Python programming for beginners course outline

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Python programming for beginners course outline

This article provides a course outline for my upcoming free course on Python programming for beginners. Python is a super flexible and dynamically evolving imperative language which provides libraries and solutions for all modern software development scenarios, including cloud computing services, machine learning and artificial intelligence.

This comprehensive course covers the basics of Python programming, object-oriented programming, machine learning, and artificial intelligence. It also provides an introduction to advanced programming concepts like creating GUI applications. Practical exercises, projects, and real-world examples can be incorporated throughout the course to enhance understanding and hands-on experience.

Section 1: Introduction to Python

In this section we will provide all fundamental knoledge needed to setup a Python development environment and work with variables, data types, expressions and operators.

  • Lesson 1: Getting Started with Python
    • Installing Python
    • Running Python code using an interpreter
    • Writing and executing your first Python program
  • Lesson 2: Variables and Data Types
    • Variables and assignment
    • Numeric data types (integers, floats)
    • Strings and string manipulation
    • Basic input/output
  • Lesson 3: Operators and Expressions
    • Arithmetic operators
    • Comparison operators
    • Logical operators
    • Operator precedence

Section 2: Control Flow and Functions

In this section we will discuss conditional statements, loops and functions in Python.

  • Lesson 4: Conditional Statements
    • if statements
    • if-else statements
    • nested if-else statements
    • logical operators in conditionals
  • Lesson 5: Loops
    • while loops
    • for loops
    • loop control statements (break, continue)
    • nested loops
  • Lesson 6: Functions
    • Defining and calling functions
    • Function parameters and return values
    • Scope and local variables
    • Recursive functions

Section 3: Object-Oriented Programming (OOP)

In this section we will provide an introduction to OOP basic and advanced concepts and discuss exception handling in your Python code.

  • Lesson 7: Introduction to Object-Oriented Programming
    • Classes and objects
    • Attributes and methods
    • Constructors and destructors
    • Inheritance and polymorphism
  • Lesson 8: Advanced OOP Concepts
    • Encapsulation and information hiding
    • Class inheritance and method overriding
    • Abstract classes and interfaces
    • Working with multiple inheritance
  • Lesson 9: Exception Handling
    • Understanding exceptions and error handling
    • Handling specific exceptions
    • Creating custom exceptions

Section 4: Data Structures and Algorithms

In this section we will discuss lists and tuples, dictionaries and sets, string manipulation and algorithmic complexity analysis.

  • Lesson 10: Lists and Tuples
    • Creating and accessing lists
    • List methods and operations
    • Tuple creation and immutability
    • List comprehensions
  • Lesson 11: Dictionaries and Sets
    • Creating and accessing dictionaries
    • Dictionary methods and operations
    • Creating and accessing sets
    • Set methods and operations
  • Lesson 12: Strings and String Manipulation
    • String methods and operations
    • Formatting strings
    • Regular expressions
  • Lesson 13: Introduction to Algorithms and Complexity Analysis
    • Big O notation
    • Common algorithmic paradigms (searching, sorting, recursion)
    • Analyzing time and space complexity

Section 5: Introduction to Machine Learning and AI

In this section we will discuss the basic concepts of machine learning, NumPy and Pandas, data preprocessing, training evaluating machine learning models, using tensorflow and keras as well as calling pre-trained AI models.

  • Lesson 14: Overview of Machine Learning
  • Lesson 15: Introduction to NumPy and Pandas
    • Installing NumPy and Pandas libraries
    • NumPy arrays and operations
    • Data manipulation with Pandas DataFrames
  • Lesson 16: Preprocessing Data
    • Handling missing data
    • Encoding categorical variables
    • Feature scaling
  • Lesson 17: Training and Evaluating Machine Learning Models
    • Splitting data into training and testing sets
    • Training and testing a machine learning model
    • Model evaluation metrics
  • Lesson 18: Introduction to Artificial Intelligence Models
    • Understanding artificial intelligence and its applications
    • Different types of AI models
    • Introduction to deep learning
  • Lesson 19: Introduction to TensorFlow and Keras
    • Installing TensorFlow and Keras libraries
    • Creating and compiling neural network models
    • Training and evaluating deep learning models
  • Lesson 20: Calling Pretrained AI Models
    • Using pre-trained AI models (e.g., image recognition, natural language processing)
    • Loading and utilizing pretrained models in Python
    • Making predictions with pretrained models

Section 6: Creating GUI Applications

In this section we will teach the basics about creating a GUI application in Python.

