COM303 and EDM304 is Research in Advanced Business Research Methodologies and Educational Management. This merged course discusses the rationale, principles and procedures in preparing research designs that are appropriate in business research or educational management. Comparative analysis of the strengths and weaknesses as well as the applicability of varied research methodologies is taken up. Ethical principles in research are clarified. Choosing and constructing valid instrument or tools for data collection and the analysis and interpretation of data are likewise stressed in this course.

Community development encompasses a wide range of skills and institutions that local communities employ to improve their citizens' well-being and quality of life. This introduction to the area of community development delves into the definitions of essential concepts like "community" and "development," as well as the responsibilities that various stakeholders play in community development. A multidisciplinary approach will be undertaken to better comprehend community development's historical foundations, as well as essential theories, techniques, and practices. There will be discussions of both older "expert-driven" traditions and more current approaches that prioritize participatory development and community action research. The links that connect communities, the behaviors that spark development, and the manner in which local communities can participate in the design and planning of neighborhoods, programs, campaigns, and initiatives to meet their needs and aims are all topics that will be investigated.

This course deals with the analysis of quantitative data in science education facilitated by the use of statistical software. It involves descriptive and inferential analysis of science education data (univariate, bivariate and multivariate) using spreadsheet application programs (e.g. MSExcel, OpenCalc), software for statistical analysis (e.g. Microstat, SAS, SPSS, Open source PSPP) and modelling applications (e.g. SEM). Computer data analysis tasks to be undertaken include designing of coding schemes, data encoding, data sanitation and management, descriptive analysis, statistical inference, data transformation, interpretation of analyzed data, and modelling.