MMA Program Overview
Required Coursework
Students must complete 11 core courses along with two elective courses. This includes a capstone course, which provides the opportunity to apply academic learning to real-world challenges.
Full-time Course Schedule
The Full-Time MMA program begins in mid-August with a one-day program orientation, followed by a two-week mandatory bootcamp designed to help students build a strong foundation for the program. Following the bootcamp, students will participate in an intensive five-day course.
Each year, two Full-Time MMA cohorts are admitted, and students will be assigned to one of the two cohorts. Beginning in September, courses transition to the regular university academic schedule. While a detailed timetable will be released closer to the start of the program, one cohort will primarily attend classes on Mondays and Wednesdays, while the other will primarily attend classes on Tuesdays and Thursdays. Students can also expect Fridays to be dedicated to experiential learning activities, career treks, and guest lectures.
In addition to scheduled class time, students should expect to dedicate time outside of class to readings, assignments, casework, and group projects throughout the program.
Part-Time Course Schedule
The Part-Time MMA pathway offers greater flexibility for students who wish to complete the same degree over a longer period while balancing other commitments. Students attend daytime classes alongside the Full-Time MMA cohorts, taking a reduced course load and typically completing the program in two to three years.
How the Part-Time Pathway Works
- Flexible class scheduling: The two cohorts primarily attend classes on Mondays and Wednesdays or Tuesdays and Thursdays. Part-Time students can select courses across both cohorts, providing flexibility in planning their schedules.
- Reduced course load: Students typically take two courses per term in Fall and Winter, with electives or the capstone scheduled in Spring/Summer, depending on course availability and their individual plan of study.
- Same curriculum and degree: Part-Time students complete the same curriculum, capstone requirements and degree as Full-Time students.
- Same total tuition: Total program instructional fees are the same as for the Full-Time MMA, but paid over a longer period. Non-instructional fees apply for each term in which students are registered.
- Access to the full MMA experience: Part-Time students have access to the same advising, career programming, employer engagement and student experience as other MMA students.
Important scheduling consideration: Classes are held during the day. Students who are working while completing the program may need to arrange time away from work with their employer to attend classes and other required activities.
In addition to scheduled class time, Part-Time students should plan for readings, assignments, casework and group projects outside of class.
Internship Option
Students can complete the Master of Management Analytics program in 12 months, or extend their studies to 16 months by pursuing a four-month internship. The internship provides an opportunity to apply the knowledge and analytical skills developed throughout the program in a professional setting.
The internship option is competitive, and placement is not guaranteed. Students must apply and successfully secure an internship to participate in the internship portion of the program. Students who do not secure a placement can complete the program through the 12-month stream instead.
Students who proceed with the internship will be assessed an additional $4,500 internship fee and applicable non-instructional fees in the Fall term following the start of the program, when the internship portion begins.
Core Courses
MMA 600 - Coding Bootcamp
Two-Week Kick Start Bootcamp: Embark on a seamless learning journey as students engage in a well-rounded experience to master two essential programming languages - Python and R. Restricted to students registered in the MMA Program.
MMA 601 - Business Foundations and Strategic Decision Making
Students are introduced to business fundamentals in the first session followed by second session that delves deep into the dynamic world of data-driven strategy, cultivating invaluable skills in utilizing data to frame decisions effectively.
MMA 602 - Machine Learning for Business I
The goal of the Machine Learning for Business course is to utilize machine learning techniques to transform raw data into valuable insights that can inform business strategies. This course demands a solid grasp of technical data handling methods as well as business goals. It involves an overview of various machine learning approaches, such as supervised and unsupervised learning, and their practical uses in business scenarios.
MMA 603 - Data Visualization and Business Communications
This course equips students with the ability to turn raw data into meaningful visualizations and communicate these insights in a business context. It covers the essentials of effective data visualization, visual design principles, and storytelling with data. Through hands-on practice with tools like Tableau and Excel, students will learn to create and interpret various visualizations, focusing on selecting the most appropriate visual forms to accurately reflect data and address business queries.
MMA 604 - Database Fundamentals for Business Analysts
Provides students with an understanding of the critical role of databases in business analytics, focusing on the principles of database systems, design, implementation, and utilization in a business context. students are introduced to fundamental concepts of data and information management.
MMA 605 - Statistics Analytics and Causal Inference
This course provides students with a robust foundation in statistical principles and techniques, alongside essential skills in descriptive analytics and causal inference. Students will develop strong analytical skills and gain hands-on experience with statistical software. Further delving into time series analysis, multivariate analysis and enhanced predictive modeling. Students will also gain proficiency in experimental design including ANOVA and A/B testing.
