Python for AI — Datalumina

Hands-on Python projects and exercises built while completing the Python for AI course by Dave Ebbelaar at Datalumina.

This repo is not just a course log — it's a working portfolio of Python skills applied to real data and AI problems, from fundamentals through to practical development workflows.

---

About the Course

Python for AI is a beginner-to-practitioner course by Dave Ebbelaar, founder of Datalumina. It covers not only Python syntax but the workflow skills that most courses skip: how to structure projects, use modern tooling, debug effectively, and write production-quality code.

• Instructor: Dave Ebbelaar (AI Engineer, Datalumina)
• Duration: 5+ hours · Full course on YouTube
• Handbook: python.datalumina.com
• Focus: Python fundamentals + real-world AI development workflow

---

Course Progress

Module 1 — Introduction

• Course overview & learning approach
• Why Python for AI
• How to follow this course
• AI assistants and learning tools

Module 2 — Getting Started

• Installing Python (macOS / Windows / Linux)
• Setting up VS Code
• Creating projects
• Virtual environments
• Using uv as a package manager
• Running your first script

Module 3 — Python Basics

• Python syntax and indentation
• Variables and assignment
• Operators (arithmetic, comparison, logical)
• Loops — for and while
• String manipulation
• Comments and code formatting

Module 4 — Data Types

• Numbers (int, float)
• Strings (methods, slicing, f-strings)
• Booleans

Module 5 — Data Structures

• Lists
• Dictionaries
• Tuples
• Sets

Module 6 — Control Flow

• if / elif / else
• Nested conditions and guard clauses

Module 7 — Functions

• Defining functions
• Parameters and return values
• Default arguments

Module 8 — Classes & OOP

• Classes and instances
• __init__ and methods
• Inheritance
• When to use classes

Module 9 — Error Handling

• try / except / finally
• Raising exceptions

Module 10 — Libraries & APIs

• Installing packages with uv
• Working with requests
• Parsing JSON responses
• Working with data

Module 11 — Practical Python

• Project structure best practices
• File operations (read, write, parse)
• Organising code into modules
• Working with CSV and JSON
• Building CLI scripts

Module 12 — Tools & Dev Workflow

• Ruff — formatting and linting
• uv — dependency management
• Git fundamentals
• GitHub — push, pull, branching
• Pre-commit hooks
• Writing a good README

Module 13 — Next Steps

• AI agent development path
• Continue learning resources

---

Projects

Sales Analysis

sales-analysis/

A practical data analysis project applying Python to a real sales dataset.

File	Purpose
get_data.py	Data loading and ingestion
analyzer.py	Core analysis logic
helpers.py	Reusable utility functions
hello.py	Entry point / exploration script

Skills demonstrated: file I/O, data parsing, modular code structure, functions, reusable helpers.

---

Tech Stack

Tool	Purpose
Python 3.12+	Core language
uv	Package and environment management
Ruff	Linting and code formatting
VS Code	Editor with Python extension
Git + GitHub	Version control

---

Setup

This project uses uv for fast, reproducible environment management.

# 1. Clone the repo
git clone https://github.com/anihc1978/python-for-ai.git
cd python-for-ai

# 2. Install uv (if you don't have it)
curl -LsSf https://astral.sh/uv/install.sh | sh

# 3. Create virtual environment
uv venv
source .venv/bin/activate  # macOS/Linux
# .venv\Scripts\activate   # Windows

# 4. Install dependencies
uv sync

# 5. Run a project
python sales-analysis/analyzer.py

---

Code Quality

# Format code
ruff format .

# Lint and check
ruff check .

# Auto-fix issues
ruff check . --fix

---

Resources

• Python for AI Handbook — Datalumina
• Full Course on YouTube
• Dave Ebbelaar on GitHub
• uv Documentation
• Ruff Documentation
• Datalumina Project Template

---

Built in Perth, WA — learning in public.