Core Courses

Students are required to take all 10 of the below core courses in the sequence outlined in the program curriculum.

F = Fall Term
S = Spring Term
Y = Program-Long

RSM8901HF – Analytics in Management (introductory term)

This course will introduce the students to the key functional areas of management and the typical decisions they face. The course will illustrate how each functional area approaches some common managerial problems, and where data and analytics may be usefully employed. The course will provide a framework for both the analytical tools and specific managerial problems discussed in subsequent courses in the MMA program.

Objectives of the course:

The course will allow a student to construct a functional area—a managerial decision “map” where various analytical approaches may be slotted. This map will serve as a reference point for the subsequent courses.
Provide students with a general overview of the key functional areas of management and the main decisions they face.
Provide an overview of key concepts and terms in each functional area.
Provide examples of how different functional areas approach typical managerial decisions.
Provide an overview of various uses of analytics in managerial decision problems. Students should understand how “hard” analytical approaches can be combined with “softer” decision analysis to arrive at effective decision recommendations.

RSM8502HF – Data-Based Management Decisions (introductory term)

The goal of this course is to introduce the students to key ideas about data-intensive business decision-making. Key ideas explored in the course include:

The difference between what the data “say” and what the data “mean”
Understanding and measuring randomness and its implications; different sources of randomness (inherently random outcomes vs. measurement errors)
The importance of mapping out the data generation process
The numerous ways to obtain or collect data
Understanding various biases in data and their implications on analysis
The value of experiments
Differences between various modeling types

Objectives of the course:

The course is built upon basic probabilistic concepts already familiar to students (e.g. distributions, measures of variability and co-variability, standard errors and statistical hypothesis) and provides them with techniques to apply these concepts in order to facilitate robust, data-driven decision-making.

RSM8411HF – Structuring and Visualizing Data for Analytics

This course will expose the learner to a broad range of technical skills that are required to prepare data for advanced analysis. Using a combination of theory and practical exercises and case studies, the learner will develop the data acquisition and preparation skills that are a necessary pre-requisite to applying advanced statistical modelling, data mining techniques, or machine learning algorithms to their data.
We will use Structured Query Language (SQL) to implement all required data processing and data mining manipulations. Students will learn various SQL techniques through applying them to real business cases and various data sources.

RSM8413HF – Machine Learning Analytics

This course will introduce the students to a diverse uses of big data techniques. These techniques are often aimed at identifying and quantifying various structures in the data (e.g. What are the key similarities between certain business units with respect to customer satisfaction? What are the characteristics of important customer segments?). Model validation and effective communication of model-based results will be stressed. The course will employ a “white-box” methodology, which emphasizes an understanding of the algorithmic and statistical model structures.

Objectives of the course:

To develop the students’ ability to:

Apply a method or algorithm to a large, real-world data set
Clearly explain why a particular method or algorithm is needed
Understand how a method or algorithm works
Follow the logic of an algorithm or method step by step
Gain a white-box insight into the inner workings of the method or algorithm

RSM8414HF – Tools for Probabilistic Models and Prescriptive Analytics

The course’s goals are:

To improve students’ ability in the area of business analytics decision making supported by fact-based, data-driven, quantitative analysis.
To develop, integrate, and reinforce students’ modeling skills in a variety of decision-making-oriented applications;
To enhance and reinforce students’ ability to intelligently use data and information in the presence of uncertainty

RSM8512HF – Modeling Tools for Predictive Analytics

This course provides a hands-on introduction to the wide variety of models and techniques used in predictive analytics, including linear and non-linear regression models, classification algorithms, machine-learning techniques like SVM and reinforcement learning, and causal inference. There will be an emphasis on conceptual understanding and interpretation of the models, so that students can interpret the results of these techniques to support effective decision-making. The course will be complemented by many hands-on exercises using the R programming language.

