Model in Data Science and Machine Learning

Hello All, I want to know which one is good from model’s point of view between data science and machine learning. Yesterday i checked the difference between them from here and As I know as model wise, In data science, the major steps involved to create a model include extraction of data, cleaning of data, using various tools such as Tableau, Micro Strategy, etc. to analyze essential patterns that are involved, making a workable model by using algorithms like supervised learning, unsupervised learning, etc., evaluating and deploying the model but not an idea about machine learning. Can anyone know about what are the useful models in machine learning?

According to my knowledge, data science involves machine learning. Its one of the steps a data science practisioner must fulfill to completely process the data.
Data science in itself is a bigger field with steps from data extraction, to data preprocessing, to analysing and visualizing it, finally to creating a model to find some predictions.
A machine Learning practisioner would’nt necessarily know all feature engineering methods for processing the data. They expect a clean dataset and theoretically, their knowledge base expands on how they can get hold of the best model with the accurate hyperparamters to get the best predictions.