MSc Business Analytics and Technology Management
The course focuses on systematic treatments of linear statistical models for regression, analysis of variance and experimental design with special emphasis on applications in business and economics. Topics include regression analysis: inference, model building, diagnostics, remedial measures and validation; single-factor and two-factor ANOVA models, and analysis of covariance. Other statistical tools for specialized applications discussed may include logistic regression, path analysis and time series regression. Case studies are employed to illustrate tools for fitting, checking, validating and interpreting linear models.
The objective of this seminar is to provide a basic understanding of the research process and a knowledge of the methods used in the design and execution of scientific research relevant to social sciences, and specifically the business context. The seminar helps students to develop skills needed to assess the feasibility and potential contribution of proposed studies, and to critically evaluate research reported by others. The application of relevant research methods are reviewed through discussions of exemplary articles published in leading journals. Cornerstone topics in this seminar include: theory construction, measurement, overview of data collection methods, reliability, as well as internal and external validity issues.
Prerequisites & notes
Students who have taken MSCA 612, MSCA 613, MSCA 614 or MSCA 616 may not take this seminar for credit.
Students are required to attend a minimum of three professional development workshops approved by the Graduate Program Director and offered in collaboration with different partners (e.g. GradProSkills). These workshops complement students' academic training and provide them with technical skills that help them succeed professionally and academically. Component(s): Workshop
Prerequisites & notes
Prerequisite/Corequisite: Permission of the Graduate Program Director is required
This course is assessed on a pass/fail basis.
This seminar provides students with an overview of the Business and Technology Management (BTM) literature in a range of research areas, exposing students to classic and modern BTM literature that has been influential in the development of the field. Very early articles and more recent articles are reviewed. The main course objectives are to help students develop an understanding of the evolution of the BTM discipline and identify major research areas, including ethical issues in business technology management. The course will follow a seminar format and will focus on the discussion of assigned readings.
The seminar covers essential ideas and techniques for analyzing and extracting information from large amounts of data. The course begins with the discussion of ethical issues in business analytics. It discusses both supervised and unsupervised methods, and covers topics such as dimension reduction, classification and regression trees, K-nearset neighbors, neural networks, association rules and collaborative filtering, cluster analysis, ensemble methods, boosting and bagging, illustrations of the concepts and methods are given, and students gain practical experience in data mining with the use of popular data mining software.
Electives (specialized seminars)
The course is planned to provide students with practical knowledge of analyzing multivariate data arising in business applications and research. The multivariate methods of data analysis provide an effective approach to describe and understand structure and the relationships between variables of interest. A wide range of statistical methods most commonly used in practice is introduced. The topics covered include methods of dimensionality reduction to better visualize and understand complex data, structured approaches in studying inter-relationships between the measured variables, analysis of their dependency and various classification techniques. Extensions of the analyses of experimental design to include multi-dimensional responses are also considered. Examples from business and other disciplines are analyzed with the extensive use of statistical software. The focus of the course is on data analysis and results interpretation rather than mathematical theory of multivariate methods.
This course covers the strategic management of information technology and investigates the potential of information technology to improve organizational competitive advantage. Students will be exposed to topics such as strategic alignment of information technology, information technology governance, impact of information technology on organizational transformation, strategic management of global information technology. Managerial responsibilities, strategies, and research issues are presented through readings, critiques, discussions, and presentations. With the knowledge acquired during this course, students will be able to meet the challenges of promoting the use of information technology as an authentic strategic asset.
Program: M. Sc.
This course provides a comprehensive foundation for designing, building, and working with databases, enabling students to understand and use commercially available database products effectively. The course examines different models of representing data with emphasis on the relational model. Topics include data modeling, database design, queries, transaction management, implementation issues, and an overview of distributed database management systems, data warehouses, databases in electronic commerce, database administration, and knowledge management. Examples are drawn from various functional and operational areas including enterprise and supply chain operations, management, and planning.
This course covers advanced data mining concepts and algorithms for analyzing and extracting information from large amounts of data. The course covers topics such as deep neural networks, text mining, social media analytics, graphical models and Bayesian learning. In addition, the course covers advanced data visualization techniques. The course includes the discussion of theoretical concepts and analysis based on real-world data.
More than one topic can be offered under this course. In such cases, the name of the topic will be indicated on the class schedule under Topic.
Prerequisites & notes
MSCA 693A: Digital Innovation: Theory and Practice
This course includes advanced topics in information systems design, development, and implementation. Topics include information systems development lifecycle, information systems development methodologies, information/data management, information security, and information systems deployment and implementation techniques. The course will include the discussion of theoretical concepts and analysis based on academic and practitioner literature.
*Please note that all course listing on this page is subject to change and that the content of the seminars may vary from a year to another. Current students should consult the graduate calendar or refer to their program team at email@example.com for the most updated information.