PYTHON COURSE
Python Programming Fundamentals
Introduction to Python and its features
Setting up the Python development environment
Basic syntax, data types, and variables
Control flow statements (if-else, loops)
Data structures (lists, tuples, dictionaries)
Functions and modules
Exception handling
Advanced Python Programming
Advanced data structures (sets, frozensets, collections)
File handling and I/O operations
Decorators and closures
Generators and iterators
Context managers
Regular expressions (re module)
Functional programming concepts
Object-Oriented Programming (OOP) with Python
OOP principles (classes, objects, inheritance, polymorphism)
Encapsulation and abstraction
Class methods, static methods, and instance methods
Special methods (dunder methods)
Inheritance and method overriding
Design patterns in Python (Factory, Singleton, Observer)
Python for Data Science
Introduction to data science and Python libraries (NumPy, Pandas)
Data manipulation and analysis with Pandas
Data visualization with Matplotlib and Seaborn
Exploratory data analysis (EDA)
Statistical analysis and hypothesis testing
Introduction to machine learning with scikit-learn
Web Development with Python
Introduction to web development concepts (HTML, CSS, JavaScript)
Flask or Django framework for web development
Routing, templates, and request handling
Database integration (SQLite, MySQL, PostgreSQL)
User authentication and authorization
RESTful APIs development
Deployment of Python web applications
Python for Automation and Scripting
Automating tasks with Python scripts
Working with files and directories
Regular expressions for text processing
Interacting with the operating system (OS module)
Handling CSV, JSON, and XML data
GUI automation with libraries like PyAutoGUI
Web scraping with BeautifulSoup or Scrapy
Data Analysis and Visualization with Python
Data cleaning and preprocessing
Exploratory data analysis (EDA) techniques
Data visualization libraries (Matplotlib, Seaborn, Plotly)
Statistical analysis and hypothesis testing
Time series analysis and forecasting
Geospatial data analysis (GeoPandas, Folium)
Interactive dashboards with Dash or Streamlit
Python for Machine Learning and Deep Learning
Introduction to machine learning concepts
Supervised learning algorithms (regression, classification)
Unsupervised learning algorithms (clustering, dimensionality reduction)
Model evaluation and hyperparameter tuning
Introduction to neural networks and deep learning
Deep learning frameworks (TensorFlow, Keras, PyTorch)
Natural language processing (NLP) with Python
Python Testing and Debugging
Unit testing with unittest or pytest
Test-driven development (TDD) principles
Mocking and patching for testing external dependencies
Debugging techniques and tools (pdb, PyCharm debugger)
Code coverage analysis
Continuous integration (CI) and automated testing pipelines
Python for IoT and Raspberry Pi
Introduction to IoT (Internet of Things)
Interfacing sensors and actuators with Raspberry Pi
GPIO programming with Python
IoT protocols (MQTT, CoAP)
Building IoT applications and projects
Data logging and visualization
IoT security considerations
Python GUI Development
GUI programming with Tkinter
Event-driven programming
Creating GUI applications (forms, buttons, menus)
Layout management
Handling user input and events
Packaging and distributing GUI applications
Python for Game Development
Introduction to game development concepts
Pygame framework for 2D game development
Game loops and event handling
Sprites and animations
Collision detection and game physics
Game design patterns
Publishing and distributing games
Python for Robotics
Robotics concepts and applications
Interfacing with robot hardware (sensors, motors)
Robot control algorithms
Robot simulation with libraries like PyBullet or Gazebo
ROS (Robot Operating System) integration
Building and programming robots with Python
Python Security and Ethical Hacking
Introduction to cybersecurity and ethical hacking
Python libraries for security testing (Scapy, Requests, etc.)
Network scanning and reconnaissance
Exploitation techniques and vulnerability assessment
Web application security testing
Incident response and forensic analysis
Python tools for cybersecurity professionals
Python Best Practices and Software Engineering
Code quality standards and best practices
Version control systems (Git) and collaboration tools (GitHub)
Code documentation (docstrings, Sphinx)
Code reviews and refactoring techniques
Software development life cycle (SDLC) methodologies
Agile practices and project management tools
Building scalable and maintainable Python applications
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