What is required to learn to be a Data Scientist or ML Engineer?

I will describe it in short and step by step:

1. First, you should choose one programming language Python or R. Python is preferred over R. And then Spend at least 2 weeks (if you are a beginner) and learn the basic and essential concepts, such as data types, functions, OOP (Object-Oriented Programming), file handling, iterators, and generators.
2. Learn about some Python libraries, NumPy, Pandas, Matplotlib, and Seaborn.
3. Learn basic mathematics and statistics, some important topics are matrix, eigenvalue, eigenvectors, calculus, basic geometry, mean, median, mode, variance and Standard Deviation, some statistical distribution, etc.
4. Take some data set from Kaggle and then do analysis the data using Feature Engineering and Exploratory data analysis (EDA) techniques.
5. Learn the process of gathering the data. Also, learn SQL if you did the above.
6. Start with Machine Learning algorithm and then Deep Learning (NLP and CV) as time goes on.
7. Deploy the ML models.

Bonus Tips:

1. Make a good record in Kaggle.
2. Make a GitHub repository to showcase your skill.
3. Make a good profile on LinkedIn.