Your browser is not supported. Please download one of the following browsers:

2020/21 Special Topics Courses

Notes:

  • Content in any given year will depend on the instructor.
  • Enrolment is restricted to Rotman Commerce students.
  • To take a 400-series course, 14.0+ credits are required; and for a 300-series, 9.0+ credits are required; except when otherwise indicated in the course descriptions below
  • Course descriptions may be found following the listings

Fall-Term Sections

RSM313H1F – Foundations of Artificial Intelligence for Management
RSM315H1F – Taxation for Business Professionals
RSM316H1F – Machine Learning
RSM319H1F – Introduction to Creative Destruction Lab (Intro Course)
RSM415H1F – BEAR Laboratory
RSM416H1F – Technology Product Management
RSM417H1F – Sustainable Finance

Winter-Term Sections

RSM312H1S – Data and Information Management for Business Analytics
RSM314H1S – Sports Analytics
RSM316H1S – Machine Learning
RSM319H1S – Creative Destruction Lab (Intro Course)
RSM412H1S – Service Operations Analytics


RSM312H1S – Data and Information Management for Business Analytics

Instructor: Allan Esser
Prerequisite: No prerequisite course.
May be applied to the following Focuses: Finance, Financial Statement Analysis, International Business, Leadership in Organizations, Marketing and Strategy and Innovation

Please find the description for this course here.


RSM313H1F – Foundations of Artificial Intelligence for Management

Instructor: Ryan Webb
Prerequisite: ECO 220Y1 or ECO 227Y1
May be applied to the following Focus: Marketing

Artificial intelligence — the application of machine-learning techniques to prediction problems historically performed by humans — is transforming business and society. This course provides a hands-on introduction to the wide variety of algorithms used in applications of machine-learning. The technical topics will include linear and non-linear regression models, classification algorithms, and more recent machine-learning techniques rooted in neuroscience like reinforcement learning and deep learning. Application topics will include predicting consumer choices, MLB salaries, and Super Mario Bros. There will be an emphasis on conceptual understanding, 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.


RSM314H1S – Sports Analytics

Instructor: Matthew Mitchell
Prerequisite: ECO220Y1

This course will apply concepts of analytic management and data analysis to the sports world. Sports offers a unique opportunity to measure employee (especially player) performance and firm decisions in response to that data. Students will learn how econometric analysis can help in this process. The class will address both popular discussions of sports analytics as well as academic papers at the research frontier since those papers are an opportunity to learn about the latest thinking. The class will be an opportunity to use evidence from sports as a way to assess other theories that are a part of business education.

We will study the use of analytics in a variety of sports settings. Students will have the opportunity to do their own data analysis using sports data from a variety of sports. Although not expressly focused on hockey, we will sometimes use data from the hockey analytics firms Stathletes brought to us by co-founder Meghan Chayka. The course will also involve guest speakers from the sports industry to better understand the ways in which analytics are being used in different sports.


RSM315H1F – Taxation for Business Professionals

Instructor: Alexander Edwards
Prerequisite: TBA
May be applied to the following Focus: Financial Statement Analysis

Description: TBA


RSM316H1F/S – Machine Learning

Instructor: Raymond Kan
Prerequisite: ECO220Y1
May be applied to the following Focus: Finance

Description: This course will provide an overview of the basic tools in data analysis and machine learning, with emphases on applications in finance with big data. Data analysis and machine learning play an important role in FinTech. Individual investors and financial institutions who are able to leverage these new tools and technology will have a significant advantage. This course discusses these new opportunities and challenges. It seeks to equip students with these highly coveted skills in the market.

Real world finance problems often deal with large datasets, traditionally with historical price and return as well as data on financial statements. More recently, other databases like consumer credit and online data (like Google search) are also becoming more important for financial analysts. Dealing with such large datasets require tools to manipulate them, and we will introduce the use of Python and detailed instructions on how to perform analysis on large datasets. Once students become comfortable with the use of Python and manipulation of large sets, we will introduce students to tools in machine learning.

Machine learning plays an important role in our financial market, from approving loans, managing portfolios, to assessing risks.  Advances in machine learning technology have enabled financial institutions to explore the applications of machine learning techniques in areas like customer service, personal finance, wealth management, and risk management.


