Program structure

The Masters of Management in Analytics (MMA) is an intensive full time, one-year, pre-experience program with a strong emphasis on experiential learning.

MMA Program Structure

The MMA consists of three modules:

  1. Core (21 credits)
  2. Complementary courses (15 credits)
  3. Experiential (9 credits)



ORGB 660 Managing Data Analytics Teams (1.5 credits)

Managing Data Analytics Teams challenges students to understand work and teamwork in new ways to unlock the potential of collaboration. The ability to find innovative solutions to difficult problems and to promote effective business functioning requires the knowledge and skills to cultivate effective teams. Ironically, the need for self-knowledge and well-developed individual skills is heightened, not lessened, by this reality.

In this course, students learn to excel in teamwork. The course provides hands-on opportunities for honing skills in collaborating effectively with others in team-oriented environments. It is designed to allow students to improve the effectiveness of their teams as they prepare to be both excellent individual contributors (technical experts) and collaborators with others, whether in teams, work units, or in whole organizations. The course runs as an executive development seminar using experiential exercises and simulations, including opportunities for students to lead sessions, to engage in hands-on team collaboration activities, to discuss team experiences with executives, and to conduct real time team projects.

ORGB 660 - Managing Data Analytics Teams

INSY 660 Coding Foundations for Analytics (3 credits)

In this core class, students will be exposed to a broad set of topics, including fundamentals of computer programming, coding for data acquisition and data manipulation, as well as specific operational issues related to big data analytics.

INSY 660 - Coding Fndtns for Analytics

MGSC 660 Mathematical and Statistical Foundations for Analytics (3 credits)

This is a foundational course for the students in the MMA program. It covers essential mathematical and statistical tools that will be used throughout the program. This course consists of two parts: (i) the first half of the course focuses on probabilistic and statistical foundations of data analytics. At the end of this part, students will have the mathematical knowledge in the following topics: probabilities, random variables, the Central Limit Theorem, prior and posterior distributions, Bayes’ rule, correlation, and sampling; (ii) the second half of the course focuses on mathematical foundations of decision analytics. At the end of this part, students will have the mathematical knowledge on the following topics: linear algebra, calculus of several variables, convexity, separating hyperplanes, unconstrained and constrained optimization, Lagrange multipliers.

MGSC 660 - Math&Stat Fndtns for Analytics

INSY 661 Database and Distributed Systems for Analytics (3 credits)

This course will provide a basis for data and database management in today’s analytics-driven enterprise. Effectively storing and managing data is a critical driver of success for enterprises wishing to engage in analytics to solve business problems.

In particular, this course will largely focus on the relational database model using one of the following databases: MS Access, MySQL, or Oracle. It will present many key concepts relating to database technology, how database technology is being used for managing large datasets, and the opportunity to put these concepts to practice. This course will cover database management system (DBMS) concepts, database architecture, database design using entity-relationship (ER) modeling, data storage, file organization, the SQL language, normalization, data integrity, database security, data warehousing, and big data related technologies such as NoSQL, Hadoop, MapReduce, Pic, and Hive.

INSY 661 - Data & Dist Sys for Analytics

MGSC 661 Multivariate Statistical Analysis (3 credits)                                 

This course covers key issues in multivariate data analysis, with an emphasis on hands-on experience in data analytics. The course aims to introduce various statistics methods for analyzing multivariate data and develop core techniques to identify casual relations in the data. The course opens with the standard linear regressions, and extends to multivariable regression models, factor analysis, principal components, selection models, and dynamic and nonlinear multivariate data methods. Students will be exposed to a broad range of techniques and applications in Business Analytics through conducting their own statistical analyses.

MGSC 661 - Multivariate Statcl Analysis

INSY 662 Data Mining and Visualization (3 credits)

To prepare students for solving business problems by using data mining and emerging job opportunities in data analytics, this course provides a comprehensive introduction to data mining and predictive analytics problems and tools to enhance managerial decision-making at all levels of the organization and across business units.

INSY 662 - Data Mining and Visualization

MGSC 662 Decision Analytics (3 credits)

The ability to make “intelligent” decisions is critical for both managers and firms. In today’s business world, problems are too complex to rely simply on intuition and common sense. Quantitative decision tools allow decision makers to base decisions on data-driven and scientific methods. This course prepares students to use management science tools to make effective business decisions in operations, finance, marketing, management, technology, and new product development.

MGSC 662 - Decision Analytics

ORGB 661 Ethical Leadership and Leading Change (1.5 credits)

Ethical Leadership and Leading Change challenges students to understand and practice leadership in new ways. Leading change requires recognizing, anticipating, and overcoming resistance from others. Those who develop their abilities for leading change will see the implementation of their ideas and agenda items. Ironically, the need for self-knowledge and well-developed individual skills is heightened, not lessened, by this reality. In the context of analytics, data privacy and ethics are a crucial consideration for the planning and implementation of any project.

