Module 1: Introduction to Python Programming

• Basics of Python syntax
• Variables, data types, and operators
• Control flow statements (if, loops)
• Functions and modules
• Input/output operations

Module 2: Data Structures and Algorithms

• Lists, tuples, sets, and dictionaries
• String manipulation
• File handling
• Recursion
• Sorting and searching algorithms

Module 3: Object-Oriented Programming (OOP)

• OOP concepts (classes, objects, inheritance, polymorphism)
• Encapsulation, abstraction, and data hiding
• Exception handling
• Testing and debugging

Module 4: Web Development with Python

• HTML and CSS basics
• Introduction to web frameworks (e.g., Flask, Django)
• Creating dynamic web applications
• Database integration (e.g., SQLite, MySQL)

Module 5: Database Programming

• Introduction to SQL
• Database concepts (tables, queries, joins)
• Using Python to interact with databases
• Database connectivity and management (e.g., SQLite, PostgreSQL)

Module 6: GUI Programming

• Introduction to GUI frameworks (e.g., Tkinter, PyQt)
• Building graphical user interfaces
• Event-driven programming
• Creating interactive applications

Module 7: Data Analysis and Visualization

• Introduction to data analysis libraries (e.g., Pandas, NumPy)
• Manipulating and analyzing data
• Data visualization using libraries like Matplotlib, Seaborn

Module 8: Web Scraping and Automation

• Basics of web scraping
• Using libraries like

• Using libraries like Beautiful Soup and Scrapy for web scraping
• Handling APIs and JSON data
• Automating tasks with Python (e.g., web automation, file manipulation)

Module 9: Machine Learning and Data Science

• Introduction to machine learning concepts
• Supervised and unsupervised learning algorithms
• Using libraries like scikit-learn for machine learning tasks
• Data preprocessing and feature engineering
• Model evaluation and selection

Module 10: Deployment and Cloud Computing

• Introduction to cloud platforms (e.g., AWS, Azure)

• Deploying Python applications to cloud platforms
• Containerization using Docker
• Introduction to serverless computing
• Managing cloud resources and scaling applications

Module 11: Web APIs and Microservices

• Building RESTful APIs using frameworks like Flask or Django
• Authentication and authorization for APIs
• Microservices architecture and design principles
• Service discovery and orchestration

Module 12: Testing and Quality Assurance

• Unit testing and test-driven development (TDD)
• Test frameworks like pytest
• Code quality and linting
• Continuous integration and continuous deployment (CI/CD) pipelines

Module 13: Security and Best Practices

• Common security vulnerabilities in Python applications
• Handling user input securely
• Secure coding practices and defensive programming
• Logging and error handling for robust applications

Module 14: Advanced Topics and Specializations

• Advanced Python features and libraries (e.g., generators, decorators)
• Specializations such as data engineering, natural language processing, or computer vision
• Exploring emerging trends in Python development

Module 15: Final Project

• Developing a comprehensive Python project that encompasses the concepts learned throughout the course
• Planning, designing, and implementing the project from scratch
• Testing, debugging, and documenting the project

Module 16: Natural Language Processing (NLP)

• Introduction to NLP and its applications
• Text preprocessing and cleaning
• Tokenization, stemming, and lemmatization
• Sentiment analysis and text classification
• Named entity recognition and part-of-speech tagging

Module 17: Big Data Processing

• Introduction to big data concepts and challenges
• Working with distributed computing frameworks (e.g., Apache Spark)
• Processing large datasets using PySpark
• Data streaming and real-time processing

Module 18: Deep Learning and Neural Networks

• Introduction to neural networks and deep learning
• Building neural networks using libraries like TensorFlow or PyTorch
• Convolutional Neural Networks (CNNs) for image recognition
• Recurrent Neural Networks (RNNs) for sequence data
• Transfer learning and model fine-tuning

Module 19: DevOps and Infrastructure as Code

• Introduction to DevOps principles and practices
• Configuration management tools (e.g., Ansible)
• Infrastructure provisioning with tools like Terraform
• Continuous Integration/Continuous Deployment (CI/CD) pipelines
• Monitoring and logging in a production environment

Module 20: Advanced Web Development

• Websockets and real-time communication
• REST API authentication and authorization mechanisms
• Building scalable and high-performance web applications
• Performance optimization techniques
• Integration with external services and APIs