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• What is Python?
• History and evolution of Python.
• Python’s popularity and use cases.
• Setting up Python (installation).
• Running Python scripts and using the interactive shell.

• Arithmetic Operators(+,-,*,/,//,%,**)
• Comparison / Relational Operators (== ,!=)
• Assignment Operators
• Logical Operators
• Bitwise Operators
• Identity Operators

• Conditional statements (if, elif, else).
• Looping structures (for and while loops).
• Iterating through sequences (lists, strings, dictionaries).
• Range and enumeration.
• Using break and continue.

• Lists: creation, manipulation, and methods.
• Tuples: creation, immutability, and uses.
• Dictionaries: key-value pairs and operations.
• Sets: unique elements and set operations.
• List comprehensions.

• Problem Solving on LeetCode question

• What is a Function?
• Types of Functions
• Function Syntax (Structure)
• Function Calling
• Parameters vs Arguments
• Scope and lifetime of variables.
• Importing modules.
• Creating and using custom modules.

• What is a Module?
• Types of Modules
• Why Use Modules?
• Importing Modules
• What is a Package?
• Why Use Packages?
• Namespace Management
• Popular Python Packages (numpy, pandas, flask, django, requests, matplotlib, etc.)

• Reading from and writing to files.
• Working with different file modes (read, write, append).
• Using context managers with files.
• Handling errors with try and except.
• Raising exceptions.
• Handling multiple exceptions.
• Using the ‘finally’ block.
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• Classes and objects.
• Attributes and methods.
• Inheritance and encapsulation.
• Polymorphism and method overriding.
• Constructors and destructors.

NumPy :  

• Arrays – Fast, memory-efficient numerical data structures that form the foundation of all data science and ML computations.
• Indexing & Slicing – Quickly access and extract specific elements, rows, or columns from an array.
• Reshape & Flatten – Change the dimensions of data (1D ↔ 2D ↔ 3D) to match what a model or function expects.
• Broadcasting – Perform fast mathematical operations between arrays of different shapes without writing explicit loops.   
• Mathematical Operations – Vectorized calculations (sum, mean, dot product) that run significantly faster than plain Python loops.

Pandas :

• Series – A labeled, one-dimensional data structure used to handle a single column of data.
• DataFrame – An Excel-like 2D tabular structure used to store, explore, and manipulate real-world datasets.
• Filtering & Selection – Apply conditions to pull out specific rows or columns (e.g., only high-salary employees).
• Merge, Join – Combine multiple tables/datasets based on a common column, similar to SQL joins.
• GroupBy & Apply Functions – Group data into categories and calculate aggregated insights like average, sum, or count.

• Real-world case studies.
• Job opportunities and career paths.
• Contributing to open-source projects.
• Staying up to date with Python developments.