Learning Pathways
01
Python Foundations
Start here if you're new to Python or want to solidify the basics.
- Variables, Types & Operators
- Control Flow — Loops & Conditionals
- Functions & Scope
- Data Structures — Lists, Dicts, Sets & Tuples
- File Handling & Exceptions
- Code-Along — Fundamentals Project
02
Object-Oriented Programming
Build structured, reusable, and maintainable code.
- Introduction & Core Concepts
- Classes & Objects
- Inheritance & Polymorphism
- Encapsulation, Abstraction & Properties
- Magic Methods & Dunder Methods
- Design Patterns in Python
- Code-Along — Bank Account Project
03
Data Analysis
Work with data like a pro using NumPy, Pandas, and visualisation tools.
- NumPy Fundamentals
- Pandas Essentials
- Data Cleaning & Preprocessing
- Exploratory Data Analysis (EDA)
- Visualisation with Matplotlib & Seaborn
- Statistical Analysis in Python
- Code-Along — Full EDA Project
04
Machine Learning
Build, train, and evaluate predictive models.
- Introduction & Core Concepts
- Supervised Learning
- Unsupervised Learning
- Model Evaluation & Metrics
- Feature Engineering
- Scikit-Learn Reference Guide
- Deep Learning with PyTorch
- Code-Along — End-to-End ML Pipeline
05
AI in Python
Leverage large language models, APIs, and agent frameworks.
- Large Language Models & How They Work
- OpenAI & Anthropic API
- Hugging Face Transformers
- LangChain & Agent Frameworks
- Vector Databases & RAG
- Prompt Engineering
- Code-Along — Build an AI Application
06
Statistics & Experimentation
Master A/B testing, hypothesis testing, and probability simulations.
- A/B Testing — Frequentist Methods
- A/B Testing — Bayesian Methods
- A/B Testing — Best Practices & Pitfalls
- Monte Carlo Simulations in Python
Reference & Best Practices
Python Best Practices & Style Guide
PEP 8, clean code, tools (black, flake8, mypy)
OOP Best Practices
SOLID principles, patterns, anti-patterns, worked examples
Data Analysis Best Practices
Clean pipelines, reproducibility, code review checklist
ML Best Practices
Preventing leakage, evaluation, deployment
AI Best Practices
Prompt design, safety, cost management
Study Guide & Cheatsheet
Quick reference for syntax & 25 practice questions
Connected Topics
→
Bayesian A/B Testing — applies Bayesian Methods & Model Evaluation & Metrics
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Monte Carlo Simulations — sports simulations, uses Frequentist Methods concepts
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K-Means › Casemiro Replacement — real project using Unsupervised Learning
Progress Tracker