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The Master of Management Analytics marries training in core data analytics concepts and tools, with outstanding education in business strategy and management. It includes extensive review of the fundamental mathematical and statistical theories and methods that underlie modern analytics, but with a practitioner focus.

This course will focus on the data management techniques frequently used as a precursor to analysis with ‘real world data’. Topics include database structures, SQL, data cleaning, merging and filtering, detecting and correcting errors. The course will also cover the application of visualization to developing and telling stories with data with data visualization techniques.

This course explores the profound implications of AI on business and society. The ethical and policy issues linked with the application of AI in business are covered in-depth, including such issues as overcoming the job displacement due to AI by job creation, ensuring the public good as AI pervades the new economy, and balancing privacy and transparency in AI related endeavors.

Good managerial decisions depend on an understanding not only of the problem structure, parameters, and constraints, but also of the organizational context in which the decision will be implemented. In this course, we will explore the use of a variety of analytical methods to assist in the mechanics of problem solving and case studies and illustrations to illuminate contextual issues. The general approach we will follow will be to construct an analytical representation of the problem, called a model. This model will be manipulated, or "solved", to identify the decision that yields the "best" outcome. Finally, the model results are applied back to the original managerial problem, or implemented.

In this course, we shall concentrate on the processes of problem recognition, model formulation, and interpretation of the model results and implementation. We will not focus on algorithmic details of specific model solution, but rather will use pre-tested computer routines in most cases. The intention of the course is to help you become a perceptive and critical user of quantitative models in an organization. Decision models can be divided into two main categories: those that assume that the variables within, and outcomes from, a decision problem are known with certainty (called deterministic models), and those which introduce elements of uncertainty or risk (called stochastic models). We will examine models from each of these categories, chosen on the basis of degree of use in current practice. Case studies will help in developing facility with model formulation and interpretation of results, and will aid development of an intuition about effective use of modeling. The phenomenal power for problem analysis provided by modern spreadsheets will be exploited in the course, using EXCEL and EXCEL Add-Ins (@RISK and Precision Tree).

This course provides a comprehensive overview of modern financial analytics. Topics will include traditional models such as: the CAPM, portfolio optimization, applied contingent-claims analysis, Altman’s-Z, Monte-Carlo methods and applied econometric models. In addition, the course will also cover recent advances in artificial neural networks and machine learning tools applied to forecasting financial time-series and corporate default as well as Block-Chain analytics.

This course introduces students to best practices and processes relating to managing projects and launching and supporting projects in organizations (i.e., project leadership). The course approach adopts both leadership and management perspectives so students learn how the organization and project teams need to work together to deliver projects that get results. This course will provide an overview of managing predictive/traditional, scope-bound projects and an overview of managing adaptive/agile, time-bound projects.

The course will still cover big data architectures, the Hadoop ecosystem (especially Spark), NoSQL databases, and a sampling of powerful applications of big data analytics, including recommender systems and social network analytics. New concepts will include additional applications of big data analytics, including text and unstructured analytics, such as sentiment analysis, document clustering, and document classification.

Creating, leading or contributing to a high-performance team are critical skills for managers today. This introductory module helps you build an understanding of the key elements of a high-performance team, and what leads to team effectiveness. During the module, students are led through a set of practical sessions that reveal a five-step process for building high-performance teams. The knowledge gained and the skill set developed are immediately transferable to the work place.

This course introduces students to entrepreneurship and innovation, designed to embed a much greater appreciation for the role of entrepreneurial thinking and know-how in the minds of all students, regardless of current or desired role in business – start-up or corporate innovation. The course will provide a unique opportunity for students to immerse themselves in what it means to be entrepreneurial, and in the entire new venture context. Students will finish the course with the following:

  • Ideation techniques such as Design Thinking
  • The ability to differentiate, using a systematic and thorough approach, between an idea and a true business opportunity, the ability to assess an analytics-based new business venture or corporate innovation
  • The ability to understand what strategies and resources are required and available to translate a viable opportunity into a real business
  • The ability to 'pitch' a business opportunity in order to gain whatever resources are necessary to execute on the opportunity presented
  • An appreciation for various types of analytics based new ventures and innovations

The course will establish a foundation of statistical modelling techniques to be immediately useful for analysis and to provide a foundation for more advanced material studied throughout the program. Topics will include data types, random numbers, probability models, hypothesis testing and statistical inference, and a thorough grounding in simple and multiple regression. The course is designed to ensure that all students, regardless of background or experience, are proficient in the use and application of a variety of statistical methods.

This course introduces the main functional areas of business, including strategy, marketing, operations and finance, and demonstrates how these areas interact to produce and market products and/or services effectively and efficiently. It provides an overview of the modern corporate enterprise in Canadian and international contexts, and of the tasks, practices, and responsibilities of its managers, and is designed to ensure all students, regardless of background or experience, are equipped to understand analytics in the context of business and management.

This course covers the role strategy development and change management play in successfully capitalizing on the promise of Analytics. Delivered towards the end of the program, the course provides students with the opportunity to synthesize their learnings and understand what to change and how to do it.

The course integrates 2 complementary aspects of driving organizational success through analytics – what to do (the strategy piece) and how to make it happen (the change management piece).

