Program Requirements
Students completing this concentration will have training in a diverse set of methods in analytics and tools to conduct analyses as applied in a variety of managerial disciplines. Today, business professionals, managers, and entrepreneurs need to be able to leverage the power of data that is collected. The Business Analytics concentration provides students with essential skills and knowledge needed to navigate in the world of data. This Concentration offers courses with a strong practical and applied orientation from a variety of managerial disciplines.
Required Courses (3 credits)
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INSY 336 Data Handling and Coding for Analytics (3 credits)
Overview
Information Systems : Preparation and analysis of data for business analytics. Topics include: data acquisition, data manipulation and computer programming for statistical analysis.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Prerequisite: MGCR 331
Restrictions: Open to U2 and U3 students.
Complementary Courses (12 credits)
3-6 credits from the following:
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MGSC 401 Statistical Foundations of Data Analytics (3 credits)
Overview
Management Science : This course will provide statistical foundations for data analytics. In this course, we will learn an introduction to advanced statistical techniques and methodologies including sampling, regression, time-series and multi-variate statistics. We will support our approach by looking at applied examples and real cases and datasets across several business areas, including marketing, human resources, finance, and operations. Students will apply their skills to interpret a real-world data set and make appropriate business recommendations.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Prerequisite(s): MGCR 271 or equivalent.
Restrictions: Open to U2 and U3 students
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MGSC 416 Data-Driven Models for Operations Analytics (3 credits)
Overview
Management Science : Examination of how data-driven models have been used to transform businesses and industries, using examples and case studies in e-commerce, retail, social and online networks, sports analytics, and online advertising. Demonstration of the use of data-driven analytics methods such as time series forecasting, network models, mixed-integer optimization, matching markets and exploration/exploitation.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
3-6 credits from the following:
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INSY 446 Data Mining for Business Analytics (3 credits)
Overview
Information Systems : Practical methods and techniques for data mining and predictive analytics to solve business problems. Use of statistical tools for hands-on learning. Topics covered include supervised learning, unsupervised learning, and text mining.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Corequisite: INSY 336
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MGSC 404 Foundations of Decision Analytics (3 credits)
Overview
Management Science : This course teaches quantitative methods used in business decision making. Topics include: optimization models, decision trees, simulation, and computer simulation. Business applications of these techniques are emphasized. Students in this course will acquire expertise in computer based methods for decision making, through computer analysis of real-life problems.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Restrictions: Open to U2 and U3 students.
0-6 credits from the following:
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ACCT 451 Data Analytics in Capital Market (3 credits)
Overview
Accounting : Exploration of how financial and non-financial metrics can be linked to business performance through experiential learning, with a focus on financial statement analysis, earnings and return predictability, textural analysis, earnings management and fraud detection. Introduction to SAS software and financial accounting databases such as CRSP, Compustat, and I/B/E/S, and alternative data sources such as SEC Edgar that enables work across different database to make better financial statement analysis and decisions.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
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BUSA 471 Artificial Intelligence Ethics for Business (3 credits)
Overview
Business Admin : Frameworks to highlight model explainability, data anonymity and privacy, bias and fairness. Analytical tools that will allow managers to incorporate these ethical concerns during artificial intelligence development and deployment.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Prerequisite: INSY 336
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FINE 460 Financial Analytics (3 credits)
Overview
Finance : An extensive study of the empirical methods casually used in the different subfields of finance. Examination of the most popular statistics models used in finance, both from a theoretical and practical point of view. An important emphasis will be put on the distinction between models of financial mechanisms, and those motivated purely by efficacy.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Prerequisites: MGCR 341
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INSY 442 Data Analysis and Visualization (3 credits)
Overview
Information Systems : Overview of methods and tools for analyzing business data to improve business decision-making, focusing on data visualization using hands-on learning.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Prerequisites: MGCR 331 or permission of the instructor and approval of the BCom ProgramOffice.
Restriction(s): Open to U2 and U3 students.
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INSY 446 Data Mining for Business Analytics (3 credits)
Overview
Information Systems : Practical methods and techniques for data mining and predictive analytics to solve business problems. Use of statistical tools for hands-on learning. Topics covered include supervised learning, unsupervised learning, and text mining.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Corequisite: INSY 336
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INSY 448 Text and Social Media Analytics (3 credits)
Overview
Information Systems : The unlimited opportunities that exist today to leverage the power of user generated content analytics, focusing on questions ranging from strategic to operational matters pertaining to a firm’s social media initiatives, metrics to capture relevant outcomes, and predictive analysis to link social media chatter to business performance.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
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INSY 463 Deep Learning for Business Analytics (3 credits)
Overview
Information Systems : Theory of Neural Networks and its applications for business analytics, including how to build and train Neural Networks to derive insights from unstructured text and image data in business contexts. Introduction to the ecosystem of software packages needed in Python, including: NumPy, Pandas, Sklearn. The theory and implementation of Neural Networks and Deep Learning. How to apply various deep learning models to real-world problems and demonstration of their power in the new data-abundant business world.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
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MGSC 483 Analytics-Based Community Project (3 credits)
Overview
Management Science : Aiding a host community organization in the application of analytics, with the aim of helping to improve the community's operations for the good of society.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Prerequisite: Permission of Instructor
Restrictions: Only open to U3 students.
This interdisciplinary experiential-learning course will partner students with an NGO to draw on that organization’s knowledge in sustainability or analytics. During these projects, students will implement AI and analytical tools to, for example, study the health of bee colonies, track microplastics in the ocean, help micro-farming initiatives, or develop compost optimization. Students will earn credits from McGill University by developing experiential, all while living on a natural reserve in Costa Rica located steps away from Playa Conchal.
There is an additional fee of $3050 that covers accommodation for 10 nights in Costa Rica, all ground transportation, entrance to national parks and natural reserves, most meals, and all course infrastructure and materials.
**Due to the intensive nature of this course, the standard add/drop and withdrawal deadlines do not apply. Course Add is the fourth lecture day.
**Web ADD only.
**No web drop allowed.
**Web withdrawal not applicable.
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MRKT 440 Marketing Analytics (3 credits)
Overview
Marketing : Analytic techniques available to marketing managers including practice with actual data sets to use the techniques. Topics covered will include customer and product analytic models, digital marketing, and marketing resource allocation.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
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MRKT 442 Customer Analytics (3 credits)
Overview
Marketing : Identification of common data science solutions to customer analytics. What, when, where and how to collect customer data. Basic customer analysis and assessment of the influence of marketing programs on business performance and customer satisfaction. Insights gained from analytics to a non-technical audience. Examination of the cutting edge applications of customer analytics and emerging trends.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Prerequisite: MGSC 401
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ORGB 330 People Analytics (3 credits)
Overview
Organizational Behaviour : This is the era of big data. Companies and organizations are collecting an enormous amount of information and we are only just beginning to grasp the ways in which this information might be used. This course covers the emerging field of people analytics, which involves applying data collection and analysis techniques to improve the management of people within organizations. We will cover current people analytics techniques, common pitfalls, and possible shortcomings of people analytics, as well as the ethical questions involved in undertaking such analyses.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Or any related undergraduate topics course (with approvals from Business Analytics and the BCom Office).
