Kursda nimalarni o'rganasiz.

• Variables
• Data Types
• Operators
• Control Flow
• Functions
• Loops
• Lists
• Dictionaries
• Tuples
• Sets
• Strings
• Modules
• File Handling
• Error Handling
• Comprehensions (List, Dict, Set, Generator)
• Closures
• Scope
• Decorators
• Generators
• Iterators
• Classes
• Inheritance
• Polymorphism
• Encapsulation
• Magic Methods (Dunder Methods)
• Static and Class Methods
• Property Decorators
• Context Managers
• Multithreading
• Multiprocessing
• Asyncio
• Coroutines
• Generators with Asyncio
• Type Annotations
• Dataclasses
• Named Tuples
• Regular Expressions
• JSON Handling
• Pickle Module
• Lambda Functions
• Map, Filter, and Reduce
• Shallow vs Deep Copy
• Mutable vs Immutable Types
• Memory Management
• Garbage Collection
• Virtual Environments
• Package Management (pip, Poetry, Conda)
• Testing (Unit, Integration, End-to-End)
• Mocking
• Profiling and Optimization
• Logging
• Debugging
• Web Frameworks (e.g., Flask, Django, FastAPI)
• Database Interaction (SQLAlchemy, Django ORM)
• Data Structures (Lists, Stacks, Queues, Trees, Graphs)
• Algorithms (Sorting, Searching, Dynamic Programming)
• Big O Notation
• Networking with Sockets
• Web Scraping (e.g., BeautifulSoup, Scrapy)
• Working with APIs (Requests, Async API calls)
• Concurrent Programming
• File Compression (gzip, zipfile)
• Cryptography (e.g., hashlib, cryptography module)
• Advanced Itertools Usage
• Custom Exception Handling
• Internationalization (i18n)
• Design Patterns in Python
• SOLID Principles in Python
• Microservices with Python
• Functional Programming
• Reactive Programming
• Building CLI Tools
• Data Analysis (e.g., Pandas, NumPy)
• Data Visualization (e.g., Matplotlib, Seaborn)
• Machine Learning Basics (e.g., scikit-learn, TensorFlow)
• Deep Learning Basics
• Natural Language Processing
• Image Processing (e.g., OpenCV, PIL)
• Parallel and Distributed Computing
• Working with Graph Data (e.g., NetworkX)