Model in Machine Learning and Data Science

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 this source and As I know as model wise, In the context of data science as a whole, the major steps involved to create a machine learning model includes collecting the data, visualizing the data, using various machine learning models such as Logical Regression, Bayesian Classifiers, Random Forest, etc., training the dataset, evaluating the dataset and parameter tuning but not an idea about data science. Can anyone know about what are the useful models in data science?

I’m not quite sure if I understand the question entirely, but I’ll give you my superficial impression of Data Scientist/Engineer vs. Machine Learning Scientist/Engineer. I would consider the Data Scientist to be more of a curator of data, whereas the ML Scientist is the user of the data. By way of analogy: the data scientist is like a librarian, whereas the ML scientist is like a researcher combing the stacks looking for useful information to be used in a specific application. The data scientist has already cleaned, sorted, and indexed the data in a way that makes it much easier for the ML scientist to both find the data that they need and to make use of that data (i.e. without having to do a lot of preprocessing of the data to get it into a usable form).