Objectives of the course:

Expose students to the application of predictive analytics, big data, machine learning, and decision analysis techniques in a variety of business decisions
Enable students to:
- Structure business decisions as analytical problems
- Identify which data sources are needed to provide an answer
- Understand how the data should be structured for analysis
- Use data transformation and manipulation techniques
- Apply appropriate analytical tools
- Obtain insights from the results and be able to apply these insights to the managerial problem at hand
- Communicate findings effectively

RSM8521HS – Leveraging AI and Deep Learning Tools in Marketing

This course will cover some of the latest advances in Artificial Intelligence and Deep Learning and how they can be used in a wide variety of marketing applications. It will introduce students to the fundamental concepts of neural networks and deep learning, provide hands-on practice with various marketing datasets, and showcase a wide range of applications from image recognition to natural language processing. These techniques will be applied to a variety of marketing applications such as recommendation engines, customer comments analysis, targeting, churn, segmentation and lifetime value.

RSM8431YY – Analytics Colloquium

The course will be composed of short modules (“colloquia”) taught by practitioners in the related fields. Each module will be delivered in 2-3 sessions of 2-3hrs each and will include at least one graded assignment. The course will provide students with skills that will be instrumental to achieving career success in data science and management analytics. The course will start in the fall term of the MMA program and continue through the winter term.

These modules are planned to be offered in the 2024-2025 Academic year (please note that this list is subject to change):

Introduction to Computer Science with Python
Responsible AI
Product Management
Data Acquisition with API
Social Network Analysis
Process and Queue Mining
Cloud Computing & MLOps
Analytics for Customer Relationship Management
Generative AI Models

Objectives of the course:

The goal of this course will be to expose students to current topics and themes in data science and management analytics.

RSM8432HY – Master of Management Analytics Practicum Project

In this practicum course, you will learn how to apply model- and data-based decision making to a problem that a real organization currently faces. These problems are not only more realistic than the problems you will face in individual courses, they are more holistic. Rather than focusing on an individual component of an analytical task, they involve all key steps in a typical management analytics project, from understanding the underlying managerial issues, to proposing an effective analytical solution, constructing a modeling plan, identifying the required data sources, structuring an analytical data view, executing your modeling and data plans, and, finally, presenting your findings and proposed implementation plans.

Students will be broken up into teams of 3-4 and assigned to one of projects proposed by host organizations, working alongside an internal analytics team, while periodically meeting with their faculty coach. The course starts in September and runs until April. During the Fall term the students will primarily focus on creating a project proposal, data request and modeling plan. During the Spring term, the students will execute on this plan and summarize their results. There will be internal (host organization) and external (Rotman) presentations in each term.

Objectives of the course:

The objective of the practicum course is to improve students’ skills in all key steps of a management analytics project: understanding the managerial background, structuring the project, working with data, producing relevant results, presenting them effectively, while managing the project effectively along the way.

RSM8601HY – Self Development Lab

More than 80% of human work in organizations is carried out in groups and teams, and around 80% of an executive’s time is spent on communicating. Generative AI tools (and, in particular, Large Language Models and Large Symbol Models capable of interacting with people in textual or symbolic forms) will likely be a complement or substitute for significant components of the individual work people still do in organizations within five years. We are aiming to help each participant to become more skilled at those skills that are uniquely human in an executive setting – by becoming an astute and informed observer, evaluator and designer of her/his own social, relational and communicative interactions, and by becoming informed users of Large Language Models to generate useful prototypes, blueprints for communications in the specific genres of business, which include emails, memoranda, presentations and pitches.

Elective Courses

Students are required to take 3 of the following 6 elective courses in their Spring term. Information regarding elective course selection will be provided prior to the start of the Spring term.

RSM8224HS – Analytic Insights Using Accounting & Financial Data

This course will build on the tools, skills, and concepts developed in the first half of the program. As an applied course, students will be expected to routinely perform accounting-based empirical analysis by using the analytics skills they have learned (e.g. SAS, R, and Python). Students must practice their ability to formulate appropriate empirical research questions in the context of the business problem or opportunity. Specifically, they will first learn how to approach and appreciate accounting information and then take advantage of the rich accounting and finance dataset to help businesses solve various problems or enhance corporate profitability. At Rotman, we have an abundance of financial accounting data including COMPUSTAT, CRSP and IBES to address a large variety of business, finance, and accounting questions. The course has four modules: 1) understanding accounting information, 2) use of financial information in the equity market, 3) use of financial information in the debt market, and 4) use of disclosure.

