Many people think you don’t need to know maths nowadays as all the modern Python packages abstract the need for it. You will practically never carry out backpropagation by hand or build a decision tree from scratch (although it’s quite fun to try!).
It’s easy to take this for granted and avoid learning any of the background theory behind the algorithms, which is dangerous and I don’t recommend it.
Sure, you can build a neural network with a few lines of code in PyTorch, but what happens when it has weird predictions and you need to debug it? Or what if someone asked you what the confidence interval around your predicted output is from a linear regression model?
These questions and scenarios come up more frequently than you think, and you can answer them by having a solid grasp of the underpinning maths.
I can sympathize that maths can be scary and might not be everyone’s strong suit. However, the maths needed for most data science roles is not master’s or PhD level. It’s typically what you are taught in your later school years and first year of university for most STEM subjects.
STEM = Science, Technology, Engineering, Mathematics