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Introduction to Python for Data Analysis❓


Basics of Python programming language.
Overview of key data structures: lists,
dictionaries, tuples, and sets.

How to Learn a Python Programming Language

for Data Analysis or Data Science??


1. Understanding the Basic👍

Introduction📍 What is Python and why Python......?

Know why you're learning Python and what you can do with it.

Setup & Installation📍
Install Python and a suitable IDE (e.g., Jupiter Notepad, VS Code).

Basics📍
Variables, data types, input-output, and basic operations.

Control Structures📍
Loops (`for`, `while`) and conditionals (`if`, `elif`, `else`).

2. Diving Deeper👍

Functions📍
Define functions, arguments, return values, and lambda functions.

Data Structures📍

Lists, tuples, sets, dictionaries, and their operations.

File Handling📍
Read, write, and manipulate files.

Modules and Packages📍
Understand the importance of modules, learn how to import them, and create your own.

3. Object-Oriented Programming (OOP)👍

Classes & Objects📍
Understand how classes and objects work, and how to create them and print properties or method value.

Inheritance📍
Learn about parent and child classes to how class inheritance

Polymorphism & Encapsulation📍
Learn about the concepts and understand their Applications and how to method overloading and overriding works.

4. Intermediate Concepts👍

Errors & Exception Handling📍
`try`, `except`, `finally`, and creating custom exceptions.

Iterators & Generators📍
Understand the difference between them and their applications.

List Comprehensions📍
Create more concise and readable lists.


5. Libraries and Frameworks👍

Data Analysis**: Pandas, Numpy, Matplotlib, seaborn, scipy & plotly

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