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Python Course

Step into the dynamic world of Python programming by learning the fundamentals, data structures, and various libraries. This course provides both foundational and advanced insights into the world of Python.

This course provides both foundational and advanced insights into the world of data. Whether you’re a beginner or have some experience, you’ll gain valuable knowledge and practical skills.

> Step 1: Practical Projects

2 live projects and 10+ demo projects to build your real-world experience.

> Step 2: Mock Interviews & Placement Support

Guidance on preparing for Python programming interviews and building a strong portfolio.

> Step 3: Trainer Expertise

Our trainers are experienced Python developers with industry expertise, providing real-world insights and applications.

  • 70 Hours Practical
  • 50 Hours Practical
  • 120 Total Hours

Step into the dynamic world of Python programming by learning the fundamentals, data structures, and various libraries.

  • Online Training
  • Offline Training

Course Syllabus

Downlaod Syllabus PDF

The Python course syllabus is designed to provide you with a strong foundation in both theoretical and practical aspects of data science.

  • Introduction to Python
  • Python Basics
  • Control Flow
  • Functions and Modules
  • Error Handling
  • Data Structures in Python
  • Working with Strings
  • File Handling
  • Algorithms

OOP Basics:

  • Classes and Objects
  • Attributes and Methods
  • Inheritance
  • Encapsulation
  • Polymorphism
  • Aggregation Functions

Advanced OOPs Concepts:

  • Abstract Classes and Interfaces
  • Method Overriding
  • Magic Methods and Operator Overloading
  • Decorators and Property Methods
  • Python Standard Library
  • Popular Python Libraries
  • Web Development with Python

Data Persistence:

  • Introduction to SQL
  • Connecting to Databases with Python (SQLite, MySQL, PostgreSQL)
  • CRUD Operations
  • ORM with SQL Alchemy

Working with Databases:

  • File-based Persistence
  • NoSQL Databases (MongoDB)

Concurrency and Parallelism:

  • Threading
  • Multiprocessing
  • Asyncio for Asynchronous Programming

Testing in Python:

  • Unit Testing with `unittest`
  • Test-Driven Development (TDD)
  • Mocking and Patching

Working with APIs:

  • Consuming APIs with `requests`
  • Creating APIs with Flask/Django
  • Handling Authentication (OAuth2, JWT)

Deployment:

  • Packaging Python Applications
  • Deployment Strategies (Docker, Heroku)
  • Continuous Integration/Continuous Deployment (CI/CD)