In this course, students learn to excel in leadership and leading change. The course provides hands-on opportunities for honing skills in leading others in team-oriented environments. It is designed to allow students to accelerate their capacity as leaders as they prepare to influence decisions, strategies, and directions within their organizations.

ORGB 661 - Ethical Ldrshp& Leading Change

Complementary courses

15 credits from the following:

ORGB 671    Talent Analytics (1.5 credits)

The book and movie Moneyball popularized the idea that careful data analysis could contribute to the efficient creation of incredibly effective teams and organizations. It’s true, and this elective teaches how to apply many of these techniques within your organization. We will cover analytical and predictive methods from statistics, ecology, and demography, among others.

ORGB 671 - Talent Analytics

ORGB 672    Organizational Network Analytics (1.5 credits)

In life and in organizations, relationships matter. Organizational network analysis provides tools to measure and analyze relationships among entities including individuals, units, and entire firms. Outcomes critical for Talent Management, such as hiring, promotion, and turnover, are critically and intricately tied with individual’s social networks. A firm’s ability to survive, adapt, and innovate depends on its location within its network of firms. This course provides an overview of basic social network analysis (SNA) methods that are specifically relevant for understanding and managing organizations.

ORGB 672 - Org Network Analysis

MRKT 674     Retail Analytics (1.5 credits)

This course covers both strategic and operational issues involved in retail management. The objectives of this course are:

  1. to learn how to leverage retail data to strategic advantage;
  2. to examine basic tools and techniques to analyze retail data to increase sales and reap a competitive advantage.

Upon successful completion of the course, students will be able to:

  1. Identify data science solutions to common retail problems;
  2. Assess whether a retail outlet fits with the requirement of the marketplace;
  3. Improve store operations by putting the right product in the right place at the right time.

MRKT 674 - Retail Analytics

MRKT 673     Pricing Analytics (1.5 credits)

With advances in information technology and data availability, a firm’s pricing strategy becomes increasingly dynamic and complex. This course focuses on how a firm sets and adjusts pricing decisions, as well as coordinates with supply constraints to maximize its profitability. The objectives of this course include:

  1. to familiarize students with the concepts and applications of quantitative models of demand, consumer behavior, and price responses;
  2. to examine basic pricing tactics that are widely adopted in practices;
  3. to apply knowledge to solve real business problems through cases analyses and marketing applications.

MRKT 673 - Pricing Analytics

MRKT 671     Advanced Marketing Analytics  (1.5 credits)

Marketing is witnessing a data revolution. Increasing amounts of data on customers and competition is becoming available and accessible to companies. This, combined with improvements in computing technology, are enabling marketing managers to make data-driven decisions. Data-driven insights allow marketing managers to more effectively understand their customers and meet their needs. They also allow managers to design optimal marketing mix strategies that effectively counteract competitive moves. Together, these help firms to allocate their marketing resources optimally and get the maximum returns on their investments. Data-driven insights require the use of sophisticated and advanced analytical tools. Proficiency in these tools needs training and practice, and is a skill that is scarce and in demand in the current marketplace. The primary goal of this course is to help students become in the use of cutting-edge marketing data-driven tools that can be valuable for future careers in marketing.

The course will introduce students to the advanced analytic techniques that are available to marketing managers today and give them hands-on experience in using these in cases with actual data sets.

MRKT 671 - Advanced Marketing Analytics

MRKT 672     Internet Marketing Analytics (1.5 credits)

The development of web and social media analytics has allowed firms to market towards consumers and analyze their behavior in ways that were not possible a mere decade ago. Social media platforms, such as Facebook and Twitter, foster connections between people that allow them to share knowledge and experiences like never before. A priority for many managers is determining how these platforms can be leveraged to gain a competitive advantage. Many organizations (both profit and non-profit) now employ marketing experts to monitor their internet presence as well as to actively engage with others via online platforms. The course has two core objectives. First, students will gain industry background knowledge so that they can handle relevant issues related to search engine optimization and online advertising analytics/strategy. Second, students will learn to evaluate the effectiveness of online marketing efforts and to develop new online marketing strategies, from both a quantitative and qualitative perspective.

MRKT 672 - Internet Marketing Analytics

INSY 670      Analytics for Digital Business Models (1.5 credits)

This course is designed for Masters of Management in Analytics students to enhance their knowledge of analytics as used in the context of IT-enabled business models. The main goal of this course is to examine how analytics can be leveraged for making better decisions and gaining a competitive advantage within a set of industries or domains where the innovative use of information technology by both organizations and consumers has had a significant impact.

INSY 670 - Analytics for Dig Bus Models

INSY 671      Analytics and Open Innovation (1.5 credits)

This course provides a comprehensive introduction to the use of data analytics in the context of open innovation. Students will learn new tools and techniques that can be applied to answer real-world questions about open innovation.

The course will assume some familiarity with programming and software tools used for data analysis. Experience acquired through earlier courses in this program will provide an adequate base for using the analytics tools covered in this class. The focus of the course is on the application of data analytics in the context of open innovation and will require students to analyze large data sets.

