MATH1062: Mathematics 1B

Learning Outcomes

• Mathematical & Statistical Thinking: Solved real-world problems using logic, inference methods, and statistical modeling techniques.
• Data Analysis with R: Summarised, visualised, and interpreted data; applied appropriate inference methods for different data types.
• Communication & Reasoning: Presented mathematical and statistical arguments clearly in written form.
• Multivariable Calculus: Modelled functions of two variables as 3D surfaces; computed partial derivatives, gradients, and directional derivatives.
• Differential Equations: Constructed and solved differential equation models; interpreted solutions in the original application context.

Takeaways

This course helped me bridge the gap between abstract mathematics and applied data science. On the one hand, I gained rigorous training in multivariable calculus and differential equations: modeling functions of several variables, computing gradients, and interpreting dynamic systems through partial derivatives and ODE/PDE models. On the other hand, using R for data analysis allowed me to connect these mathematical tools with real-world data, from visualization to statistical inference. The fast pace of the course pushed me to strengthen both my mathematical reasoning and coding fluency. At first, it was challenging to shift between formal proofs, computational techniques, and applied modeling, but I gradually developed a clearer sense of how these pieces fit together. By the end, I could approach complex problems from multiple perspectives: using geometry and equations to describe dynamics, and using R to test, interpret, and communicate the results. Most importantly, this course taught me how to think flexibly—moving between theory and practice, math and code. It gave me the confidence that I can not only solve equations on paper, but also apply them to messy, real-world contexts with reproducible workflows.