Objectives of the course:

At the end of the course students will:

Better understand and appropriately use accounting and other financially-related data,
More confidently conduct empirical modelling to make decisions and solve the problem at hand, and
Appreciate the strengths and limitations of empirical analysis.

RSM8301HS – Analytical Methods in Finance

In this course, students will:

Learn the tools necessary for the valuation and hedging of derivatives
Understand and apply risk management tools such as value at risk and expected shortfall
Review the nature of market risk, credit risk, and operational risk and how they impact the regulatory requirements for banks
Learn alternative approaches to modeling interest rates

A number of applications of machine learning in finance will be explored. Students will also be engaging in a major natural language processing case study.

RSM8415HS – Service Analytics for Management Analysis

In this course, students will learn decision making in different service industries including education, healthcare, retail, and car sharing using analytics. We will use Python (and other tools as necessary) to explore the application of standard analytics tools in the context of the service industry.

Students will learn how to (i) design experiments effectively, (i) apply appropriate hypothesis testing to confirm statistically significant variables, (iii) handle imbalanced datasets, (iv) develop appropriate predictive models to optimize planning, (v) implement text mining and natural language processing to predict sentiment and emotion, and (vi) how to use simulations to estimate waiting time. We will discuss how analysis of these issues supports decision making and management in different services.

This content will be introduced to students with cases and data that permit them to experience the different tools. This exposure will improve students’ confidence in using analytical tools appropriately in the context of real-world problems in the service industry.

RSM8416HS – Healthcare Analytics

Transform data into impactful healthcare decisions with predictive analytics, optimization, and simulation skills.

This GPT-based course is designed for students aiming to build careers in the healthcare industry:

Learn to leverage data analytics to inform strategic decisions in healthcare settings.
Master techniques to forecast patient outcomes, healthcare demand, and resource needs.
Gain skills in optimizing healthcare operations, from staff scheduling to resource allocation.
Build simulation models to test scenarios and plan for uncertainties in healthcare.

RSM8423HS – Optimizing Supply Chain Management and Logistics

Operations and supply chain management functions are heavy analytics users in a number of industries, including retail, transportation, healthcare, and financial services. This course will focus on identifying, developing, and applying effective analytics models and tools to solve typical operations and supply chain management problems, including network design, inventory management, assortment and price optimization, and service process design.

Objectives of the course:

Knowledge
- Learn key concepts, models, tools and techniques for a range of typical problem settings in operations and supply chain management.
Problem-solving skills
- Develop ability to systematically identify, create, and apply effective analytics models and tools to solve typical operations and supply chain management problems. These problem-solving skills include the ability to:
  - Effectively justify and communicate the recommendations so they are convincing for implementation
  - Identify appropriate data needs
  - Link data to models and decision tools
  - Select and/or build the models and tools that best fit a business situation
  - Identify important mismatches between a model and the real system
  - Generate insightful model analyses and solutions
  - Correctly interpret the results
  - Make appropriate recommendations

RSM8522HS – Analytics for Marketing Strategy

This course is about how to use data to answer marketing questions. The questions we examine are the quintessential marketing ones: How do I identify my target segment? How do I effectively position my product? What features should I include in my product prior to its introduction? What is the price-elasticity of demand for my product? Is my advertising effective? What is it doing? Are consumers brand-loyal? How can I measure the value of my brand?

Objectives of the course:

This course will teach students how to apply widely-used techniques in marketing analytics to business problems. Students will learn how to use these techniques, along with real-world data, to achieve important marketing objectives such as: effective construction of market segments; effective product positioning; effective product design; measurement of price elasticity and brand value; measurement of advertising effectiveness.

Master of Management Analytics Courses