INSY 671 - Analytics and Open Innovation

INSY 672      Health Analytics (1.5 credits)

This course is an elective course in the Masters of Management in Analytics program that will allow students to explore the use of analytics in the context of health care.

Students will get hands-on experience with how data analytics can be used to predict and understand disease outbreaks, how it can be used to improve the operation of hospitals, and the manner in which analytics can be used as decision support for physicians to diagnose and treat patients.

By the end of the course, students should be able to develop an appreciation for the changes that are taking place in the provision of healthcare services due to analytics, as well as the role and opportunities for analytics to reduce cost and improve the quality of healthcare in their communities.

INSY 672 - Healthcare Analytics

INSY 673      Security Analytics (1.5 credits)

This elective course provides an opportunity for students to understand the role of analytics in information security. Protecting information assets has become an essential tasks of government entities and companies in recent years. This course provides a comprehensive introduction to data analytics in the context of information security. Students will be exposed to real-world datasets, tools, and techniques that can be applied to analyze such data.

The course requires a prerequisite knowledge of R , which will be primarily used throughout the course to provide hands-on experience in the analytics techniques covered in the class. The focus of the course is on the application of the data analytics in the context of information security, rather than the theories and mathematics behind the methods.

INSY 673 - Security Analytics

MGSC 670    Revenue Management (1.5 credits)

Management is about matching demand and supply. This course focuses on the demand without attempting to manage the supply. But it does take the amount, location, condition, or vintage  of the supplies into account. Hence, it focuses on determining optimal prices depending on inventory, capacity, input costs, and previous prices. In this process, both analytical arguments and methods are presented and their appropriateness in various practical contexts is discussed.

MGSC 670 - Revenue Management

MGSC 672    Operations and Supply Chain Analytics (1.5 credits)

The course covers analytical models that explore the key issues associated with the design and management of supply chains. A considerable portion of the course is devoted to data-driven decision models that treat uncertainty explicitly. Topics include supply network design, inventory centralization, value of information, and contracts.

MGSC 672 - Ops and Supply Chain Analytics

MGPO 695   Topics in Strategy Analytics (1.5 credits)

Strategy: Current emerging topics in strategy analytics. Course content will vary each term.

MGPO 695 - Adv Topics in Strategy Analyt

ORGB 695    Special Topics in Organizational Behaviour (1.5 credits)

Organizational Behaviour: Current emerging topics in organizational behaviour analytics. Course content will vary each term.

ORGB 695 - Adv Topics in Org Behav

INSY 695      Topics in Information Systems (1.5 credits)

Information Systems: Current emerging topics in information systems analytics. Course content will vary each term.

INSY 695 - Adv Topics in Information Syst

MGSC 695    Topics in Management Science (1.5 credits)

Management Science: Current emerging topics in management science analytics. Course content will vary each term.

MGSC 695 - Adv Topics in Mgmt Science

MRKT 696    Topics in Marketing Analytics (1.5 credits)

Marketing: Current emerging topics in marketing analytics. Course content will vary each term.

MRKT 696 - Adv Topics in Mrktg Analytics

ACCT 696     Topics in Accounting Analytics (1.5 credits)

Accounting: Current emerging topics in accounting analytics. Course content will vary each term.

ACCT 696 - Adv Topics in Acct Analytics

FINE 695   Topics in Finance Analytics 1 (1.5 credits)

Finance: Current emerging topics in finance analytics. Course content will vary each term.

FINE 695 - Adv Topics in Fin Analytics 1

FINE 696    Topics in Finance Analytics 2 (1.5 credits)

Finance: Current emerging topics in finance analytics. Course content will vary each term.

FINE 696 - Adv Topics in Fin Analytics 2

Complementary courses are subject to revision and are not offered every year.


As core to the program, the EXP Analytics Consulting module has McGill MMA students working alongside Industry professionals over a 10-month period solving a significant Data & Analytics problem, aimed to boost the client’s top or bottom lines. All students undertake a technical consulting role by working in teams with real companies and attempting to solve a live data-driven problem. Learn more >

BUSA 684       Analytics Study Trip   (3 credits)

This course aims to expose students to state-of-art organisational practices in the ever-evolving field of analytics through the experience of visiting a location with a high density of analytics-related organizations. Students will be required to study such practices in depth, complete a project related to organizational practice in analytics, and write a reflection paper. The course will be delivered through a combination of company visits, guest lectures of top-level executives, as well as daily student reflections.

The cost of the study trip is absorbed in the tuition fees.

BUSA 684 - Analytics Study Trip

BUSA 693       Management Analytics Capstone (6 credits)

The capstone project is based on real-life projects that require the use of descriptive and predictive analytics methodologies and skills. The course instructors engage with private or public sector partners to plan the real-life projects, each requiring the students to handle big data prior to the beginning of the semester. The objective of this course is to integrate the formal knowledge acquired in various courses with the demands of a complex real-world problem.

BUSA 693 - Management Analytics Capstone