate knowledge and theory learned in the classroom with practical application and skill development in a professional setting. It enables students to learn about the latest developments in the industries and to interact with engineers and other professionals as they join projects or tasks that help to develop or enhance their skills whilst contributing to the organization. Students can extend their internship module by another 4 weeks and earn additional 2 MC by registering EG3611B Industrial Attachment.

Grade obtained: TBC (graded on CS/CU basis)