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.
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
Small data-oriented programs that reuse clear names and grow from single values into complete workflows.
None. No programming experience is assumed.
Open any module to review its exact sections. Progress and completion follow you through the course.
Topics include Why Python for Data Science, Variables & Assignment, Data Types (int, float, str, bool), and more.
Topics include Boolean Logic & Comparisons, if/elif/else Statements, for Loops, and more.
Topics include Lists - Ordered Collections, Tuples - Immutable Sequences, Dictionaries - Key-Value Pairs, and more.
Topics include Defining Functions, Parameters & Arguments, Return Values, and more.
Topics include Exceptions & Error Types, try/except/finally, Raising Exceptions, and more.
Topics include Classes & Objects, __init__ & self, Attributes & Methods, and more.
Complete programming beginners
Analysts adding Python to their toolkit
AI-assisted coders who want to validate generated code
These are independent LDS courses that extend the closest skills. Choose the direction that matches what you want to build next.
Turn messy tables into trustworthy analysis.
Build statistical intuition before formulas.
Train models the way working data scientists do.
The first module establishes the language and example used throughout the rest of the course.
