Master of Business Analytics

Fuse your passion for data science and business to drive organizational value

The Master of Business Analytics (MBAN) program is designed to meet the job market growing need for talented business analytics professionals with cutting-edge skills and applied knowledge for developing, evaluating, refining, and implementing analytics solutions to turn data into insights and drive business innovation and success. Join the Sobey MBAN program to benefit from a unique opportunity to fuse and advance your data science and business competencies and cultivate an analytical mindset to lead and sustain the efforts of small, medium, and large organizations in designing, deploying, and monitoring solutions for data-driven business decision-making and value creation. The MBAN program integrates and emphasizes the following:

• Business analytics skills including descriptive, predictive and prescriptive analytics;
• Programming skills and computer languages;
• Data governance, security, and privacy;
• Business fundamentals and managerial practice;
• Ethical, evidence-based and responsible management and research.

The MBAN is a 42-credit hour program that can be completed in 16 months of full-time study (4 semesters). The program consists of 10 required courses, 5 elective courses and 1 of 3 experiential learning pathways: Consulting, Research, and Internship. In addition, a longitudinal Professional Development course will ensure that students have the professional skills required for success in their chosen career. The MBAN curriculum is aligned with the Body of Knowledge of the highly prized Certified Analytics Professional (CAP) designation and prepares you to write the CAP exam. The MBAN will be AACSB-accredited and will embrace the Sobey School of Business commitment to the Principles for Responsible Management Education (PRME) and Evidence-Based Management (EBM).

Sobey MBAN Brochure

Required courses include

• Foundations of Professional Analytics
• Business Fundamentals
• Fundamentals of Programming and Database Management
• Ethics and Governance in Analytics
• Statistics and Predictive Modeling
• Prescriptive Analytics and Optimization
• Enterprise Data Management and Strategy
• Machine Learning and Artificial Intelligence
• Financial and Accounting Analytics
• Marketing Analytics
• Analytics in Action
• Business Consulting
• People Analytics and Leadership (Elective)
• Operations and Supply Chain Analytics (Elective)

Internship Project (IP)

This experiential learning pathway will provide students with an opportunity to apply concepts and theories learned during the program in an organizational setting over a period of four months. Students are assigned individually or in teams to a business or a not-for-profit organization to apply their knowledge and skills on real world data and realize analytics projects under the co-supervision of a SMU faculty member and a qualified mentor at the partner organization.

Consulting Project (CP)

This experiential learning pathway will provide students with an opportunity to apply concepts and theories learned during the program while working as an in-house/in-residence consultant over a period of hour months. Students are assigned individually or in teams to a client of the University’s entrepreneurship centre and/or other centres to complete a consultancy project under the cosupervision of a SMU faculty member and a qualified mentor at the partner organization.

Research Project (RP)

This experiential learning pathway will provide students with an opportunity to apply concepts and theories learned during the program and acquire research skills and experience while working as a data analytics researcher over a period of hour months. Students are assigned individually or in teams to a research project under the supervision of a research active SMU faculty member. Interdisciplinary projects are particularly encouraged.

Undergraduate degree from an accredited university with strength in mathematics, computer science, business, economics, engineering, or science. Including at least one mathematics or statistics course that covers hypothesis testing and regression.

At least a B average (3.0 GPA) or equivalent in the final 60 credit hours of the undergraduate degree.

Strong English language proficiency is important for success in the program. Applicants to this program, whose first language is not English, must demonstrate their English language proficiency as outlined in the Graduate Academic Regulation 1e.