Master of Business Analytics (MBAN)

Students are introduced to the nature, organizational structure, aims and objectives of a range of business models and entities, and their role in society. Students develop literacy and competence in aspects of marketing, operations, finance, management and leadership as they relate to the operations of a business.

Students learn the essential programming languages in data science; topics include, but are not limited to Python, R, SQL, Power BI and Tableau. Students will learn how to solve real-world and practical tasks, such as healthcare information systems, database design, and data management.

Students are provided with a comprehensive understanding of big data skills, aligning closely with certified programs in analytics. Topics include business problem framing, analytics problem framing, data collection and preparation, methodology selection, model building, and model deployment and lifecycle management.

Students learn to manage complex datasets, select appropriate models, and apply predictive techniques to generate actionable insights. Applications span various domains. Hands-on work with real data such as healthcare data enhances analytical skills and supports data-driven decision-making.

Students are provided with a framework to examine the ethical and governance implications of collecting, managing, and using big data for business decision-making. Students acquire the knowledge and skills necessary to assess the impact of the business analytics field on modern society, applying principles such as fairness, accountability and transparency.

A key element of decision-making is to identify the best course of action. Since businesses problems often have many alternative solutions, students learn how optimization can help businesses identify the best decision option. Students are also introduced to applied topics include computer modeling and data visualization.

Students are introduced to organizational information systems and business processes and workflows. Students identify where business data come from, its inherent strengths and weaknesses, and how it can be best organized to generate appropriate information for business analytics models that support operational and strategic decisions effectively and efficiently.

Students are introduced to effective machine learning techniques and the fundamentals of artificial intelligence. Using tools in statistical learning, students focus on major supervised and unsupervised machine learning methods. Students study systems that require learning algorithms built in penalized linear and logistic regressions and non-parametric learning techniques.

Students learn key concepts in health information systems, healthcare analytics, and AI for Health. Aligned with the CPHIMS certification, it equips students to evaluate healthcare data, apply AI tools, and assess digital health solutions. Topics include workflows, data management, interoperability, system lifecycles, and equity in digital transformation.

Students are introduced to various accounting and financial decision-making tools and their applications in business practices. Excel-based modelling and dedicated statistical packages are used to delve into some of the industry best practices of financial analytics. In essence, this course is a gateway for students, pursuing careers in business analytics, to conduit their accounting and financial knowledge to actual enterprise applications.

Students are prepared to be effective marketing leaders in a landscape that involves digital initiatives and data. The course is designed to help students develop a working knowledge of the current digital marketing ecosystem, common strategic goals of digital marketing, and types of data typically available for analysis.

Students cover a variety of topics related to effective use of analytics for strategically advancing people management decisions regarding issues such as recruitment, evaluation, diversity, compensation, and training. The ethical, legal, and practical implications of such decisions will be discussed.

Students learn how to make effective decisions in operations and supply chain management using data tools. Students are introduced to frameworks and ideas that provide insights into a spectrum of real-world business challenges.

Students develop skills and competencies in gathering, synthesizing and processing big data to inform decision making in the provision of care, and improving systems and operations used in healthcare organizations and practices.  Students engage on real world situations and challenges facing healthcare, in particular, patient outcomes, wait time management and resource allocation and utilization. Insights gained through this course will be of value to healthcare professionals, leaders, managers and administrators and ultimately enhance the systems and processes used by healthcare organizations and practices in the delivery of healthcare excellence.

Students develop an understanding of the contribution data mining and data visualization brings to business intelligence and decision-making processes in organizations and practices. The course will explore theoretical frameworks and leverage these to guide the collection, evaluation, analysis and the communication of data insights to inform decision making for tactical and strategic purposes.  Students consider the origins of data mining and data visualization in organizational settings, the application in various functional areas such as healthcare, marketing, operations, and finance, and the software and tools used.

Through a series of workshops led by internal and external experts, students work through a detailed process to create a customized professional development plan. Supported with personal coaching and engagement with business professionals, students work to enhance targeted competencies and create their own professional development plan to achieve future goals.

In this capstone experience, students from all three experiential streams will come together on campus to engage in hands-on learning opportunities, applying concepts learned in courses and drawing on work-integrated learning experiences. These immersive learning activities will vary annually and could include hackathons, case competitions, and/or service-learning projects.

This option provides students with an opportunity to apply course concepts and theories while working as a consultant. Students are assigned individually or in teams to a client of the University’s entrepreneurship centre and work with a faculty supervisor to develop a detailed consulting report in four months.

This option provides students with an opportunity to apply concepts and theories learned in the program in an organizational setting. Students are assigned individually or in teams to a business or non-profit to apply their knowledge and skills on real world data and analytics projects over four months.

Students complete a four-month project involving the practical application of research concepts and techniques used in a particular discipline, under the supervision of a faculty member or group. Emphasis in this option is on developing research experience using skills and knowledge gained through MBAN program. Interdisciplinary projects are acceptable.