1. Introduction to Python programming

• What is Python? Overview of the language's history, features, and advantages.
• Setting up the Python environment and IDEs (e.g., Anaconda, PyCharm).
• Introduction to programming concepts, variables, data types, operators, and basic input/output operations.
• Understanding Python's syntax, indentation rules, comments, and keywords. 

2. Python fundamentals

• Data Structures: Deep dive into Python's built-in data structures: Lists, Tuples, Sets, and Dictionaries.
• Control Flow: Conditional statements (if, elif, else) and looping constructs (for, while).
• Functions and Modules: Defining and calling functions, understanding parameters and arguments, working with built-in and user-defined modules, and exploring standard library modules like math.
• Strings and Regular Expressions (Regex): String manipulation, methods, formatting, and using the re module for pattern matching.
• File Handling and Exception Handling: Reading and writing to files, handling different file types, and managing errors and exceptions using try, except, and finally blocks. 

3. Object-oriented programming (OOP) in Python

• Introduction to OOP concepts: classes, objects, attributes, methods, inheritance, polymorphism, and encapsulation.
• Creating classes and objects, using constructors (__init__), and working with class and instance attributes.
• Understanding different types of methods (instance, class, static).
• Implementing inheritance (single, multiple, multilevel), method overriding, and using the super() function. 

4. Advanced Python topics

• Generators and Iterators: Understanding and implementing lazy evaluation and creating custom generators.
• Decorators and Closures: Using decorators to modify function behavior and understanding closures for retaining state.
• Advanced String and List Operations: List comprehensions, dictionary comprehensions, and more.
• Data Serialization: Converting Python objects to other formats (e.g., JSON, Pickle) for storage or transmission.
• Database Connectivity: Connecting Python applications to databases like MySQL and MongoDB.
• Web Scraping: Extracting data from websites using libraries like Beautiful Soup.
• Multithreaded Programming: Introduction to concepts like threads and locks for concurrent programming.
• GUI Programming: Building graphical user interfaces using libraries like Tkinter. 

5. Python for specialized domains (optional, depending on course focus)

• Web Development with Frameworks: Learning to build web applications using frameworks like Django or Flask.
• Data Science and Machine Learning: Using libraries like NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn for data manipulation, analysis, visualization, and machine learning model building.
• Test-Driven Development (TDD) and Debugging: Learning to write robust and testable code using tools like Pytest and techniques for debugging applications.
• Automation and Scripting: Automating tasks and managing system processes using Python scripts.
• API Integration: Accessing and utilizing external APIs for various purposes. 

6. Projects and real-world applications

• Hands-on projects covering various domains like web development, data analysis, automation, and game development to reinforce learning and build a portfolio.
• Case studies and examples of Python's use in different industries like data science, web development, and machine learning.