Curriculum
- 6 Sections
- 29 Lessons
- 10 Weeks
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- Python Fundamentals and Best PracticesWeek 1-2:7
- 0.0Understanding advanced data types and structures
- 0.1Exploring functional programming techniques
- 0.2Mastering exception handling and error management
- 0.3Practicing Pythonic coding practices
- 0.4Diving into memory management and performance optimization
- 0.5Examining Python’s execution model
- 0.6Implementing unit testing and test-driven development
- Object-Oriented Programming in DepthWeek 3-4:7
- 0.0Delving into advanced class and instance management
- 0.1Harnessing the power of inheritance and polymorphism10 Minutes0 Questions
- 0.2Exploring metaprogramming and decorators10 Minutes0 Questions
- 0.3Analyzing design patterns in Python10 Minutes0 Questions
- 0.4Utilizing magic methods and operator overloading10 Minutes0 Questions
- 0.5Implementing abstract base classes and interfaces10 Minutes0 Questions
- 0.6Exploring advanced topics in Python metaclasses10 Minutes0 Questions
- Asynchronous Programming and ConcurrencyWeek 5-6:7
- 0.0Grasping asynchronous programming concepts
- 0.1Using asyncio for asynchronous I/O
- 0.2Implementing concurrent futures and ThreadPoolExecutor
- 0.3Exploring asynchronous frameworks like aiohttp, Trio, and Curio
- 0.4Managing concurrent data structures and synchronization
- 0.5Applying advanced coroutine patterns
- 0.6Optimizing performance in asynchronous applications
- Web Development with PythonWeek 7-8:7
- 0.0– Introducing web development in Python
- 0.1– Exploring popular web frameworks: Django and Flask
- 0.2– Building RESTful APIs with Flask-RESTful
- 0.3– Implementing authentication and authorization mechanisms
- 0.4– Handling Websockets with Flask-SocketIO
- 0.5– Deploying strategies and best practices
- 0.6– Optimizing web application performance
- Data Science and Machine Learning with PythonWeek 9-10:7
- 0.0– Introduction to data science libraries: NumPy, Pandas, Matplotlib
- 0.1– Cleaning and preprocessing data
- 0.2– Implementing supervised and unsupervised learning algorithms
- 0.3– Exploring deep learning with TensorFlow and Keras
- 0.4– Evaluating models and tuning hyperparameters
- 0.5– Deploying machine learning models
- 0.6– Project work and case studies in data science and machine learning
- NoteEach week includes a mix of theoretical concepts, hands-on coding exercises, and practical projects to reinforce learning. Assignments, quizzes, and projects will be provided to assess understanding and progress throughout the course. Additionally, students will have access to online resources, forums, and office hours for further support and clarification.0