RSM319H1F/S – Creative Destruction Lab (Intro Course)

Instructor: Mara Lederman (Fall Term) / Joshua Gans (Winter Term)
Prerequisite:  TBA

Description: For a description of this course please visit this page:  Creative Destruction Lab (Intro Course)


RSM412H1S – Service Operations Analytics

Instructor: TBA
Prerequisite: RSM270H1
May be applied to the following Focuses: Finance, Financial Statement Analysis, International Business, Leadership in Organizations, Marketing and Strategy and Innovation

53% of companies are using big data analytics today, up from 17% in 2015 with services industries fueling the fastest adoption.  Analytical practitioners today have a vast array of analytical capabilities and techniques at their disposal to help organizations harness their data, use it to identify new opportunities and drive strategic business decisions. These range from the most fundamental techniques, “descriptive analytics”, which involve preparing the data for subsequent analysis, to “predictive analytics” that provide advanced models to forecast and predict future, to the top-notch of analytics called “prescriptive analytics” that utilize machine-based learning algorithms and dynamic rule engines to provide interpretations and recommendations.

In this course, students will develop a working familiarity with the grounding principles of data analytics tools and techniques.  Topics covered include data visualization and interpretation, feature and model selection, statistical and machine learning techniques, optimization, and deep learning. The course will also provide step-by-step training on how to apply these techniques in modern computer languages such as Python and R Studio, the most popular tools in the analytics field. During the semester, common business questions will be discussed in the context of specific services including Insurance, Healthcare, and Finance industry. Students will learn how to apply and test their underlying analytics techniques to provide practical and managerial insights using these cases.


RSM415H1F – BEAR Laboratory

Instructor: Bing Feng
Prerequisite: TBA
May be applied to the following Focus: Marketing

Course Description: TBA


RSM416H1F – Technology Product Management

Instructor: Omkar Chetty
Prerequisite: RSM250H1
May be applied to the following Focus: Marketing

Product Managers are responsible for owning a product at every stage of its life cycle, bringing it from ideation to market and ensuring success. TPM Course is designed to introduce the very latest product management concepts and techniques. Students will learn to develop product ideas – from modeling and testing market opportunities with an MVP, to leading design and development teams with an agile workflow.

This goal of this course is to demystify technology product management and reduce the barrier of entry into the profession. This course will provide the foundational knowledge of technology product management, and cover the following topics (with real world examples/cases) –

1. What is a PM? Why are they crucial both in large tech organizations and start-ups?
2. What does a PM do and with whom do they work at different stages of the product life cycle? What are the attributes of successful PMs?
3. What techniques do PMs use to understand customer needs and validate demand for a product?
4. What does a PM need to know about user experience design?
5. What is the difference between waterfall and agile software development methods, and when/why would one chose one over the other?
6. Does a PM need to know about technology, e.g., tech stacks, APIs, databases? If so, to what extent?
8. What are the next steps one could take to continue the product journey?


RSM417H1F – Sustainable Finance

Instructor: Jan Mahrt-Smith
Prerequisite: RSM332H1; RSM333H1  (Please note that this course is open to 3rd and 4th year students)
May be applied to the following Focus: Finance

This course prepares students for the emerging jobs where future leaders must apply their commerce training together with a deep understanding and appreciation for sustainability issues and goals. Examples of these jobs range from finance roles in asset management, where ESG/CSR and other sustainability objectives and measures (e.g. fossil fuel divestment, social justice, development etc.) are fast becoming required currency, to roles inside large and small non-financial corporations and non-for-profit institutions where financial literacy and finance skills are required in the pursuit of goals related to sustainability. The market for these jobs is expanding and they range from opportunities in the traditional financial sector to the corporate sector to small fin-tech firms to the entrepreneurial start-up world. Risk management is another sector that worries about global, national, and local risks arising in fields associated with sustainability like climate change and income inequality.

This is a finance course, but it is not aimed specifically at students with high skills and career goals in finance. We expect students to have a wide range of proficiency in finance skills and diverse career goals. Yet, we believe that all students’ personal commitment to understanding sustainability issues will be high, and we encourage everyone to value and appreciate the challenge and rigor of the course in the pursuit of these goals. It will take solid business thinking and advanced business skills to encourage firms and organizations of all types to collaborate to overcome sustainability challenges. We expect students to be reasonably familiar with basic finance concepts from the RSM230/332/333 courses. A background in sustainability is not required, but an interest is helpful. The course is case and lecture based, includes discussions and guest speakers, and involves hands-on projects.