This course introduces an overview of the characteristics and various applications of research. Though the classification of the types of research is generally discussed, this course emphasizes the quantitative approach being applied in natural, formal, applied and social sciences. Students will learn to conduct research based upon a step-by-step research process which outline the various tasks one need to undertake in the following three phases: (a) research problem formulation phase; (b) research study planning phase; and (c) research study management phase. Students will be trained to disseminate research findings and their implications through scholarly writing and oral presentation. Over the course of study, students are expected to enhance the following generic skills: (a) problem solving and scientific skills; (b) verbal and written communication skill; and (c) information retrieval and lifelong learning skills.

This course is one of the core courses for Decision Science undergraduate students. It covers the following topics: Network Models, Dynamic Programming, Queuing Analysis, Simulation, Forecasting, and Markov Analysis. This course will expose students to the deterministic and probabilistic decision making techniques in solving the authentic and hypothetical problems.

This course is one of the core courses for Bachelor of Decision Science programme. Simulation technique is a tool for solving real-world system problems by providing students with the ability to create computer simulation models and analyze their simulation results. The course introduces the modern concepts, model development process, methodologies and applications, and benefits of using discrete simulation in system design and analysis.

This course is one of the core courses for Bachelor of Decision Science programme. It covers the following topics: basic decision models, utility theory, game theory, multi attribute decision analysis and multi-objective decision analysis.

Game Theory is an analysis of situations in which the payoff of a decision maker depends not only on his own actions but also on those of others. The course will pave a way to learn in analyzing such strategic interaction and decision making. It is designed to provide the basic concepts of game theory and its approaches to problem solving. It emphasize on the conceptual analysis and applications, keeping the level of mathematical technicalities to a minimum. It also covers several applications of game theory in real life problems.

This course consists of two main parts: basic aspects of statistics and probability & its distribution. Topics that will be discuss in basic aspects of statistics includes sampling, data collection and descriptive data, central tendency and dispersion measurement. In the second part of this course, the students will be taught on how to find probability for certain random variables and sample statistics. Thus, the basic concept of probability, some special distribution for discrete and continuous random variables, and also the distribution for sample statistics will be discuss.