Data science, as a whole, or even machine learning requires intricate knowledge of mathematics, especially algebra as most of the machine learning algorithms are based out of linear algebra. A few of the topics which are a must for all students aiming to pursue a career in data science are matrix theory, graph theory, optimization algorithms, multivariate calculus, and a detailed study of linear algebra.

You need basic background of almost all of the engineering mathematics, except for differential equations (for traditional machine learning).

Broadly speaking, following are the math topics you need to be familiar with for learning data science:

- Linear algebra
- Probability theory
- Multivariable Calculus
- Multivariate Statistics
- Optimization: Linear programming and convex optimization

Once you understand the key concepts in these subjects, I guarantee that you can then comprehend almost any research papers or tools related to machine learning. If you want to be a good machine learning researcher or engineer, there is no escape from mathematics. If you want jobs where people just implement ML methods, then you can probably escape but only until you encounter a dataset for which traditional methods fails. Then, nothing but good understanding of mathematics can save you.