QBUS1040: Foundations of Business Analytics

Learning Outcomes

• Linear Algebra & Calculus: Built fluency in matrix operations, differentiation, and multivariable calculus to support regression.
• Multiple Regression Models: Formulated and interpreted multivariable regression using linear-algebraic tools.
• Optimisation: Explored core optimisation methods (incl. quadratic programming) for decision-making.
• Python Programming: Wrote Python programs to solve practical business problems using mathematical models.

Takeaways

This course was one of the most challenging units I’ve taken, as it required both solid mathematical skills and strong Python programming. Much of the unit was grounded in linear algebra, with weekly topics covering norms, clustering, linear independence, matrices, least squares methods, and constrained optimization. We also worked extensively with regression models and optimization techniques, applying them to business-related decision-making contexts. Even though my final grade was not high, the process of grappling with the material significantly strengthened my resilience and problem-solving mindset. I learned how to approach business analytics problems from both a mathematical and computational perspective, and the constant use of Python helped me develop practical coding habits. This course showed me the importance of connecting mathematics with business insights, and gave me the confidence to face difficult analytical problems in the future.