📘 Data Structures Learning Roadmap

1. Foundations

Before diving into data structures, build a strong foundation.

• Prerequisites
  • Learn a programming language (C, C++, Java, or Python recommended).
  • Understand basic programming concepts:
    • Variables, loops, conditionals
    • Functions & recursion
    • Arrays and strings
    • Memory concepts (stack vs heap)

📚 Resources:

• Introduction to Programming in Python or C Programming Absolute Beginner’s Guide
• Online coding platforms (LeetCode, HackerRank)

Files:

• [[01-Foundations.md]]

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2. Introduction to Data Structures

Start with the “why” before the “how.”

• What are data structures?
• Why do we need them?
• Trade-offs: time complexity vs space complexity
• Learn Big-O notation

📚 Resources:

• “Big-O Cheat Sheet” (visual guides available online)
• MIT OCW: Introduction to Algorithms (6.006)

Files:

• [[02-Introduction-to-Data-Structures.md]]

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3. Linear Data Structures

Learn structures where data elements are arranged sequentially.

• Arrays
  • Static vs dynamic arrays
  • Applications (storing lists, matrices)
• Linked Lists
  • Singly, doubly, circular linked lists
  • Pros/cons compared to arrays
• Stacks
  • LIFO principle
  • Applications: undo operations, expression evaluation
• Queues
  • FIFO principle
  • Variants: circular queue, deque, priority queue

🛠 Practice: Implement from scratch in your language of choice.
💡 Applications: Browser history (stack), task scheduling (queue)

Files:

• [[03-Linear-Data-Structures.md]]
• [[Arrays.md]]
• [[Linked-Lists.md]]
• [[Stacks.md]]
• [[Queues.md]]

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4. Non-Linear Data Structures

Explore structures where data is hierarchical or networked.

• Trees
  • Binary Trees, Binary Search Trees (BSTs)
  • Traversals (inorder, preorder, postorder)
  • Heaps (min-heap, max-heap)
  • Balanced trees (AVL, Red-Black Trees)
• Graphs
  • Representations: adjacency list, adjacency matrix
  • Graph traversal: BFS, DFS
  • Applications: social networks, maps

🛠 Practice: Build a family tree, shortest path finder
💡 Applications: Routing algorithms, compiler design

Files:

• [[04-Non-Linear-Data-Structures.md]]
• [[Trees.md]]
• [[Graphs.md]]

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5. Advanced Data Structures

Once you are comfortable, move to more specialized structures.

• Hash Tables (hashing, collisions, open addressing)
• Tries (prefix trees)
• Disjoint Set / Union-Find
• Segment Trees, Fenwick Trees
• B-Trees and B+ Trees

💡 Applications:

• Hash Tables → dictionaries, caches
• Tries → autocomplete, spell-check
• Segment Trees → range queries in games/databases

Files:

• [[05-Advanced-Data-Structures.md]]
• [[Hash-Tables.md]]
• [[Tries.md]]
• [[Union-Find.md]]
• [[Segment-Trees.md]]
• [[Fenwick-Trees.md]]
• [[B-Trees-and-Bplus-Trees.md]]

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6. Algorithms with Data Structures

Combine your data structure knowledge with algorithms.

• Sorting algorithms: quicksort, mergesort, heapsort
• Searching algorithms: binary search
• Graph algorithms: Dijkstra’s, Kruskal’s, Prim’s
• Dynamic programming and data structures together

Files:

• [[06-Algorithms-with-Data-Structures.md]]
• [[Sorting-Algorithms.md]]
• [[Searching-Algorithms.md]]
• [[Graph-Algorithms.md]]
• [[Dynamic-Programming.md]]

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7. Practical Applications & Projects

Solidify your understanding by building projects.

• Implement a text editor with undo/redo (stacks)
• Build a simple database index (B-Trees)
• Social network graph analysis (graphs + BFS/DFS)
• Autocomplete search engine (tries)
• Memory allocator simulator (linked lists + heaps)

Files:

• [[07-Practical-Applications-and-Projects.md]]

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8. Interview & Competitive Programming Prep

If you want to prepare for coding interviews:

• Master common problems on arrays, strings, stacks, queues
• Practice coding challenges daily
• Focus on time complexity analysis

📚 Resources:

• Cracking the Coding Interview by Gayle Laakmann McDowell
• LeetCode Patterns guide

Files:

• [[08-Interview-and-Competitive-Programming-Prep.md]]

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9. Next Steps

Once you’re confident:

• Explore Parallel & Distributed Data Structures
• Study Data Structures in Machine Learning (tensors, computational graphs)
• Learn about persistent data structures and functional programming approaches

Files:

• [[09-Next-Steps.md]]

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✅ Tip: Always implement data structures yourself first before using built-in libraries. This gives deep intuition.