#Module 2 : Python for Data Science Introduction
Section 2 is divided into sub sections

👉 2.1 Python, Anaconda and relevant packages installations

👉 2.2 Why learn Python?

👉 2.3 Keywords and identifiers

👉 2.4 comments, indentation and statements

👉 2.5 Variables and data types in Python

👉 2.6 Standard Input and Output

👉 2.7 Operators

👉 2.8 Control flow: if else

👉 2.9 Control flow: while loop

👉 2.10 Control flow: for loop

👉 2.11 Control flow: break and continue

2.1 Python, Anaconda and relevant packages installations

☝️
Go with python 3.7 version - python ( link ) and if you face any issue better to install 3.6 version and it works absolutely fine...

Note: - Please don't go with 3.8 , because it is unstable with tensorflow ( link ) .
Click on the links provided.

2.2 Why learn Python?

2.3 Keywords and identifiers

2.4 comments, indentation and statements

2.5 Variables and data types in Python

2.6 Standard Input and Output

2.7 Operators

2.8 Control flow: if else

2.9 Control flow: while loop

#Module 3 : Python for Data Science: Data Structures
Section 3 is divided into sub sections

👉 3.1 Lists

👉 3.2 Tuples part 1

👉 3.3 Tuples part 2

👉 3.4 Sets

👉 3.5 Dictionary

👉 3.6 Strings

3.1 Lists

3.2 Tuples part 1

3.3 Tuples part 2

3.4 Sets

3.5 Dictionary

3.6 Strings

#Module 4 : Python for data science: Functions
Section 4 is divided into sub sections
👉 4.1 Introduction

👉 4.2 Types of functions

👉 4.3 Function arguments

👉 4.4 Recursive functions

👉 4.5 Lambda functions

👉 4.6 Modules

👉 4.7 Packages

👉 4.8 File Handling

👉 4.9 Exception Handling

👉 4.10 Debugging Python