Python Tutorial: From Zero to Python Developer
1. Introduction to Python
1.1 Overview of Python
1.2 History of Python
1.3 Python 2 vs Python 3
1.4 Installing Python and Setting Up Environment
1.5 Running Python Code (REPL, Scripts, and IDEs)
1.6 Introduction to Python Syntax (Whitespace, Indentation, Comments)
2. Basic Data Types and Operations
2.1 Variables and Assignment
2.2 Integers, Floats, and Strings
2.3 Type Conversion
2.4 Arithmetic and Logical Operators
2.5 String Operations
2.6 Input and Output (I/O)
3. Control Flow
3.1 Conditional Statements (if, elif, else)
3.2 Loops: for and while
3.3 Break, Continue, and Pass Statements
3.4 Nested Loops and Conditional Statements
3.5 Exception Handling (try, except, finally)
4. Data Structures
4.1 Lists: Creation, Indexing, Slicing, and Operations
4.2 Tuples: Immutable Sequences
4.3 Sets: Unordered Collections
4.4 Dictionaries: Key-Value Pairs
4.5 List Comprehensions
4.6 Dictionary Comprehensions
5. Functions and Modules
5.1 Defining Functions
5.2 Arguments and Return Values
5.3 Lambda Functions
5.4 Variable Scope (Local, Global, Nonlocal)
5.5 Importing Modules and Standard Library
5.6 Writing and Organizing Modules
5.7 Creating and Using Packages
6. Object-Oriented Programming (OOP)
6.1 Classes and Objects
6.2 Attributes and Methods
6.3 Constructors (__init__)
6.4 Inheritance
6.5 Method Overriding and Super Calls
6.6 Encapsulation and Information Hiding
6.7 Polymorphism and Duck Typing
6.8 Magic Methods (__str__, __repr__, __eq__, etc.)
7. File Handling
7.1 Reading and Writing Text Files
7.2 Working with Binary Files
7.3 File Modes (r, w, a, b)
7.4 Using with for Resource Management
7.5 Handling Exceptions in File Operations
8. Error Handling and Debugging
8.1 Types of Errors (Syntax, Runtime, Logical)
8.2 Debugging Tools and Techniques
8.3 Using the assert Statement
8.4 Exception Hierarchy
8.5 Raising and Handling Exceptions
9. Iterators and Generators
9.1 Introduction to Iterators
9.2 Iterable Objects and the iter() Function
9.3 Custom Iterators
9.4 Generators and the yield Statement
9.5 Generator Expressions
10. Functional Programming Tools
10.1 Map, Filter, and Reduce
10.2 List Comprehensions vs Functional Tools
10.3 Using functools Module
10.4 First-Class Functions and Closures
10.5 Decorators
11. Working with Libraries and APIs
11.1 Using External Libraries (pip, virtualenv)
11.2 Introduction to Python Package Index (PyPI)
11.3 HTTP Requests with requests
11.4 Handling JSON Data
11.5 Web Scraping with BeautifulSoup and Scrapy
12. Advanced Data Structures
12.1 Collections Module (Counter, deque, namedtuple, etc.)
12.2 Heapq and Priority Queues
12.3 Linked Lists
12.4 Binary and Search Trees
12.5 Graphs and Algorithms
13. Concurrency and Parallelism
13.1 Threading in Python
13.2 The threading Module
13.3 Multiprocessing vs Multithreading
13.4 The multiprocessing Module
13.5 Asynchronous Programming with asyncio
13.6 Coroutines and Event Loops
14. Working with Databases
14.1 Installing Docker and Running PostgreSQL in Docker
14.2 Introduction to SQLAlchemy
14.3 Building a CRUD API with SQLAlchemy
14.4 Connecting to MySQL/PostgreSQL Databases
14.5 SQLAlchemy Complex Queries
15. Testing and Debugging
15.1 Unit Testing with unittest
15.2 Writing Test Cases
15.3 Mocking and Patching
15.4 Test-Driven Development (TDD)
15.5 Introduction to pytest
15.6 Coverage and Profiling
16. Web Development with Python
16.1 Introduction to Web Frameworks
16.2 Working with Flask: Routes, Templates, Forms
16.3 Introduction to Django: Models, Views, Templates (MVT)
16.4 Building RESTful APIs with Flask and Django
16.5 WebSockets and Real-Time Applications
17. Data Science and Visualization
17.1 Introduction to NumPy: Arrays and Operations
17.2 Data Manipulation with Pandas
17.3 Data Visualization with Matplotlib
17.4 Introduction to Seaborn for Advanced Visualization
17.5 Working with Jupyter Notebooks
18. Machine Learning and AI Basics
18.1 Introduction to Machine Learning Concepts
18.2 Supervised vs Unsupervised Learning
18.3 Using Scikit-Learn for Basic ML Models
18.4 Deep Learning with TensorFlow and Keras (Basic Overview)
18.5 Natural Language Processing with nltk
19. Advanced Topics
19.1 Metaclasses in Python
19.2 Decorators and Descriptors
19.3 Context Managers (with, __enter__, __exit__)
19.4 Introspection and Reflection
19.5 Memory Management and Garbage Collection
19.6 Performance Optimization and Profiling
20. Best Practices and Advanced Techniques
20.1 Code Readability and PEP 8 Guidelines
20.2 Writing Pythonic Code
20.3 Design Patterns in Python
20.4 Effective Use of Logging
20.5 Version Control with Git in Python Projects
21. Deployment and Distribution
21.1 Creating Executables with pyinstaller
21.2 Packaging and Distributing Python Code
21.3 Using Docker with Python Applications
21.4 CI/CD in Python Projects
This outline covers foundational Python skills through advanced topics, with a focus on academic rigor rather than marketing hype. The topics are logically organized to guide learners from beginner-level programming to expert-level mastery.