My name is Charlie Yang, and I am an M.S. in Commerce graduate student in the Business Analytics Track. I attended the University of Toronto for undergrad and studied Economics. I chose to study business analytics because it plays a crucial role in how companies operate and conduct business today. Business analytics helps companies to connect pieces of data together to form a narrative and create solutions for complex business challenges.
This post focuses on my experience in the Business Analytics Track, but you should know how it fits into the entire academic plan. The M.S. in Commerce is broken into three parts:
- Integrated Core Experience, in which we take a deep dive into all things business (strategy, cost management, marketing and analytics, organizational behavior, finance, etc.).
- Track-specific courses, where we take classes focused on our chosen track (Business Analytics, Finance, or Marketing & Management).
- Global Immersion Experience, during which we study international business. GIE culminates in a three-week international experience that allows us to learn about business and apply our knowledge in real time.
Business Analytics Track Classes
The spring term in the Business Analytics Track includes five track-specific courses, one elective, and a global foundations course designed to prepare us for the Global Immersion Experience. For those interested in the Business Analytics Track, these classes might interest you:
Big Data—This course introduces the solutions and architectures available when traditional storage technologies don’t handle the amount of data companies need to store. It allows students to gain competence in practical databases and database management solutions, including Hadoop, Cassandra, and Microsoft Azure.
Advanced Quantitative Analytics—This course introduces us to a series of techniques in the arena of regression analysis to allow for a better understanding about how to describe and model data. We use both R and SPSS to explore and master these concepts. Additional topics in this course include logistics regression, curvilinear regression, factor analysis, and path analysis.
Web Analytics for eCommerce—This course introduces topics in both web analytics and social analytics. We not only learn theories and methodologies in these analytics areas, but also apply techniques that help businesses improve their performance. We also learn to create data visualizations in Tableau and Google Analytics.
Text Analytics—Text Analytics, as the name suggests, helps students to understand the intricacies of extracting valuable information and deriving insights from texts. This course covers topics including frequency analysis, sentiment analysis, and natural language processing.
Customer Analytics—In this course, we learn to assign values to customers using analytics. We use R to perform data wrangling, data visualization, and data analysis. These skills enable us to formulate insights that enhance how companies make marketing-mix decisions.
Foundations of Global Commerce—Students in each track in the Program take this course as a prerequisite for our three-week stint abroad for the Global Immersion Experience. This class is mainly discussion-based around several readings each day, making it more of a seminar than a lecture. We discuss the “business world” as a complex and dynamic system, learning about the history of globalization, the current world order and how different countries fit into it, and the ever-changing future of global commerce.
Emerging Topics of Commerce: AI and Machine Learning—This course expands upon machine learning methodologies. We analyze case studies written by McIntire’s Center for Business Analytics, which focus on the classification problems in machine learning and introduce basic concepts of data governance and different neural networks.
Advanced Quantitative Analysis Project—This year in our Advanced Quantitative Analysis course, we are fortunate to work with a McIntire corporate partner. They asked us to use real-time data to analyze challenges they are facing right now. My team is using advanced statistical analysis techniques like factor analysis, logistic regression, and structural equation modeling to derive insights. We are also doing extensive independent research to come up with recommendations, which we will present to the company at the end of the project.