Welcome to the ED — Data Structures course reference. This site documents the Python implementations, concepts, and exercises from the University of Caldas (UCALDAS) 4th-semester Data Structures course. All code is available as interactive Jupyter Notebooks runnable directly in Google Colab.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/tutosrive/ed/llms.txt
Use this file to discover all available pages before exploring further.
Introduction
Course overview, learning objectives, and how the repository is organized.
Setup
Get your environment ready and open notebooks in Google Colab or locally.
Recursion
Understand stack and tail recursion through binary search, factorial, and Fibonacci.
Binary Search Trees
Build, search, insert, and delete nodes in a BST with full traversal methods.
Graphs
Model weighted directed graphs using adjacency lists and visualize them with NetworkX.
Workshops & Homeworks
Hands-on exercises combining all the data structures covered in the course.
What You’ll Learn
Master Recursion
Implement recursive algorithms using both stack (LIFO) and tail (FIFO) strategies. Analyze memory differences with factorial and Fibonacci examples.
Build Binary Search Trees
Implement a BST from scratch in Python — including insert, search, traversal, and all three node deletion cases (leaf, one child, two children).
Work with Graphs
Construct directed weighted graphs using adjacency lists, traverse them, and render visual diagrams with NetworkX and Matplotlib.
All notebooks in this course are designed to run in Google Colab — no local Python setup required. Click any “Open in Colab” badge to start immediately.