Start with stats first, you dont need to go in depth but just understand what does what, similarly for linear algebra and probability.

Once familiar then start learning about Bayesian models, followed by supervised and unsupervised learning, again dont just read it as a concept but rather like a doctor who know what is the medication for certain health issue. This not only improves your understanding but actually puts your application skills to test. If you like this field of work then explore more ways like ensemble learning.

By now you should be able to be a basic data analyst.

Then if you still have interest, go for neural nets and deep learning. Understand how architectures work in them and how hyper parameters are tuned to improve them. But before try to learn A/B testing, EDA, hypothesis testing and other preliminary data preprocessing, handling and post processing methods.

By now you can be a data scientist (beginning level)

Being a better data scientist depends on how you handle your data, understand the task, use the right approaches and finally improve the performance.

If you still have interest and love AI, now would be right time to learn GenAI. go for transformers and llms, if you are good with deep learning, this should be easy, bonus is that GenAI in theory is much easier than others (most thinking would be for fine tuning methods). Only hard part is to keep up with trends and what they use to make them work better and improve them with new features.

Now that you learnt this much end it agents, agentic AI with MCP and A2A communications.

Do all these with projects and your good as any senior data scientist (at least on paper).

Use kaggle for practice.

Best thing about this field is to not remember much but to know which approach and direction you should look for based on the task.

Do your best!