Learning Data Structures: From First Principles - Expanded Project Guides

Generated from: DATA_STRUCTURES_FROM_FIRST_PRINCIPLES.md

This folder contains deep-dive guides for each project in the learning path.

Overview

This learning path builds data structures from memory-first principles, then layers on ADTs, fast lookup structures, and graph algorithms before culminating in a mini database engine. Each project is designed to show how memory layout, pointer logic, and algorithmic trade-offs shape real-world performance.

Project Index

# Project Difficulty Time Key Focus
1 Memory Arena & Dynamic Array Intermediate 1 week Contiguous memory, amortized growth
2 Linked List Toolkit Intermediate 1 week Pointer-based structures, insertion trade-offs
3 Stack Machine Intermediate 1 week LIFO, parsing, evaluation
4 Queue Systems Intermediate 1 week FIFO scheduling, fairness, throughput
5 Hash Table Advanced 2 weeks O(1) average lookup, hashing trade-offs
6 Binary Search Tree & Balancing Advanced 2 weeks Ordered search, rotations, invariants
7 Heap & Priority Queue Intermediate 1 week Partial ordering, top-k problems
8 Graph Algorithms Visualizer Advanced 2 weeks Graph modeling, traversal, shortest paths
9 Mini Database Engine Expert 4-6 weeks Storage engines, indexes, query execution

Learning Paths

Phase 1: Foundations (3-4 weeks)

Phase 2: Abstract Data Types (2-3 weeks)

Phase 3: Fast Lookup (3-4 weeks)

Phase 4: Specialized Structures (3-4 weeks)

Phase 5: Capstone (4-6 weeks)

Prerequisites

  • Comfortable with C (or Rust/Zig) syntax and compilation
  • Basic understanding of pointers and memory allocation
  • Familiarity with command-line tooling and build systems (Make/CMake)
  • Willingness to read foundational CS texts for deeper theory