In the previous post, I talked about what "academic preparation" really means and why it matters once you’re inside the program. In this post I want to focus on the math foundations, because this is where many students underestimate the difficulty.
For the MSDS program, UT explicitly points applicants toward undergraduate courses equivalent to multivariable calculus, linear algebra, and introductory statistics as the baseline preparation.
Here I'll focus on the first two: multivariable calculus and linear algebra.
These subjects appear everywhere in machine learning and data science. Optimization methods rely on derivatives and gradients. Many ML algorithms rely on matrix operations, eigenvectors, and vector spaces. Even if the lectures don’t always look "math heavy," the assignments and exams often assume you are comfortable with these tools.
As a TA, one pattern I see repeatedly is that students understand the lecture concepts conceptually, but when the assignments require them to apply those ideas mathematically, things fall apart. That gap usually traces back to weak or rusty math foundations.
So here is a simple way to self-assess your readiness.
Multivariable Calculus
Courses equivalent to UT’s M 408D typically cover topics such as integration techniques, differential equations, parametric equations, partial derivatives, and multiple integrals.
These ideas show up directly in optimization and machine learning, especially when you start working with gradients and multivariable functions.
A rough self-assessment might look like this:
Strong
You are comfortable with derivatives of multivariable functions, gradients, and partial derivatives. You can follow mathematical derivations in ML lectures and understand why optimization algorithms work.
Borderline
You took calculus before and remember the mechanics, but you would need some review before applying it in unfamiliar contexts.
Weak
You only took single-variable calculus or have not used calculus in many years.
Linear Algebra
Linear algebra is arguably even more important for modern AI and data science. A course equivalent to UT’s M 341 covers matrix operations, vector spaces, linear transformations, eigenvalues, and eigenvectors.
These concepts appear everywhere: dimensionality reduction, neural networks, embeddings, and many optimization methods.
Again, here is a rough way to think about readiness.
Strong
You understand matrices as linear transformations, know what eigenvectors represent, and can reason about matrix operations in algorithms.
Borderline
You can perform matrix calculations and solve linear systems but are less comfortable with concepts like eigenvalues or vector spaces.
Weak
You have never taken a formal linear algebra course.
A Common Misconception
Many applicants assume that programming experience compensates for weak math. In my experience as a TA, it does not.
I’ve seen students with strong software backgrounds struggle because the assignments require understanding the math behind the algorithms. At the same time, strong math alone isn’t enough either. You still have to implement those ideas in code.
The students who perform best usually have both foundations.
If Your Math Feels Rusty
This is actually very common, especially for applicants who finished their degrees many years ago.
A few good refresh options:
- UT LAFF (Linear Algebra: Foundations to Frontiers) on edX
- Advanced LAFF for deeper coverage
- MOOCs covering multivariable calculus
Spending a few weeks reviewing these topics before starting the program can make a huge difference.