Learn to read, reason about, and write Python.
Start with values and control flow, then build confidence with data structures, functions, error handling, files, and object-oriented programming.
What you will leave with
You finish able to read unfamiliar Python, break a problem into functions, and write small programs that handle real input safely.
What you will be able to do
The curriculum is organized around these 4 practical outcomes.
Trace variables, expressions, conditions, and loops
Choose the right built-in data structure
Write reusable functions with clear inputs and outputs
Handle errors, files, and objects without losing the program flow
Course map
Curriculum
Open a module to inspect every section before you start. Your progress follows you through the course.
Why Python for Data Science, Variables & Assignment, Data Types (int, float, str, bool), and more.
Boolean Logic & Comparisons, if/elif/else Statements, for Loops, and more.
Lists - Ordered Collections, Tuples - Immutable Sequences, Dictionaries - Key-Value Pairs, and more.
Defining Functions, Parameters & Arguments, Return Values, and more.
Exceptions & Error Types, try/except/finally, Raising Exceptions, and more.
Classes & Objects, __init__ & self, Attributes & Methods, and more.
Who this course is for
Start with the background you have. The prerequisite notes above tell you exactly what is assumed.
Complete programming beginners
Analysts adding Python to their toolkit
AI-assisted coders who want to validate generated code
Continue learning
Each course stands on its own. Pick the one closest to the next task you need to handle.
Turn messy tables into trustworthy analysis.
Build statistical intuition before formulas.
Train models the way working data scientists do.
Start the course
Module 1 introduces the language and example used throughout the rest of the course.
