Teaching

This course provides students with an intuitive and non-technical introduction to data analytics in healthcare, and the importance of models in interpreting data. The key to effective analytics is a scientific approach to management. The course focuses on the thinking skills that managers must have in order to understand and, more importantly, influence the types, quality, and usefulness of the analytics carried out in an organization. The course makes the case that there is a difference between “what data says” (facts) and “what data means” (relationships). Facts provide information about “how the world looks” while relationships provide information about “how the world works”. Mistaking facts for relationships can result in misguided and potentially costly decisions. Furthermore, understanding the nature of a relationship in data is needed to assess whether recent advances in artificial intelligence will be helpful, or whether other analytical techniques are needed.

Digital marketing leverages the ability to communicate and distribute information in bits, rather than atoms. Social media, search engines, mobile commerce, digital advertising, and online marketplaces are impacting competition for all firms, large and small. Drawing on some common themes across digital marketing platforms, we examine (i) how companies find and serve customers using digital tools, (ii) the kinds of digital products that companies offer, (iii) the role of distance in the customer-company relationship when information is digital, (iv) the locus of control of brand-related messages, (v) the concept of privacy, and (vi) the digital targeting of marketing tactics. Broadly, for each technological innovation, we will emphasize what is different, and what is not, for consumers, and for the production, distribution, and communication of goods and services.

This program helps students position themselves and their companies to advance their data literacy and confidently lead a data-driven corporate culture. Upon completion, students will gain: (1) Improved data literacy, the ability to communicate effectively with data analysts to understand when to use (and how to interpret) descriptive, predictive, and prescriptive analytics (2) Expert guidance on evaluating various data analytics tools, such as dashboards, machine learning, prediction models, and regression analysis (3) Techniques to apply scientific thinking to business, such as examining cause and effect in data analysis and designing an experiment and (4) A deeper understanding of how to build a data-driven organization, including managing privacy concerns and developing strategies for cultivating evidence-based decision-making.

AI is poised to infiltrate all industries and markets. Businesses at the frontier have begun to take advantage of machine learning to grow and transform their strategies. Leaders need a deep understanding of what AI can do in order to position their organizations for the upcoming wave of competition based on AI, similar in some ways to what we experienced 25 years ago with the arrival of the internet. Through this program, students will learn how to identify opportunities for AI and implement them, and discover how to develop strategies with their leadership team for leveraging powerful AI tools. Note: this course is not technical, it does not take a computer science perspective on the topic. Instead, it focuses on the economics of AI and the implications for business.

This course introduces students to the use of data in model-based decision making. Technological improvements have resulted in businesses having access to more data and more types of data than they have ever had before. This wealth of data means that it is now critical for managers to understand how data can and should be used as part of the decision-making process. In this course, we make the case that there is a difference between “what data say” (facts) and “what data mean” (relationships). Facts provide information about “how the world looks” while relationships provide information about “how the world works”. Mistaking facts for relationships can result in misguided and potentially very costly decisions. Thus, a key challenge that businesses face is determining when the facts observed in data can be interpreted as relationships on which decisions should be based. The objective of the course is to provide students with the skills to think carefully, critically, and creatively about the data available to them and the facts presented to them to improve their ability to integrate data into their decision-making process.

The premise in this course is that customer value is a prerequisite to business success. We explore what the term “customer value” means, how to align the company’s product or service with customer needs, and distinguish it from competitive offerings. The task involves marshalling the efforts of the company and its network partners to provide customers with a superior total package of benefits – comprising the product itself, associated services, brand image, appropriate pricing, and availability. An intimate understanding of customers’ needs and behaviour is critical and we will focus strongly on this topic. Students will be engaged in active research and interpretation of information about potential areas for delivering value. The course develops skills in strategy development, research and analysis, and judgment in making business decisions that touch on customer value.

This course teaches future managers how to extract information from data using statistical tools and how to apply probabilistic thinking to managerial problems. Topics include statistical study design, inference, regression analysis, and decision analysis. Applications to all functional areas of management are discussed. Upon course completion, the student will be better able to: (1) Identify and formulate problems where statistics can have an impact (2) See the relevance of statistics and apply what has been learned to career practice and to other business courses (3) Distinguish between routine and special problems requiring statistical analysis (4) Understand statistical methods for quality improvement (5) Assess data with healthy skepticism and seek expert help when needed and (6) Recognize when better data and information are needed for decision-making.

This course introduces students with a hands-on introduction into how to apply the statistical techniques they learn in their econometrics courses to real world data. Together with other methods courses, it will prepare students for doing independent empirical research. Offered in the spring of every second year. (Syllabus)