Python & AI Training: Streamline Your Code with AI Assistants
Module 1 – Introduction to Python and AI Assistants
Overview of the language and its scientific applications
Installing Python and the code editor (VS Code or Jupyter recommended for AI integration)
Introduction to AI Coding Tools: Setting up an assistant (e.g., Gemini, Copilot) and core prompting principles for developers
First steps with the Python console, scripts, and generating your initial code using AI
Module 2 – Programming Fundamentals
Variables, data types, and type conversion
Strings, numbers, and booleans
Arithmetic and logical operations
AI Practice: Using the assistant to generate examples of data type manipulation and understanding conversion errors (TypeError)
Module 3 – Control Structures and Algorithmic Logic
Conditions (if, elif, else)
Loops (for, while)
Indentation principles and best practices
AI Practice: Asking the AI to translate a textual algorithm into Python control structures, and using the assistant to catch indentation errors (IndentationError)
Module 4 – Functions, Modularity, and Documentation
Creating and calling a function (passing parameters and returning values)
Structuring code into logical blocks
AI Practice:
Writing prompts to generate reusable functions
Using AI to automatically generate comments and documentation (docstrings) compliant with PEP 8 standards
Module 5 – Introduction to Object-Oriented Programming (Optional)
Understanding the concept of objects in Python
Creating a class and instantiating simple objects
AI Practice: Using the assistant to model simple object relationships and generate the structural code for a class
Module 6 – File Manipulation and Data Handling
Reading and writing text files (TXT, CSV)
Parsing and processing data line by line
AI Practice: Asking the assistant to generate quick data cleaning or formatting scripts based on a provided file sample
Module 7 – AI-Augmented Practical Project
Creating a complete project combining variables, loops, functions, and file manipulation
Prompt Engineering for Debugging: Learning how to submit error codes to the AI to get a diagnosis and refactoring suggestions
Corrections, AI-driven automated code reviews, and personalized instructor feedback