Curriculum

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 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.

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).

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 career. 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).

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.

This course equips professionals to lead generative AI initiatives by combining strategic insight with a practical understanding of core technologies such as large language models and multimodal systems. Through expert-led instruction, case studies, and hands-on workshops, participants gain skills in prompt engineering, output evaluation, and agentic AI design while addressing critical ethical, legal, and societal considerations.

Emphasizing real-world application, the program challenges learners to identify high-value use cases, collaborate effectively with technical teams, and craft responsible implementation roadmaps. Designed for cross-functional leaders, it empowers participants to champion innovation, drive organizational change, and foster an AI-ready culture that unlocks the transformative potential of GenAI.

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.

This course equips early-career students with the foundational skills and insights needed to lead and collaborate effectively in academic, professional, and organizational settings. Through interactive projects and real-world case studies, students explore group dynamics, communication, motivation, and ethical leadership while developing practical strategies for teamwork, conflict resolution, and personal growth.

This interactive course builds foundational skills in oral and written communication, emphasizing message design, delivery, and persuasive impact. Through hands-on exercises, peer feedback, and real-time practice, students learn to craft clearly, compelling presentations and develop personalized strategies for continuous improvement. Ideal for aspiring professionals, the course equips participants to communicate confidently and effectively across diverse audiences and career contexts.

This course equips students with the professional, technical, and reflective skills needed to thrive in experiential learning environments such as internships and analytics projects. Through interactive workshops and hands-on practice, students build workplace readiness, coding competencies, and collaborative habits aligned with modern analytics roles. The course bridges academic learning with real-world expectations, preparing students to contribute confidently and ethically in data-driven, software-enabled settings.

This internship course offers students a structured, real-world opportunity to apply analytics in professional settings, working alongside industry partners to solve data-driven challenges and inform strategic decisions. Through hands-on experience, students strengthen key competencies in communication, problem-solving, and ethical leadership, preparing them to contribute meaningfully to analytics-driven organizations and reflect on their own professional growth.

* Prospective students are advised that the internship & project are still subject to formal approval by Queen’s University Senate.

This capstone project course empowers students to lead a comprehensive analytics project that tackles a complex organizational challenge. Working independently or in teams, students apply advanced methods to generate strategic insights and deliver professional-grade recommendations. Emphasizing critical inquiry, project management, and evidence-based decision-making, the course prepares students to communicate findings effectively and contribute meaningfully to careers in consulting, research, and strategic planning.

* Prospective students are advised that the internship & project are still subject to formal approval by Queen’s University Senate.

"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.

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.

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.

This hands-on elective equips students to tackle real-world healthcare challenges—like emergency crowding, care variation, and equity gaps—by transforming operational problems into analytics questions with measurable outcomes. Through case-based learning and anonymized datasets, students apply advanced analytical methods to generate evidence-based recommendations, dashboards, and decision briefs. Emphasizing responsible data use, equity, and change management, the course culminates in a practitioner-ready project that demonstrates how analytics can drive meaningful improvements in care delivery and health-system performance.

This graduate-level course equips students to harness analytics and generative AI for stronger financial risk management, with a focus on assurance, fraud detection, and reporting quality. Through hands-on labs and real-world use cases, students learn to automate audit workflows, analyze financial documents, and design AI-enabled solutions that are ethical, transparent, and auditor-ready. Emphasizing governance, internal controls, and professional ethics, the course prepares future leaders to responsibly integrate advanced technologies into financial oversight and decision-making.

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.