Rotman School of Management

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Course descriptions

What you will study

Courses within the program consider how data and analytics can be used for a number of different management situations. You will learn from experts in these fields.

Analytics in Management (RSM8901)

Course co-ordinator: Joseph Milner, Professor of Operations Management and Statistics

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:

  • 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.
  • 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.

Data-Based Management Decisions (RSM8502) 

Instructor: David Soberman, Professor of Marketing

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.

Analytics Colloquia (RSM8431)

Instructor: Brian Keng, Data Scientist in Residence and Chief Data Scientist at Rubikloud Technologies

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 colloquia expected to be offered in 2018/19 include (please note that the list below is subject to change):

  1. Ethics in Data Analytics and AI
  2. APIs and Google Analytics
  3. Hadoop for Analytics
  4. Text mining and Sentiment Analysis
  5. Graph Analysis in Machine Learning
  6. Neural Networks I: Tuning and Topology
  7. Neural Networks II: Deep Learning
  8. Fraud Detection and Money Laundering

Objectives of the course:

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

Management Analytics Practicum (RSM8432) 

Instructor: Dmitry Krass, Professor of Operations Management and Statistics

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. 

Objectives of the course:

The objective of the practicum course is to guide you through the process of developing and presenting this analytical view. In the process of working on their projects, the students will also be expected to:

  • Develop effective techniques for working productively in groups, providing constructive and fair peer evaluations to their group partners, and resolving any intra-group issues that may arise
  • Develop effective techniques for communicating with their business counterparts, providing interim updates on the project progress and challenges
  • Maintain a professional approach to issues of confidentiality, data, modeling, and academic integrity 

Structuring and Visualizing Data for Analytics (RSM8411)

Instructor: Allan Esser, Adjunct Instructor, Operations Management and Statistics; Professor, School of Business, George Brown College

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.

Objectives of the course:

  • Demonstrate the ability to prepare, explore and validate sample data for advanced analysis
  • Develop and implement BI (Business Intelligence) Dashboards to support business decision-making

Modeling Tools for Predictive Analytics (RSM8512) 

Instructor: Ryan Webb, Assistant Professor of Marketing

Predictive analytics (sometimes called data science) is increasingly important in a variety of industries and functional areas. In this hands-on course, students will be exposed to a variety of aspects of predictive analytics, starting with data acquisition, cleaning and preparation, proceeding to data modeling techniques, and then integrating these with decision analysis to support effective decision-making. The course will expose student to several software packages, including SAS—one of the workhorses of predictive analytics.

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

Big Data Analytics (RSM8413)

Instructor: Gerhard Trippen, Assistant Professor (Teaching Stream) of Operations Management and Statistics

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.

Objectives of the course:

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.

Tools for Probabilistic Models and Prescriptive Analytics (RSM8414)

Instructor: Opher Baron, Professor of Operations Management and Statistic

The emphasis of the course will be on systematic, logical thinking, problem solving, and risk analysis, 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.  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.

Objectives of the course:

  • To develop students’ ability to communicate effectively with spreadsheets in the area of fact-based, data-driven, quantitative decision making
  • To develop and reinforce students’ probabilistic modeling skills—when limited or no data exists to estimate effects of planned business decisions, and decision support must rely on explicit probabilistic assumptions
  • To develop students’ modeling skills in the areas of optimization and simulation modeling

Improving Customer Value with Analytics (RSM8521) 

Instructor: Tarun Dewan, Associate Professor of Marketing (Teaching Stream), UTSC

This course illustrates how managers can use data from various sources (sales data, historic consumption data, transactions data, and marketing effectiveness data) to make more effective business decisions. We will understand the basic principles of data-driven marketing in verticals as diverse as financial services and banking, direct marketing, leisure and entertainment, wholesale management, packaged goods, and retailing. Applications will range from targeting decisions, segmentation decisions, customer relationship management (CRM), vendor and supplier management, loyalty programs, revenue management, marketing mix decisions, price and service differentiation, and service quality management.

Objectives of the course:

  • Understand the key principles of CRM (customer relationship management) and SRM (supplier relationship management)
  • Design and build a data-based marketing system for a given organization and a given set of issues, and in the process pick up some valuable consulting skills
  • Think about appropriate methods for creating a customer portfolio matrix, and using the matrix to drive business decisions. As a specific application, you will learn how to do an RFM analysis.
  • Construct appropriate financial metrics to determine the ROI of marketing programs
  • Identify means of calculating customer worth, value, churn rates, quality—and use these indices in making decisions
  • Formulate answers to: How much should I spend per prospect (or customer) to acquire (or retain) them?  How should I allocate my marketing budget across acquisition and retention?
  • Think about value in the context of a multiproduct and in-supply chain corporation
  • Identify critical success factors for CRM-type efforts

Analytics for Marketing Strategy (RSM8522)

Instructor: Nitin Mehta, Professor of Marketing

This course is about how to use data to answer marketing questions. The questions we examine are the quintessential marketing ones: Is my advertising effective? What is it doing? Which advertising medium is most productive? What is the price-elasticity of demand for my product? How does it compare to my advertising elasticity? Are my promotions leading to incremental sales? How sticky is my demand? Are consumers brand-loyal? How can I measure the value of my brand?

Objectives of the course:

This course focuses one of the key uses of analytics in marketing: measuring the effectiveness of different marketing channels and forming the optimal marketing mix. The questions the course examines are the quintessential marketing ones: Is my advertising effective? What is it doing? Which advertising medium is most productive? What is the price-elasticity of demand for my product? How does it compare to my advertising elasticity? Are my promotions leading to incremental sales? How sticky is my demand? Are consumers brand-loyal? How can I measure the value of my brand?

Analytic Insights using Accounting and Financial Data (RSM8224)

Instructor: Scott Liao, Associate Professor of Accounting

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: (1) better understand and appropriately use accounting and other financially-related data, (2) more confidently conduct empirical modelling to make decisions and solve the problem at hand, and (3) appreciate the strengths and limitations of empirical analysis.

Optimizing Supply Chain Management and Logistics (RSM8423)

Instructor: Philipp Afeche, Professor of Operations Management and Statistics

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:

  • 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
  • effectively justify and communicate the recommendations so they are convincing for implementation

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The first application deadline is November 20, 2018

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Key Facts

Duration

  • 9 months, full-time

Intake

  • August, one intake per year

Tuition fee (2018 entry):

  • Domestic = $46,000 CAD
  • International = $63,000 CAD


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