In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
Many a time, I have seen beginners in data science skip exploratory data analysis (EDA) and jump straight into building a hypothesis function or model. In my opinion, this should not be the case.
Python For Data Analysis - Build a Data Analysis Library from Scratch - Learn Python in 2019
Learn about numpy, pandas, scikit-learn, pandas DataFrame and build some statistical models with this Data Analysis with Python course.
Python for Data Science, you will be working on an end-to-end case study to understand different stages in the data science life cycle. This will mostly deal with "data manipulation" with pandas and "data visualization" with seaborn. After this, an ML model will be built on the dataset to get predictions. You will learn about the basics of the sci-kit-learn library to implement the machine learning algorithm.