  • Lesson 21: Introduction to GUI Programming
    • Basics of graphical user interfaces
    • Introduction to Tkinter library
    • Creating simple GUI applications
  • Lesson 22: Building Interactive GUIs
    • GUI elements and event handling
    • Layout management
    • Creating interactive and responsive GUI applications
  • Lesson 23: Advanced GUI Development
    • Customizing GUI appearance
    • Working with multiple windows and dialogs
    • Integrating GUI with backend functionality

Section 7: Debugging Python Applications

In this section, we will explore common debugging procedures and techniques in Python to help you become more effective at identifying and resolving issues in your programs.

  • Lesson 24: Understanding Errors and Exceptions and using print statements
    • Overview of errors and exceptions in Python
    • Common types of exceptions and their meanings
    • Reading and interpreting error messages
    • Adding print statements to your code for visibility
    • Printing variable values and intermediate results
    • Debugging control flow issues using print statements
  • Lesson 25: Leveraging Debugging Tools and handling exceptions:
    • Introduction to Python debugger (pdb)
    • Setting breakpoints and stepping through code
    • Inspecting variables and stack traces
    • Using try-except blocks to catch and handle exceptions
    • Differentiating between expected and unexpected exceptions
    • Debugging exception-related issues
  • Lesson 26: Logging and Tracing:
    • Utilizing the logging module for debugging purposes
    • Configuring log levels and output formats
    • Tracing program execution using log messages
  • Lesson 27: Using Debuggers and Debugging Common Issues
    • Overview of third-party debuggers (e.g., PyCharm, VS Code)
    • Setting up breakpoints and debugging sessions
    • Examining variables and stepping through code
    • Debugging syntax errors and typos
    • Identifying and fixing logical errors
    • Addressing issues with data structures and algorithms
  • Lesson 28: Effective Debugging Strategies and best practices
    • Isolating the problem area through code commenting or division
    • Reproducing and simplifying the issue
    • Using a systematic approach to troubleshoot
    • Writing modular and readable code for easier debugging
    • Documenting code and adding inline comments
    • Seeking help from online resources and community forums
  • Lesson 29: Testing and Test-Driven Development (TDD):
    • Writing test cases to identify and prevent errors
    • Debugging failures in test cases
    • Incorporating TDD principles in debugging process

Section 8: Documenting Python applications

Documentation plays a crucial role in software development as it improves code readability, facilitates collaboration, and helps others understand and use your code. In this section, we will explore the importance of documentation and learn techniques for effectively documenting Python programs and applications.

  • Lesson 30: The Importance of Documentation and types of documentation.
    • Understanding the benefits of well-documented code
    • Enabling code maintenance and future enhancements
    • Improving code readability and comprehension
    • Overview of different types of documentation
    • API documentation, user documentation, and internal documentation
    • Choosing the appropriate documentation type for your project
  • Lesson 31: Documenting Code Structures and APIs
    • Adding comments to explain code logic and functionality
    • Documenting function signatures, parameters, and return values
    • Writing docstrings to provide detailed explanations
    • Writing clear and comprehensive API documentation
    • Specifying input and output requirements
    • Documenting endpoints, methods, and parameters
  • Lesson 32: Writing Clear and Concise Comments and Docstrings
    • Guidelines for writing effective comments
    • Commenting best practices and conventions
    • Using comments to explain complex or non-obvious code
    • Understanding the purpose and structure of docstrings
    • Formatting docstrings using reStructuredText or Markdown
    • Documenting classes, functions, and modules with docstrings
  • Lesson 33: Documenting User-Facing Features, Project Structure and Dependencies:
    • Creating user guides and tutorials
    • Writing step-by-step instructions for installation and usage
    • Providing examples and sample code snippets
    • Outlining project structure and file organization
    • Documenting requirements and dependencies
    • Explaining how to set up and run the project
  • Lesson 34: Documenting Internal Code:
    • Documenting internal libraries and modules
    • Explaining design decisions and implementation details
    • Providing guidelines for code contributors and maintainers
  • Lesson 35: Using Documentation Generation Tools to create, update and maintain documentation
    • Introduction to documentation generation tools (e.g., Sphinx, MkDocs)
    • Configuring and generating documentation from code and docstrings
    • Customizing documentation styles and layouts
    • Regularly reviewing and updating documentation
    • Incorporating changes and new features in documentation
    • Encouraging community contributions and feedback