My Course Work
Assistant Professor, University of Alberta
Doctoral Degree
The course provides detailed learning in the field of expert systems (ES) and decision support systems (DSS) with an emphasis on business applications. It combines the technical, business and managerial aspects of developing expert systems (ES) and decision support systems (DSS).
The course provides detailed methodologies, techniques used and deliverables created in developing large-scale information systems that includes preliminary planning, feasibility analysis, design implementation, and post-implementation review of the system.
The course entails data collection, classification, verification, and transmission; file organization, including sequential and random processing techniques, record locating, overflow procedures, and file security; analysis of alternative methods of data organization; commercial file management systems; design of data processing systems.
The course stresses a top-down, business oriented approach to evaluate and select data communications. The course provides practical knowledge of network telecommunications technology including hardware and software used in organizations.
The course is designed to provide students with "real world" experience as Business Information Consultants on cross-functional teams working on actual projects for business and industry clients. In addition to regular class sessions, students are assigned to work with teams of MBA students, providing crucial assistance in accurately defining and satisfying the clients' project information needs. Class sessions are designed to a) increase students' understanding of information services within the context of an organization and, b) to provide a framework for students to explore new organizational roles related to information use.
The course gives an introduction to the nature of knowledge and the problems it presents for research, work and technology support for knowledge that spans organizations, disciplines, and geographic distance. The focus is on understanding the nature of distributed knowledge processes and issues, and exploring the social and technical infrastructure that can support knowledge creation, sharing, exchange and dissemination.
The course provides theory and practice of research methodology for the study of administrative, industrial, and consumer behavior and organizations; alternative methods of data collection and their strengths and weaknesses; observational, questionnaire, field, and laboratory experimentation and statistical analysis of pre-gathered time-series and cross-sectional data; and examples of good and bad research in business disciplines.
The course explored early, visionary systems that shaped the field of human-computer interaction, usability engineering methods, models of human abilities, and empirical research methods. These core topics will then be followed by an in-depth investigation of several hot-topic research areas such as design tools that enhance human creativity, computational models of human attention, and user interfaces for pervasive computing.
This doctoral level seminar will explore research issues related to collaborative computing. The focus will be mostly on issues of usability and acceptance of technologies into the work setting, and the design process to achieve that. This includes aspects of analysis, requirements specification, tailoring, usability, learnability, and their incorporation into applications development.
The doctoral level independent study. The objective of the study was to explore idea of developing Ubiquitous Knowledge Management System (UKMS) by integrating ubiquitous computing concepts to Knowledge Management System.
The doctoral level independent study. The objective of the study was to explore the potential of a representation to facilitate the sharing of technical how-to help in workplace environments. Experiment was conducted on comparing help support by screenshots of trails through a computer application made by others and sharable through a Knowledge Management System with conventional built-in application help files, and with the ad hoc individualized face-to-face help provided by colleagues in informal workplace knowledge sharing interactions.
MS Finance Degree
The course addresses both the theoretical and applied aspects of firms' financing decisions; topics include capital structure and cost of capital theories; mergers, acquisitions and leveraged buyouts; options, warrants, and convertibles; venture capital and initial public offerings; and pensions.
The courses teaches the characteristics of the international financial market, related topics such as international parity conditions, exchange rate risk management, country risk, cross-border investment analysis, multi national firm budgeting, hedging in foreign currency markets, accessing international financial markets for financing, and competitive strategy in a global marketplace. The course also examines various aspects of corporate financial management.
The course gives experience in valuing firms with focus on mergers and acquisitions issues and cover topics other related topics such as initial public offerings, leveraged buyouts, spin-offs, and divestitures.
The course is on options, futures, swaps and other derivative securities, examination of institutional aspects of the markets, theories of pricing, trading strategies (arbitrage, hedging, and spread) and applications for asset and risk management.
It is case study oriented course examining how corporations deal with risks and what are the risk management techniques and approaches.
The course emphasizes on microeconomic theory; principal topics include a review of value and distribution theory, the theory of choice by households and firms, general microeconomic theory, and theoretical developments of current interest.
The course covers classical statistics and regression analysis, descriptive statistics, probability and point and interval estimation, decision theory, variance analysis, and linear regression and least-squares estimates.
The course teaches the construction of econometric models, characteristics of models and choice of estimating methods and estimates of parameters by various methods, Bayesian statistics and decision theory.