Business Analytics Concentration (B.Com.) (15 credits)
Offered by: Management (Desautels Faculty of Management)
Degree: Bachelor of Commerce
Program credit weight: 15
Program Description
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.
Note: For information about Fall 2025 and Winter 2026 course offerings, please check back on May 8, 2025. Until then, the "Terms offered" field will appear blank for most courses while the class schedule is being finalized.
Required Courses (3 credits)
| Course | Title | Credits |
|---|---|---|
| INSY 336 | Data Handling and Coding for Analytics. | 3 |
Data Handling and Coding for Analytics. Terms offered: this course is not currently offered. Preparation and analysis of data for business analytics. Topics include: data acquisition, data manipulation and computer programming for statistical analysis. | ||
Complementary Courses (12 credits)
3-6 credits from the following:
| Course | Title | Credits |
|---|---|---|
| MGSC 401 | Statistical Foundations of Data Analytics. | 3 |
Statistical Foundations of Data Analytics. Terms offered: this course is not currently offered. 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. | ||
| MGSC 416 | Data-Driven Models for Operations Analytics. | 3 |
Data-Driven Models for Operations Analytics. Terms offered: this course is not currently offered. 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. | ||
3-6 credits from the following:
| Course | Title | Credits |
|---|---|---|
| INSY 446 | Data Mining for Business Analytics. | 3 |
Data Mining for Business Analytics. Terms offered: this course is not currently offered. 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. | ||
| MGSC 404 | Foundations of Decision Analytics. | 3 |
Foundations of Decision Analytics. Terms offered: this course is not currently offered. 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. | ||
0-6 credits from the following:
| Course | Title | Credits |
|---|---|---|
| ACCT 451 | Data Analytics in Capital Market. | 3 |
Data Analytics in Capital Market. Terms offered: this course is not currently offered. 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. | ||
| BUSA 471 | Artificial Intelligence Ethics for Business. | 3 |
Artificial Intelligence Ethics for Business. Terms offered: this course is not currently offered. 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. | ||
| FINE 460 | Financial Analytics. | 3 |
Financial Analytics. Terms offered: this course is not currently offered. 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. | ||
| INSY 442 | Data Analysis and Visualization. | 3 |
Data Analysis and Visualization. Terms offered: this course is not currently offered. Overview of methods and tools for analyzing business data to improve business decision-making, focusing on data visualization using hands-on learning. | ||
| INSY 446 | Data Mining for Business Analytics. | 3 |
Data Mining for Business Analytics. Terms offered: this course is not currently offered. 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. | ||
| INSY 448 | Text and Social Media Analytics. | 3 |
Text and Social Media Analytics. Terms offered: this course is not currently offered. 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. | ||
| INSY 463 | Deep Learning for Business Analytics. | 3 |
Deep Learning for Business Analytics. Terms offered: this course is not currently offered. 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. | ||
| MGSC 483 | Analytics-Based Community Project. | 3 |
Analytics-Based Community Project. Terms offered: Summer 2025 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. | ||
| MRKT 440 | Marketing Analytics. | 3 |
Marketing Analytics. Terms offered: this course is not currently offered. 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. | ||
| MRKT 442 | Customer Analytics. | 3 |
Customer Analytics. Terms offered: this course is not currently offered. 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. | ||
| ORGB 330 | People Analytics. | 3 |
People Analytics. Terms offered: this course is not currently offered. 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. | ||
Or any related undergraduate topics course (with approvals from Business Analytics and the BCom Office).