The course covers the entire spectrum of enterprise strategic and cultural transformation, including functional level changes in strategy (e.g. marketing, finance) through to enterprise/corporate level changes to strategy and culture. The course also touches on strategy and change as they relate to intra- and entrepreneurial endeavors.

Specific topics covered include:

  • How to set strategy via an agile strategic planning process
  • Creating and implementing a strategic plan (for an existing enterprise or function)
  • Creating a Business Model Canvas (for an entrepreneurial venture)
  • Implementation essentials (link to project leadership course)
  • Organizing for innovation (intrapreneurship)
  • Defining and creating a culture of analytics
  • Leading and managing cultural change (organizational and individual level change management)

This new course will introduce machine learning (ML) concepts, with a heavy focus on business applications. The course will look in-depth at all three types of ML: supervised (including classification and deep learning), unsupervised (including association rule learning and dimensionality reduction), and reinforcement learning. The course will survey key technologies and applications that are driving the ML revolution. The course will include some theoretical background, but will be application-focused. The overall goal of the course is to provide a foundation and framework for understanding how to use machine learning models in data-driven decision making.

On completion of this course, students will be able to frame various classes of business problems as ML problems. Students will understand which ML model to use for a given problem, how to use the model, how to evaluate the model, and how to deploy the model.

The course will include a group project that will provide an opportunity to apply various ML models to a real-world business dataset.

Advances in data and technological infrastructure have enabled the tracking and collection of detailed information, structured or otherwise, on customer behaviours, preferences, and attitudes towards the brands that they buy from. Organizations today expect their marketing executives to be able to understand and apply analytical frameworks to this myriad of information and data to generate insights that drive sound business decisions. The objective of this course is to show you how to apply an analytical approach to marketing decision making in this era of "big data."

Through a combination of theory-based and hands-on learning, participants of this course will be able to learn the key set of marketing analytical techniques, including but not limited to segmentation and targeting, new product design, customer value management, and marketing mix modeling. The theoretical content from the text book will be augmented with real life applications and practitioner reflections from this field.

"Operations" refers to how an organization delivers its customer value proposition, its "business model". In all cases, whether it is a commercial firm that designs, manufactures, distributes, or provides products or services, a non-profit firm that manages volunteers, a healthcare facility that treats patents, or a government body that serves residents -- operational excellence is key to an organization's success and often survival. Further, as the complexities of the modern world often require working beyond the boundaries of the organization, this course also explores the operations of inter-connected agents as they work together in a value-chain.

This course considers the main analytical techniques underlying the efficient and effective management of both strategic and tactical operational decisions within a single firm and throughout a supply chain. The course intermixes lectures, cases and interactive experiences not only to obtain familiarity with these techniques, but also to expose students to applying them in various industrial and organizational settings.

The course will combine three key elements: analytics techniques, business applications, and basic coding/programming (in R, one of the leading open-source tools for analyzing data that you will be able to use in your jobs.) The emphasis will be not on the technicalities or theory, but rather on applications to various business cases. Basic familiarity with R is required, but for most classes you will receive a starter code, by running and modifying which you will learn analytics techniques and coding principles, and which you will also be able to use in your jobs. Because of that, much of the course will be in a form of a "hands-on" workshop; students are be expected to bring your laptop to class (with all the necessary software tools installed) and actively participate in the learning process. The course will cover 2 major topics within the domain of predictive analytics: “predicting quantities” and “predicting events”. Within the “quantities” part we will focus on linear models, variable selection and regularizations, as well as on time-series analyses. Within the “events” part we will focus on generalized linear models (logistic regression) and get an introduction to supervised machine learning (CART, random forest, boosting, and neural networks).

Pricing and Revenue Optimization (PRO) focuses on how a firm should set and update pricing and product availability decisions across its various selling channels in order to maximize its profitability. Through a combination of case studies, lectures and guest speakers, this course reviews the main methodologies of analytical pricing and surveys current practices in different industries. Within the broader area of pricing theory, the course places particular emphasis on tactical optimization of pricing, capacity allocation decisions, demand forecasts, market uncertainty, and the tools of constrained optimization.

Technical Training

Students will be exposed to a variety of tools and programming languages including Python, SQL, R, Tableau, SAS, Hadoop, and Spark.

Meet the Experts

Courses are taught by experts and range from importing data and data visualization to machine learning, deep learning & more.

View Faculty & Instructors

MBA Option

You can apply Master of Management Analytics credits to the MBA program at Smith School of Business, Queen’s University to fast-track to an MBA at a reduced fee.

Professional Designations

You may be eligible to apply some of the MMA work against the following designations. Contact our admissions team to learn more.

  • Certified Analytics Professional
  • Project Management designation

Experiential Learning

Experiential learning or "learning by doing" is one of the most effective ways to learn. Students in the Master of Management Analytics program have the opportunity to participate in the Queen's University Alternative Asset Fund (QUAAF.) It is the only student run hedge fund in North America and was the brainchild of four Queen's Master's students. The student management group consists of a mix of MBA, Master of Finance, and Master of Management Analytics students that comprise an Executive Committee and teams of Analysts. They are supported by an Advisory Committee of industry professionals. This fund has been seeded with contributions from alumni and friends of Queen's, and all proceeds contribute to the maintenance and expansion of the fund.