Creating High Performance Teams

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.

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Predictive Modelling

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).

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Machine Learning & Artificial Intelligence

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 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.

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Introduction to Management

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 AI in the context of business and management.

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Introduction to Analytic Modeling

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.

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Acquisition and Management of Data

This course will focus on the data management techniques frequently used as a precursor to analysis with ‘real world data’. Topics will include including 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.

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Marketing Analytics

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 my reflections as a practitioner of this field. The end goal is to help you develop a good, practical foundation to marketing analytics.

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Analytical Decision Making

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).

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Big Data Analytics

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.

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Operations & Supply Chain Analytics

"Operations" refers to how an organization delivers its customer value proposition, its "business model". Whether it is a commercial firm that designs, manufactures and distributes goods or provides services, a hospital that treats patients, a government body that serves residents, or a non-profit firm that manages volunteers – in all cases, operational excellence is key to any organization's success and often survival.

The course will therefore consider the main analytical techniques underlying the efficient and effective operations management. Furthermore, since the complexities of the modern world often require working beyond the boundaries of any spec organization, the course will also consider operations of chains of inter-connected agents that all work together to deliver customer value – supply chains. We will focus on both strategic and tactical operational decisions within a single firm and throughout supply chains, and learn how these techniques apply to various industrial and organizational settings.

The course will intermix lectures, cases and interactive experiences so that the students not only obtain the familiarity with the approaches we study, but also experience their applications to practical situations. The ultimate goal is to get students' "toes in the water" with multiple techniques and applications, so that they can get a comprehensive overview of operations and supply chain analytics, and have a basis to zoom into ones most pertinent to their current jobs and future careers.

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Project Management

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.

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Advanced Analytics Using SAS Enterprise Miner

The best way to achieve a working knowledge of commercial-grade business analytics systems is to gain familiarity with at least one such system. And the SAS system is a major provider of business analytics software. This course will help attendees gain a complete understanding of how SAS software works and is used and will facilitate learning any other systems that they may encounter in their work lives. The course will prepare you for the SAS Certification exam.

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High Performance Teams

This class builds on the work that was done during the first residential session.

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Analytical Decision Making

In this course, we explore the use of analytical methods in management problem-solving, highlighting organizational and contextual issues. We study how to construct an analytical model of a problem that can be manipulated or solved to identify a decision that yields the best outcome, according to one or more carefully defined criteria. The challenges of communicating and implementing results in an organizational context will also be explored through mini-cases and illustrations.

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Multivariate Statistical Analysis

In this course we study multivariate statistical methods and how they are used in management analytics. After a review of multivariate distributions, sample geometry, and general linear models, we focus on important approaches to multivariate statistical analysis such as: categorical data analysis; multi-equation regression; principal components; classification methods; cluster analysis; and multidimensional scaling.

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Analytics for Financial Markets

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.

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Pricing Analytics

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.

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Management of Analytics

This is a capstone course that ties together the key concepts of the program and links them to the strategic objectives of organizations. Topics include: Supply Chain Management; Service Management; Innovation; Managing Change; Ethics and Analytics. We will also discuss senior management strategies for developing and using analytical expertise in organizations. An integrative case exercise is a key part of this module.

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MasterCard Innovation Challenge

Students will be presented with transactional data. Teams will then use this information, along with their own ideas and creativity, to create a potential product that could be developed based on this data.

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Data Privacy Workshop

An expert in data privacy will discuss Canadian privacy obligations and compare these to American, Asia-Pacific, and EU practices. Students will learn about database governance as well as international consumer privacy and data protection policies.

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Entrepreneurship & Innovation

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
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Text Analytics

80% of all corporate data is captured in an unstructured textual form. A billion Tweets are sent every two days. Americans spend 54 billion minutes per month reading and writing Facebook status updates. In this course, we will explore the use of text analytics to organize, understand, and mine textual data sources. We will investigate tools and techniques for preprocessing (e.g., removing noise from) unstructured textual data sources; tokenizing text streams; named entity recognition and part-of-speech tagging; sentiment analysis; text classification; and information retrieval. We will focus our discussion on the practical application of these techniques, especially in the case of social media monitoring.

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Analytics Strategy & Change

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)
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Curriculum

The program curriculum has been developed to provide the team skills, business acumen, analytic capabilities, and communication skills you will need to be successful. It includes extensive review of the fundamental mathematical and statistical theories and methods that underlie modern analytics…but with a practitioner focus.

Students will be exposed to a variety of tools & programming languages throughout the MMA program including SAS, R, Python, Tableau, Hadoop, and Spark. Students are encouraged to participate in optional workshops in which they will be introduced to these tools. These sessions will take place outside of regularly scheduled classes. Additional self-study will be required in order for students to master these technical skills.

Residential Session 1
(Kingston one week)

Module 1 (Toronto)
Module 2 (Toronto)
Residential Session 2: Electives
(Kingston one week)

Select one of:

Module 3 (Toronto)
Module 4 (Toronto)
Module 5 (Toronto)
DataCamp Logo

As part of the program, students will have access to DataCamp for the first 6 months. DataCamp is an interactive learning platform for data science. Learn R & Python from the comfort of your browser with over 75+ courses featuring high-quality video, in-browser coding and gamification. Courses are taught by experts and range from importing data, data visualization, machine learning, deep learning & more.

Professional Designations

During the program, you will receive training that can be applied for a Certified Analytics Professional (CAP) with the Institute for Operations Research and the Management Sciences. Learn more

The program also provides twenty-five instructional hours which can be applied to the certification requirements for either a Certified Associate in Project Management (CAPM), or (with additional training) a Project Management Professional (PMP) with the Project Management Institute (PMI).

As part of the program, you will also take the SAS Certified Predictive Modeler (using SAS Enterprise Miner).

SAS logo
Jerry Oglesby

We are pleased to be working with the Smith School of Business and their Master of Management Analytics program. I believe that graduates of this program will be in great demand 

Jerry Oglesby
Senior Director, Global Academic Program & Global Certification
SAS