Python

  1. Why Python

    Why Python is the language of choice for statistics, data analysis, and machine learning in medicine. A methodological introduction for healthcare professionals.

  2. Python & VS Code Setup (From Zero to a Professional Environment)

    Learn how to install Python and configure Visual Studio Code correctly. This foundational lesson prepares you for advanced Python applications in data analysis, statistics, and machine learning.## Lesson ObjectivesBy the end of this lesson, you will be able to:- Install Python correctly on your system- Configure Visual Studio Code as a professional Python IDE- Run Python scripts from the terminal- Understand how this setup scales to data analysis and machine learningThis lesson is **foundational**.A poor environment setup will compromise everything that follows.

  3. Variables, Naming Rules, and Basic Syntax

    Learn Python variables, naming rules, and basic syntax. This lesson builds the foundation required for data analysis, statistics, and machine learning workflows in Python.

  4. Core Data Types in Python

    Learn Python core data types (int, float, str, bool) and type conversion. This lesson explains why data types matter for numerical computation, statistics, and data analysis in Python.

  5. Control Flow: Conditions and Loops

    Learn Python control flow with if/else statements and loops (for, while). This lesson introduces algorithmic thinking essential for statistics, data analysis, and machine learning in Python.

  6. Functions and Code Reusability

    Learn Python functions: parameters, arguments, return values and scope, and how to reuse code cleanly in medical data analysis scripts.

  7. Collections: Lists, Tuples, Sets, and Dictionaries

    Learn Python collections: lists, tuples, sets, and dictionaries. This lesson explains how Python stores and organizes data, a core concept for data analysis, statistics, and machine learning.

  8. Modules, Packages, and File Handling

    Learn how to use modules and packages in Python and how to read and write files. This lesson introduces modular code organization and file handling, essential for data analysis and machine learning workflows.

  9. Errors, Exceptions, and Robust Code

    Learn how Python handles errors and exceptions. This lesson explains runtime errors, try/except blocks, and best practices for writing robust, fault-tolerant code in data analysis and machine learning.

  10. Object-Oriented Programming (OOP) in Python

    Learn Object-Oriented Programming in Python: classes, objects, attributes, and methods. This lesson explains how OOP improves structure, reuse, and clarity in data analysis and machine learning code.

  11. Intermediate Python: Writing Clean, Pythonic Code

    Learn intermediate Python concepts: list comprehensions, lambda functions, *args and **kwargs, and Pythonic style. This lesson completes the transition from beginner to intermediate Python for data analysis and machine learning.

  12. Python Wrap-Up Lesson: A Mini Cardiology Risk-Factor Audit (Step-by-Step)

    A fully guided Python walkthrough: load a small outpatient cardiology dataset, clean it, compute risk-factor prevalence, stratify by age and sex, and produce a simple report—linking each step to earlier lessons.