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.
RSM8411HY – 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.
RSM8413HY – Big Data Analytics
This course will introduce the students to a diverse collection 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.
RSM8414HY – Tools for Probabilistic Models and Prescriptive Analytics
In this course, we will learn how to structure, analyze, and solve business decision problems using Excel spreadsheets. We will focus on problems involving decision-making and risk analysis. The emphasis of the course will be on systematic, critical and logical thinking, and problem solving using spreadsheets as our primary tool. We will start with the basic techniques of good spreadsheet modeling and organization, and proceed to introduce a variety of modeling techniques and approaches. All along, we will critically think on how to interpret the results of our analysis process in the context of decision-making. These will be illustrated by building and analyzing problems in finance, marketing, and operations. While the underlying concepts, models, and methods of this course are mathematical in nature, we will develop them on the more intuitive and user-friendly platform of spreadsheets, always focusing on the ideas and insights, rather than the underlying mathematical details. The spreadsheet approach to problem solving is more accessible to managers, as they often find spreadsheets a natural medium for organizing information and performing “what if” analyses. We will study how to use Excel and various add-ins to perform such analyses and interpret them. We will study three specific techniques: optimization, decision trees, and simulation. The usage of these techniques in practice can improve the decision making process in many situations.
RSM8423HS – Optimizing Supply Chain Management and Logistics
“Operations and supply chain management functions are heavy analytics users in a number of industries.
This course will focus on a selection of important supply chain management decision problems.
For these decision problems, the course will focus on how to appropriately combine data, modeling, analytical
techniques and tools to systematically (1) understand, structure and formulate the problem; (2) evaluate key
performance metrics under various policies; (3) optimize key performance metrics; and (4) interpret and
communicate the results.
The course will draw on a range of analytical techniques in the areas of probability and statistics, optimization and
simulation. The course will focus on analyzing and solving business problems, by applying and building on the
(prerequisite) techniques and tools covered in Term I, rather than on developing these from first principles.
The course consists of a mix of lectures, discussion of business cases, and simulation games.
This course will draw on a range of software tools, focusing on whatever tool is most convenient for the problem at
hand, such as Excel with add-ins (Solver, @Risk, StatTools), Python or R, Tableau, and Arena.”
RSM8431HY – Analytics Colloquium
The course will be composed of short (2-3 week) modules (“colloquia”) taught by practitioners in the related fields. The course will provide students with skills that will be instrumental to achieving career success in data science. The course will start in the fall term of the MMA program and continue through the winter term. The goal of this course will be to expose students to some current topics and themes in 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 steps, from understanding the underlying managerial issues, to structuring an analytical data view, to effectively presenting your findings and proposed implementation plans. The problems you will deal with are also messier than the ones you encounter in class, in the sense that they may not initially be well-defined, may span functional areas, may invite competing approaches and explanations, and may lack ideal data.
RSM8502HF – Data-Based Management Decisions
“The goal of this course is to introduce the students to key ideas about data-intensive business decision-making. The ideas explored in the course include:
• Understanding that the questions a business needs answered precedes the collection and analysis of data
• 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)
• Introduction to standard questions and analyses that businesses need to address
• Understanding traps and biases in the data and their implications on the analysis
• Difference between various modelling approaches
In sum, this course is designed to get you excited about how you can use data and analysis to help a business make better decisions.”
RSM8512HY – 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.
RSM8521HS – Improving Customer Value with Analytics
“This course illustrates how managers can use data from various sources (sales data, historic
consumption data, transactions data, marketing effectiveness data) in making more effective business
decisions. We will understand the basic principles of data driven marketing in several industries.
Applications will range from targeting decisions, segmentation decisions, customer relationship
management (CRM), resource allocation, Retention and loyalty programs.”
RSM8522HS – Analytics for Marketing Strategy
The forces and dynamics of today’s market are making the marketing task more complex and competitive. In this context, a successful marketing strategy involves complementing the basic elements of marketing mix, with analyzed data, and appropriate models and simulations incorporated in a consistent and professional way. elements of marketing strategy as well as the skills needed to make intelligent use of marketing data in making recommendations about marketing strategies. These are learned through a combination of lectures, cases, and “hands-on” exercises with actual business data. The course is designed to equip the student with practical “know how”, which can be used immediately on the job. Students gain a working knowledge of segmentation, targeting and positioning, conjoint analysis for product design, forecasting the demand of a new product, pricing analysis, channel design, allocation of resources amongst different promotional vehicles and an introduction to digital marketing analytics.
RSM8901HF – Analytics in Management
This course serves as an introduction to the functional areas of business: Marketing, Operations, Accounting, Finance, Organizational Behavior, and Strategy. Our focus will be on learning the main concepts of each of these areas and how they relate to each other. We will also consider the role of analytics in these areas in creating descriptive and prescriptive models to aid in decision making. As such the modules in each area will lay the foundations for further study of these topics throughout the year. Students will participate in case analyses and discussions, group activities, and a group presentation.