MMA 606 -Machine Learning for Business II
Builds upon the foundational knowledge students acquired in Machine Learning for Business I, diving deeper into the specialized applications of machine learning techniques to unstructured data. By exploring areas such as text analytics, network analytics, recommender systems, and deep learning applications, students will gain a robust understanding of how to handle and analyze unstructured data such as text and images, which constitute a significant proportion of the data businesses encounter
MMA 607 - Prescriptive Analytics
This course is designed to provide a foundation of prescriptive analytics based on mathematical modeling and optimization for managerial decision-making. Topics covered in the course include decision analysis; simulation modeling; constraint programming and constraint-based optimization; network optimization and graph algorithms; optimization under uncertainty; application of prescriptive analytics techniques in various industries; integration of predictive and prescriptive analytics; and practical implementation of prescriptive analytics techniques to solve real-world problems. By the end of the course, students will have a solid understanding of prescriptive analytics techniques and their practical applications.
MMA 608 - Business Applications of Artificial Intelligence
This comprehensive course, co-taught by a panel of expert instructors, aims to provide students with an in-depth understanding of how artificial intelligence (AI) technologies are applied in real-world business settings. It introduces students to a range of AI applications across different industries and functional areas, highlighting the transformative potential of AI in driving innovation, improving operational efficiency, and creating competitive advantages.
MMA 609 - Responsible AI & Ethical Issues in Data Analytics
This course focuses on the ethical and legal considerations in artificial intelligence (AI) and data analytics, fields that are evolving rapidly and prompting novel ethical and regulatory concerns. It will cover subjects such as data privacy, fairness in algorithms, interpretability, and accountability. Participants will be educated on the responsible and ethical application of AI and data analytics technologies.
MMA 610 - Analytics Capstone Project
This course represents the apex of the MMA program, extending over two semesters, and offers students an immersive, real-world experience in analytics. The Analytics Capstone Project serves as a significant demonstration of the students' analytical skills and their capacity to make data-informed decisions in intricate business environments.
Elective Courses
MMA 611 - Accounting Analytics
This course combines advanced data analytics and technology, essential for modern accounting, as part of the Master of Management Analytics program. It is structured into two main parts: Data Analytics and Technology Integration in Accounting, with a strong emphasis on practical learning. Students will use data analytics tools like OLS, logistic and probit regressions, and optimization analysis to address various accounting challenges. The curriculum covers financial and managerial accounting, auditing, and taxation, focusing on problem-solving and decision-making. This prepares students for roles in audit risk assessment, audit procedures, and strategic tax planning and compliance.
This course integrates financial skills with data science for enterprise decision-making, structured into four key sections. It covers core financial modeling skills, including interest rate discounting and uncertainty modeling, and explores Real Optionality to understand how management decisions and uncertainties affect valuation, focusing on NPV@Risk. The section on Decision Quality (DQ) delves into its relevance in business, biases, risk definition differences in finance and enterprise, and practical implementation strategies. Lastly, the course emphasizes creating interactive Data Science applications, teaching students to develop apps for engaging senior management, with all content exclusively using R programming.
MMA 613 - Operations and Supply Chain Analytics
This course prepares students to tackle complex business logistics challenges using advanced analytics techniques such as regression, optimization, and simulation. It focuses on key areas like inventory management, site selection, revenue optimization, and transportation logistics, emphasizing data-driven approaches for cost minimization, operational efficiency, and market responsiveness. Students will apply real-world data to enhance supply chain operations, including developing effective pricing strategies and optimizing delivery routes. The course offers hands-on experience with extensive supply chain datasets, equipping students with the skills to turn data into actionable insights for innovative and efficient supply chain management.
This marketing course equips students with tools to generate actionable insights by understanding consumers and market trends. It focuses on designing analytical plans to tackle marketing problems, covering aspects from data collection to communicating findings. Key skills include measuring variables, choosing appropriate analytical methods, interpreting data analysis techniques, and effective storytelling. The course prepares students for roles in marketing analytics across various sectors and emphasizes a hands-on approach, with project design and data analysis in class. Upon completion, students will be proficient in areas like marketing research, experimental design (e.g., A/B testing), data collection, regression analysis, segmentation, machine learning applications in marketing, and results communication.
MMA 615 - Healthcare Analytics
This course is a gateway to healthcare analytics, teaching students how data reshapes healthcare strategy and improves patient care quality. It covers extracting and processing data from various sources like electronic health records and wearable devices, and advanced analytics techniques such as predictive modeling and machine learning for patient outcomes and diagnostics. Students will understand the ethical and legal aspects of handling sensitive patient data and learn to optimize healthcare operations like patient flow and resource allocation. The course emphasizes data-driven decision-making, with practical applications through case studies, projects, and guest lectures from industry experts, fostering interdisciplinary expertise to tackle healthcare challenges.
This course is designed to prepare future managers to effectively integrate data science and business analytics into strategic decision-making. It provides an overview of how these functions can harmonize to create effective strategies while highlighting the pitfalls of poor integration. Students will learn about strategic vision, data-driven decision frameworks, competitive intelligence, risk assessment, and the use of performance metrics for continuous improvement. The course includes real-world case studies to apply theory to practice and emphasizes ethical considerations in data strategy, focusing on responsible data use, transparency, and privacy. By the end, students will understand the interplay between data science and business analytics and be able to develop strategies aligned with organizational goals.